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ENGINEERING (1240 journals)                  1 2 3 4 5 6 7 | Last

Showing 1 - 200 of 1205 Journals sorted alphabetically
3 Biotech     Open Access   (Followers: 8)
3D Research     Hybrid Journal   (Followers: 19)
AAPG Bulletin     Hybrid Journal   (Followers: 8)
AASRI Procedia     Open Access   (Followers: 14)
Abstract and Applied Analysis     Open Access   (Followers: 3)
Aceh International Journal of Science and Technology     Open Access   (Followers: 2)
ACS Nano     Full-text available via subscription   (Followers: 265)
Acta Geotechnica     Hybrid Journal   (Followers: 7)
Acta Metallurgica Sinica (English Letters)     Hybrid Journal   (Followers: 6)
Acta Polytechnica : Journal of Advanced Engineering     Open Access   (Followers: 3)
Acta Scientiarum. Technology     Open Access   (Followers: 3)
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Active and Passive Electronic Components     Open Access   (Followers: 7)
Adaptive Behavior     Hybrid Journal   (Followers: 11)
Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi     Open Access  
Adsorption     Hybrid Journal   (Followers: 4)
Advanced Engineering Forum     Full-text available via subscription   (Followers: 7)
Advanced Science     Open Access   (Followers: 5)
Advanced Science Focus     Free   (Followers: 5)
Advanced Science Letters     Full-text available via subscription   (Followers: 9)
Advanced Science, Engineering and Medicine     Partially Free   (Followers: 7)
Advanced Synthesis & Catalysis     Hybrid Journal   (Followers: 18)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Complex Systems     Hybrid Journal   (Followers: 6)
Advances in Engineering Software     Hybrid Journal   (Followers: 27)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 15)
Advances in Fuzzy Systems     Open Access   (Followers: 5)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 12)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 21)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 23)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 9)
Advances in Natural Sciences: Nanoscience and Nanotechnology     Open Access   (Followers: 30)
Advances in Operations Research     Open Access   (Followers: 12)
Advances in OptoElectronics     Open Access   (Followers: 5)
Advances in Physics Theories and Applications     Open Access   (Followers: 13)
Advances in Polymer Science     Hybrid Journal   (Followers: 43)
Advances in Porous Media     Full-text available via subscription   (Followers: 5)
Advances in Remote Sensing     Open Access   (Followers: 43)
Advances in Science and Research (ASR)     Open Access   (Followers: 4)
Aerobiologia     Hybrid Journal   (Followers: 2)
African Journal of Science, Technology, Innovation and Development     Hybrid Journal   (Followers: 6)
AIChE Journal     Hybrid Journal   (Followers: 35)
Ain Shams Engineering Journal     Open Access   (Followers: 5)
Akademik Platform Mühendislik ve Fen Bilimleri Dergisi     Open Access   (Followers: 1)
Alexandria Engineering Journal     Open Access   (Followers: 1)
AMB Express     Open Access   (Followers: 1)
American Journal of Applied Sciences     Open Access   (Followers: 26)
American Journal of Engineering and Applied Sciences     Open Access   (Followers: 10)
American Journal of Engineering Education     Open Access   (Followers: 9)
American Journal of Environmental Engineering     Open Access   (Followers: 16)
American Journal of Industrial and Business Management     Open Access   (Followers: 23)
Analele Universitatii Ovidius Constanta - Seria Chimie     Open Access  
Annals of Combinatorics     Hybrid Journal   (Followers: 3)
Annals of Pure and Applied Logic     Open Access   (Followers: 2)
Annals of Regional Science     Hybrid Journal   (Followers: 7)
Annals of Science     Hybrid Journal   (Followers: 7)
Antarctic Science     Hybrid Journal   (Followers: 1)
Applicable Algebra in Engineering, Communication and Computing     Hybrid Journal   (Followers: 2)
Applicable Analysis: An International Journal     Hybrid Journal   (Followers: 1)
Applied Catalysis A: General     Hybrid Journal   (Followers: 6)
Applied Catalysis B: Environmental     Hybrid Journal   (Followers: 18)
Applied Clay Science     Hybrid Journal   (Followers: 5)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 11)
Applied Magnetic Resonance     Hybrid Journal   (Followers: 4)
Applied Nanoscience     Open Access   (Followers: 8)
Applied Network Science     Open Access   (Followers: 3)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Physics Research     Open Access   (Followers: 4)
Applied Sciences     Open Access   (Followers: 3)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4)
Arabian Journal for Science and Engineering     Hybrid Journal   (Followers: 5)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 5)
Archives of Foundry Engineering     Open Access  
Archives of Thermodynamics     Open Access   (Followers: 8)
Arkiv för Matematik     Hybrid Journal   (Followers: 1)
ASEE Prism     Full-text available via subscription   (Followers: 3)
Asia-Pacific Journal of Science and Technology     Open Access  
Asian Engineering Review     Open Access  
Asian Journal of Applied Science and Engineering     Open Access   (Followers: 1)
Asian Journal of Applied Sciences     Open Access   (Followers: 2)
Asian Journal of Biotechnology     Open Access   (Followers: 8)
Asian Journal of Control     Hybrid Journal  
Asian Journal of Current Engineering & Maths     Open Access  
Asian Journal of Technology Innovation     Hybrid Journal   (Followers: 8)
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
ATZagenda     Hybrid Journal  
ATZextra worldwide     Hybrid Journal  
Australasian Physical & Engineering Sciences in Medicine     Hybrid Journal   (Followers: 1)
Australian Journal of Multi-Disciplinary Engineering     Full-text available via subscription   (Followers: 2)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 9)
Avances en Ciencias e Ingeniería     Open Access  
Balkan Region Conference on Engineering and Business Education     Open Access   (Followers: 1)
Bangladesh Journal of Scientific and Industrial Research     Open Access  
Basin Research     Hybrid Journal   (Followers: 5)
Batteries     Open Access   (Followers: 6)
Bautechnik     Hybrid Journal   (Followers: 1)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 26)
Beni-Suef University Journal of Basic and Applied Sciences     Open Access   (Followers: 4)
BER : Manufacturing Survey : Full Survey     Full-text available via subscription   (Followers: 1)
BER : Motor Trade Survey     Full-text available via subscription  
BER : Retail Sector Survey     Full-text available via subscription   (Followers: 1)
BER : Retail Survey : Full Survey     Full-text available via subscription   (Followers: 1)
BER : Survey of Business Conditions in Manufacturing : An Executive Summary     Full-text available via subscription   (Followers: 2)
BER : Survey of Business Conditions in Retail : An Executive Summary     Full-text available via subscription   (Followers: 3)
Bhakti Persada : Jurnal Aplikasi IPTEKS     Open Access  
Bharatiya Vaigyanik evam Audyogik Anusandhan Patrika (BVAAP)     Open Access   (Followers: 1)
Biofuels Engineering     Open Access   (Followers: 1)
Biointerphases     Open Access   (Followers: 1)
Biomaterials Science     Full-text available via subscription   (Followers: 10)
Biomedical Engineering     Hybrid Journal   (Followers: 15)
Biomedical Engineering and Computational Biology     Open Access   (Followers: 13)
Biomedical Engineering Letters     Hybrid Journal   (Followers: 5)
Biomedical Engineering, IEEE Reviews in     Full-text available via subscription   (Followers: 19)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 35)
Biomedical Engineering: Applications, Basis and Communications     Hybrid Journal   (Followers: 5)
Biomedical Microdevices     Hybrid Journal   (Followers: 9)
Biomedical Science and Engineering     Open Access   (Followers: 4)
Biomedizinische Technik - Biomedical Engineering     Hybrid Journal  
Biomicrofluidics     Open Access   (Followers: 4)
BioNanoMaterials     Hybrid Journal   (Followers: 2)
Biotechnology Progress     Hybrid Journal   (Followers: 39)
Boletin Cientifico Tecnico INIMET     Open Access  
Botswana Journal of Technology     Full-text available via subscription   (Followers: 1)
Boundary Value Problems     Open Access   (Followers: 1)
Brazilian Journal of Science and Technology     Open Access   (Followers: 2)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 10)
Bulletin of Canadian Petroleum Geology     Full-text available via subscription   (Followers: 15)
Bulletin of Engineering Geology and the Environment     Hybrid Journal   (Followers: 14)
Bulletin of the Crimean Astrophysical Observatory     Hybrid Journal  
Cahiers, Droit, Sciences et Technologies     Open Access  
Calphad     Hybrid Journal  
Canadian Geotechnical Journal     Hybrid Journal   (Followers: 31)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 42)
Case Studies in Engineering Failure Analysis     Open Access   (Followers: 6)
Case Studies in Thermal Engineering     Open Access   (Followers: 5)
Catalysis Communications     Hybrid Journal   (Followers: 6)
Catalysis Letters     Hybrid Journal   (Followers: 2)
Catalysis Reviews: Science and Engineering     Hybrid Journal   (Followers: 7)
Catalysis Science and Technology     Free   (Followers: 8)
Catalysis Surveys from Asia     Hybrid Journal   (Followers: 3)
Catalysis Today     Hybrid Journal   (Followers: 7)
CEAS Space Journal     Hybrid Journal   (Followers: 2)
Cellular and Molecular Neurobiology     Hybrid Journal   (Followers: 3)
Central European Journal of Engineering     Hybrid Journal  
CFD Letters     Open Access   (Followers: 6)
Chaos : An Interdisciplinary Journal of Nonlinear Science     Hybrid Journal   (Followers: 2)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
Chinese Journal of Engineering     Open Access   (Followers: 2)
Chinese Science Bulletin     Open Access   (Followers: 1)
Ciencia e Ingenieria Neogranadina     Open Access  
Ciencia en su PC     Open Access   (Followers: 1)
Ciencias Holguin     Open Access   (Followers: 2)
CienciaUAT     Open Access   (Followers: 1)
Cientifica     Open Access  
CIRP Annals - Manufacturing Technology     Full-text available via subscription   (Followers: 11)
CIRP Journal of Manufacturing Science and Technology     Full-text available via subscription   (Followers: 13)
City, Culture and Society     Hybrid Journal   (Followers: 20)
Clay Minerals     Full-text available via subscription   (Followers: 10)
Clean Air Journal     Full-text available via subscription   (Followers: 1)
Coal Science and Technology     Full-text available via subscription   (Followers: 3)
Coastal Engineering     Hybrid Journal   (Followers: 11)
Coastal Engineering Journal     Hybrid Journal   (Followers: 5)
Coatings     Open Access   (Followers: 4)
Cogent Engineering     Open Access   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 4)
Color Research & Application     Hybrid Journal   (Followers: 2)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 14)
Combustion, Explosion, and Shock Waves     Hybrid Journal   (Followers: 13)
Communications Engineer     Hybrid Journal   (Followers: 1)
Communications in Numerical Methods in Engineering     Hybrid Journal   (Followers: 2)
Components, Packaging and Manufacturing Technology, IEEE Transactions on     Hybrid Journal   (Followers: 28)
Composite Interfaces     Hybrid Journal   (Followers: 7)
Composite Structures     Hybrid Journal   (Followers: 271)
Composites Part A : Applied Science and Manufacturing     Hybrid Journal   (Followers: 207)
Composites Part B : Engineering     Hybrid Journal   (Followers: 246)
Composites Science and Technology     Hybrid Journal   (Followers: 182)
Comptes Rendus Mécanique     Full-text available via subscription   (Followers: 2)
Computation     Open Access  
Computational Geosciences     Hybrid Journal   (Followers: 15)
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 8)
Computer Science and Engineering     Open Access   (Followers: 19)
Computers & Geosciences     Hybrid Journal   (Followers: 30)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 8)
Computers and Electronics in Agriculture     Hybrid Journal   (Followers: 5)
Computers and Geotechnics     Hybrid Journal   (Followers: 11)
Computing and Visualization in Science     Hybrid Journal   (Followers: 6)
Computing in Science & Engineering     Full-text available via subscription   (Followers: 33)
Conciencia Tecnologica     Open Access  
Concurrent Engineering     Hybrid Journal   (Followers: 3)
Continuum Mechanics and Thermodynamics     Hybrid Journal   (Followers: 8)
Control and Dynamic Systems     Full-text available via subscription   (Followers: 9)
Control Engineering Practice     Hybrid Journal   (Followers: 43)
Control Theory and Informatics     Open Access   (Followers: 8)
Corrosion Science     Hybrid Journal   (Followers: 25)

        1 2 3 4 5 6 7 | Last

Journal Cover Computers and Electronics in Agriculture
  [SJR: 0.823]   [H-I: 73]   [5 followers]  Follow
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0168-1699
   Published by Elsevier Homepage  [3176 journals]
  • A compiled project and open-source code to generate web-based forest
           modelling simulators
    • Authors: Esteban Gómez-García; João C. Azevedo; Fernando Pérez-Rodríguez
      Pages: 1 - 5
      Abstract: Publication date: April 2018
      Source:Computers and Electronics in Agriculture, Volume 147
      Author(s): Esteban Gómez-García, João C. Azevedo, Fernando Pérez-Rodríguez
      Sustainable forest management requires decision support systems to evaluate possible scenarios and anticipate the consequences of decisions. Forest modellers typically develop complex systems of equations to predict the behaviour of forests which makes the use of forest models difficult for end-users in general, affecting transfer of knowledge and technology. To overcome these difficulties and facilitate their practical use, models can be integrated into software to generate user-friendly forest simulators. In this paper we introduce and describe ForestMTIS, a cloud computing compiled and editable open-source project to generate forest simulators which was developed for statistical, non-spatial, deterministic, disaggregated, single species even-aged stand growth and yield models. We demonstrate the use of ForestMTIS based on the development of FlorNExT®, its first practical application, based on a collaborative approach to make growth and yield modelling and sustainable forest management available to a large community of users in the Northeast of Portugal.

      PubDate: 2018-02-26T10:42:16Z
      DOI: 10.1016/j.compag.2018.02.010
      Issue No: Vol. 147 (2018)
  • Hierarchical pattern recognition in milking parameters predicts mastitis
    • Authors: Esmaeil Ebrahimie; Faezeh Ebrahimi; Mansour Ebrahimi; Sarah Tomlinson; Kiro R. Petrovski
      Pages: 6 - 11
      Abstract: Publication date: April 2018
      Source:Computers and Electronics in Agriculture, Volume 147
      Author(s): Esmaeil Ebrahimie, Faezeh Ebrahimi, Mansour Ebrahimi, Sarah Tomlinson, Kiro R. Petrovski
      The aim of this study was to develop a predictive model for mastitis incidence, independent from Somatic Cell Count (SCC), to provide an alternative, simple, and cost-effective approach for mastitis risk management based on available milking parameters. The test-day Somatic Cell Count (SCC) is the most common indicator for Sub-Clinical Mastitis (SCM) surveillance in dairy industries worldwide. However, SCC is highly variable between days, raising major concerns for its reliability. This caveat highlights the need for longitudinal/frequent monitoring of SCC and/or developing alternative approaches for SCM surveillance. A considerable proportion of available milking data such as Milk Volume, Protein, Lactose, Electrical Conductivity (EC), Milking Time, and Peak Flow provide the possibility of pattern recognition and model discovery towards mastitis occurrence. Developing a predictive model involves: (1) finding the threshold (cutoff) of different predictive milking parameters and (2) finding the best combination of features that lead to mastitis and their hierarchical pattern/order. Here, in a large-scale study on 346,248 milking records, for the first time, we evaluated four different decision tree algorithms (Decision Tree, Stump Decision Tree, Parallel Decision Tree and Random Forest Decision Tree) with four different criteria (Accuracy, Info Gain, Gini Index and Gain Ratio) run on 11 datasets (original dataset and 10 created datasets by attribute weighting selection algorithms). Therefore, 572 models were evaluated and compared by 10-fold cross validation. The performance of each decision tree in drawing an inverted tree; with the most important feature at the root and less important variables as the leaf; was calculated by 10-fold cross validation. Random Forest Decision Tree with Gini Index criterion was the best model for predicting mastitis from milking parameters with a high accuracy of 90%. Decision Tree models identified a strong pattern for SCM in milking data where all (100%) of cows with low levels of lactose (Lactose ≤ 4.5 g/L) and low milk volume (Volume ≤ 21.7 L) had mastitis. In addition, a significant pattern was found for identifying healthy cows by high levels of lactose (Lactose ≥ 4.5 g/L) and low levels of EC (EC ≤ 5.2). This study doccuments that milking parameters mined by the Decision Tree Random Forest model can be utilised to accurately predict SCM. The findings can be employed to increase the reliability of test-day SCC or as SCC-independent and cost-effective predictors of SCM.

      PubDate: 2018-02-26T10:42:16Z
      DOI: 10.1016/j.compag.2018.02.003
      Issue No: Vol. 147 (2018)
  • A refined method for rapidly determining the relationship between canopy
           NDVI and the pasture evapotranspiration coefficient
    • Authors: Muhammad Shahinur Alam; David W. Lamb; Muhammad Moshiur Rahman
      Pages: 12 - 17
      Abstract: Publication date: April 2018
      Source:Computers and Electronics in Agriculture, Volume 147
      Author(s): Muhammad Shahinur Alam, David W. Lamb, Muhammad Moshiur Rahman
      The estimation of actual crop evapotranspiration (ETc) from any given land cover or crop type is important for irrigation water management and agricultural water consumption analysis. The main parameter used for such estimations is the crop coefficient (Kc). Spectral reflectance indices, such as the normalized difference vegetation index (NDVI) and the crop coefficient of a specific crop or pasture canopy are important indicators of ‘vigour’, namely the photosynthetic activity and rate of biomass accumulation. Measuring both parameters simultaneously, with a view to understanding how they interact, or for creating optical, surrogate indicators of Kc is very difficult because Kc itself is difficult to measure. In this study a portable enclosed chamber was used to measure ETc of a pasture and subsequently calculated Kc from reference evapotranspiration (ETo) data derived from a nearby automatic weather station (AWS). Calibration of the chamber confirms the suitability of the device to measure the amount of water vapour produced by local plant evapotranspiration, producing a calibration factor (C) close to 1 (C = 1.02, R2 = 0.87). The coincident NDVI values were measured using a portable active optical sensor. In a test involving a pasture (Festuca arundinacea var. Dovey) at two different stages of growth in two consecutive growing seasons, the NDVI and crop coefficients were observed to be strongly correlated (R2 = 0.80 and 0.77, respectively). A polynomial regression (R2 = 0.84) was found to be the best fit for the combined, multi-temporal Kc-NDVI relationship. The main advantages of this method include the suitability of operating at a smaller scale (<1 m2), in real time and repeatability.

      PubDate: 2018-02-26T10:42:16Z
      DOI: 10.1016/j.compag.2018.02.008
      Issue No: Vol. 147 (2018)
  • Improved underwater Helmholtz resonator with an open cavity for sample
           volume estimation
    • Authors: Stephen N. Njane; Yoshiaki Shinohara; Naoshi Kondo; Yuichi Ogawa; Tetsuhito Suzuki; Takahisa Nishizu
      Pages: 18 - 26
      Abstract: Publication date: April 2018
      Source:Computers and Electronics in Agriculture, Volume 147
      Author(s): Stephen N. Njane, Yoshiaki Shinohara, Naoshi Kondo, Yuichi Ogawa, Tetsuhito Suzuki, Takahisa Nishizu
      Helmholtz resonators with an opening on the side of the cavity have the potential to improve the limitations of closed cavity resonators as a practical means for determining sample volume underwater. When inserting samples into a closed cavity resonator, a door must be opened, the samples inserted and the door once more closed before commencing measurement. By incorporating an additional opening in the cavity, it could act as east access inlet for samples, as well as an entrance for live aquatic samples in the future. However, the characteristics of such an open cavity resonator are unknown, as is the mechanism for generating resonance in such a resonator. This acts as a barrier to the design and optimize these resonators. To overcome these issues, we first characterized the relationship between flow velocity and the region where Helmholtz resonance is generated. From this, we determined the optimal flow velocity to generate Helmholtz resonance in the cavity, taking into account the energy required to generate the signal, as well as avoiding unstable resonance generation regions. Our results demonstrate that the flow characteristics of an open cavity resonator are more stable with less jumps in frequency, than a closed cavity resonator. Moreover, for design optimization, we determined the cavity to sample volume ratio that gives the most sensitive resonance results. To do this, we used empirical equations to analyze the ratio of cavity volume to sample volume and demonstrated that a high cavity to sample volume ratio is preferable for a less dense sample (compared to water), while a lower cavity to sample volume ratio is desirable for a denser sample. In addition, this was experimentally validated using model samples that were either less dense or denser than water. The linear regression model for denser samples accounted for R-squared (R2) of 99.7% and 99.5% of the variance of the actual sample volume in the open and closed cavity resonators, respectively. However, for less dense samples, the model accounted for R-squared (R2) of 97.5% and 99.3% in the open cavity and closed cavity resonators, respectively. These results demonstrate that precise and non-invasive sample volume estimation is possible with an open cavity resonator, which can also be used as inlet in the future for aquatic sample insertion and volume estimation.

      PubDate: 2018-02-26T10:42:16Z
      DOI: 10.1016/j.compag.2018.02.015
      Issue No: Vol. 147 (2018)
  • Economical thermal-RGB imaging system for monitoring agricultural crops
    • Authors: Yasin Osroosh; Lav R. Khot; R. Troy Peters
      Pages: 34 - 43
      Abstract: Publication date: April 2018
      Source:Computers and Electronics in Agriculture, Volume 147
      Author(s): Yasin Osroosh, Lav R. Khot, R. Troy Peters
      In this study, a low-cost thermal-RGB imager was developed for use in agricultural crop monitoring applications. It is weatherproof, and has a geo-referencing capability along with a power management panel that allows unattended field deployment of the systems for crop monitoring over extended period of time. The imager is made up of single-board Linux-based computer integrated with RGB and thermal imaging modules. The imager can be configured as FTP server to allow data transfer to/from a client computer. Developed was also the custom image-processing algorithm which overlays, aligns thermal and RGB images, and mask for the thermal image to remove the soil background and shaded leaves. The algorithm outputs are the average temperature of sunlit leaves and canopy coverage. Prior to field validation, the performance of ten thermal modules and four fully assembled RGB-thermal imagers were assessed under laboratory conditions. In the spring of 2017, two imagers were mounted on a center pivot retrofitted with Medium Elevation Spray Application (MESA) and Low Elevation Spray Application (LESA) systems in a mint field near Toppenish, WA. The thermal modules showed an accuracy of  ±2.4 °C on average over a range of 0–50 °C of a blackbody target. Although accurate for larger canopies, the imperfect alignment of RGB and thermal images introduced significant errors in the calculations of sunlit leaves surface temperature in images with small canopy coverage. Further investigations revealed that the first peak of thermal image relative frequency histogram could be a more accurate representative of sunlit leaf surface temperature. Overall, the amended image-processing algorithm was able to successfully extract canopy surface temperature and percent canopy cover from a wide range of images captured during the crop growing season. The current design of imager allows for creating a network of imaging units in the field to obtain real-time surface temperature data from plant canopies. The system has the potential to be used for creating evapotranspiration and prescription maps in real-time, and irrigation scheduling.

      PubDate: 2018-02-26T10:42:16Z
      DOI: 10.1016/j.compag.2018.02.018
      Issue No: Vol. 147 (2018)
  • Automatic recognition of lactating sow postures from depth images by deep
           learning detector
    • Authors: Chan Zheng; Xunmu Zhu; Xiaofan Yang; Lina Wang; Shuqin Tu; Yueju Xue
      Pages: 51 - 63
      Abstract: Publication date: April 2018
      Source:Computers and Electronics in Agriculture, Volume 147
      Author(s): Chan Zheng, Xunmu Zhu, Xiaofan Yang, Lina Wang, Shuqin Tu, Yueju Xue
      The behaviors of livestock on farms are the primary representatives of animal welfare, health conditions and social interactions. Measuring behavior quantitatively in an automatic detection system on computer vision provides valuable behavioral information in an efficient and noninvasive way compared with manual observations or sensing techniques. Lactating sow postures, which are the crucial indicator of maternal evaluation, provide fundamental information for studying the maternal behavioral characteristics and regularities. We introduce a detector, Faster R-CNN, on deep learning framework to identify five postures (standing, sitting, sternal recumbency, ventral recumbency and lateral recumbency) and obtain sows accurate location in loose pens. The detection system consists of a Kinect v2 sensor that acquires depth images and a program that identifies sow postures and locates its bounding-boxes. The depth images of testing dataset of a sow were acquired at 5 frames per second in 24 h on the 15th day of postpartum, and training dataset were collected by some different sows. Since the identification performance from RGB images are impacted by the color and illumination variations caused by in-situ heat lamp and day-night cycle, we show that the automatic detection from depth images could avoid disturbances of the light. We find that the sow spent greater amount of time in recumbency (92.9% at night and 84.1% during the daytime) as compared with standing (0.4% at night and 10.5% during the daytime) and sitting (0.55% at night and 3.4% during the daytime). Statistically, the sow’s activity level is non-uniform in 24-h of a day, and her preferred lying positions is accordant with the pen’s floor design. The posture’s change frequency and average duration are presented. From the estimated general manner of posture change, we find that the sow takes more time in descending body than ascending, which could be a favorable indication of maternal ability with a slow-motion falling to avoid crushing piglets.

      PubDate: 2018-02-26T10:42:16Z
      DOI: 10.1016/j.compag.2018.01.023
      Issue No: Vol. 147 (2018)
  • Discrimination of tea varieties using FTIR spectroscopy and allied
           Gustafson-Kessel clustering
    • Authors: Xiaohong Wu; Jin Zhu; Bin Wu; Jun Sun; Chunxia Dai
      Pages: 64 - 69
      Abstract: Publication date: April 2018
      Source:Computers and Electronics in Agriculture, Volume 147
      Author(s): Xiaohong Wu, Jin Zhu, Bin Wu, Jun Sun, Chunxia Dai
      For the purpose of classifying tea varieties, allied Gustafson-Kessel (AGK) clustering was proposed to cluster the Fourier transform infrared reflectance (FTIR) spectra of tea samples. As a fuzzy clustering algorithm, AGK can not only produce fuzzy membership and typicality values but also cluster various shapes of data with the help of Gustafson-Kessel (GK) clustering. After FTIR spectra were collected by FTIR-7600 infrared spectrometer, they were preprocessed with multiple scatter correction (MSC). To reduce the dimensionality of FTIR spectra and make the classification of data easily, principal component analysis (PCA) and linear discriminant analysis (LDA) were used to process the FTIR spectra. After that, fuzzy c-means (FCM) clustering, possibilistic c-means (PCM) clustering, AGK clustering and allied fuzzy c-means (AFCM) clustering were performed to cluster data, respectively. The clustering accuracy of AGK achieved 93.9% which was the highest one than other fuzzy clustering algorithms. The results obtained in experiments showed that AGK coupled with FTIR spectroscopy could provide an effective discrimination model for classification of tea varieties successfully.

      PubDate: 2018-02-26T10:42:16Z
      DOI: 10.1016/j.compag.2018.02.014
      Issue No: Vol. 147 (2018)
  • Deep learning in agriculture: A survey
    • Authors: Andreas Kamilaris; Francesc X. Prenafeta-Boldú
      Pages: 70 - 90
      Abstract: Publication date: April 2018
      Source:Computers and Electronics in Agriculture, Volume 147
      Author(s): Andreas Kamilaris, Francesc X. Prenafeta-Boldú
      Deep learning constitutes a recent, modern technique for image processing and data analysis, with promising results and large potential. As deep learning has been successfully applied in various domains, it has recently entered also the domain of agriculture. In this paper, we perform a survey of 40 research efforts that employ deep learning techniques, applied to various agricultural and food production challenges. We examine the particular agricultural problems under study, the specific models and frameworks employed, the sources, nature and pre-processing of data used, and the overall performance achieved according to the metrics used at each work under study. Moreover, we study comparisons of deep learning with other existing popular techniques, in respect to differences in classification or regression performance. Our findings indicate that deep learning provides high accuracy, outperforming existing commonly used image processing techniques.

      PubDate: 2018-02-26T10:42:16Z
      DOI: 10.1016/j.compag.2018.02.016
      Issue No: Vol. 147 (2018)
  • Rectification methods for optimization of management zones
    • Authors: Nelson Miguel Betzek; Eduardo Godoy de Souza; Claudio Leones Bazzi; Kelyn Schenatto; Alan Gavioli
      Pages: 1 - 11
      Abstract: Publication date: March 2018
      Source:Computers and Electronics in Agriculture, Volume 146
      Author(s): Nelson Miguel Betzek, Eduardo Godoy de Souza, Claudio Leones Bazzi, Kelyn Schenatto, Alan Gavioli
      The use of management zones (MZs) is an approach to precision agriculture that considers spatially contiguous subregions of the field, within which effects on the crop due to differences in soil, topography, and other abiotic factors are expected to be nearly uniform. Delimiting regions within the fields with similar yield potential and yield-limiting factors can lead for field management optimization. Regardless of the method used to delimit these zones, patches or isolated pixels generally appear. To smooth the MZs and improve their contiguity, a computational rectification function was implemented, allowing the analysis of 8 (3 × 3 mask) or 24 (5 × 5 mask) neighboring pixels using the statistical median and mode, to evaluate whether each pixel in the map should be reassigned to a different MZ. After being interpolated and normalized, sample data from three experimental fields were used to create clusters through fuzzy c-means algorithm, generating maps with two, three, four, and five classes. Then, the rectification function was applied five times on each map, which eliminated isolated pixels and virtually all patches, smoothing the boundaries between classes. The smoothness index showed higher variation in the first rectification as well as with an increase in the number of classes. The best performance was obtained with the 5 × 5 mask regardless of the statistical method used (median or mode). Our results show that these techniques are an effective way to increase the contiguity and smoothness of MZs, thereby improving their effectiveness, and are suitable for application in precision agriculture.

      PubDate: 2018-02-26T10:42:16Z
      DOI: 10.1016/j.compag.2018.01.014
      Issue No: Vol. 146 (2018)
  • Pixel based bruise region extraction of apple using Vis-NIR hyperspectral
    • Authors: Wenkai Che; Laijun Sun; Qian Zhang; Wenyi Tan; Dandan Ye; Dan Zhang; Yangyang Liu
      Pages: 12 - 21
      Abstract: Publication date: March 2018
      Source:Computers and Electronics in Agriculture, Volume 146
      Author(s): Wenkai Che, Laijun Sun, Qian Zhang, Wenyi Tan, Dandan Ye, Dan Zhang, Yangyang Liu
      Bruises on apples will directly influence its preservation and marketing for they can cause the internal decomposition and flaws of the appearance of apples. Therefore, an effective pixel based bruise region extraction method was proposed in this study to obtain the complete bruise region. Hyperspectral images of 60 apples were obtained via the hyperspectral imaging (HSI) system at 0, 12 and 18 h after the damage experiment. Principal Component Analysis (PCA) was used to compression data size and eliminating redundant data of hyperspectral image cubes. After the selection of the region of interest (ROI) by certain rules, different pixel based apple bruise extraction models were built and compared. The result shows that Random Forest (RF) model have a high and stable classification accuracy, which turns out that RF algorithm is more suitable for classifying bruises on apples than others. The average accuracy of bruise extraction models reached 99.9%. Compared with the most used image processing method in recent literature for extracting bruises of apples, the bruising region predicted by RF model was more consistent with the true bruise region. Additionally, two characteristic wavebands around 675 nm and 960 nm related to the bruise region were singled out for reducing the dimensionality of data by analyzing the feature importance scores of the built RF model. The overall results indicated that the proposed method has a great potential to detect complete bruise region on apples based on hyperspectral imaging for improving the efficiency of apple grading and sorting.

      PubDate: 2018-02-26T10:42:16Z
      DOI: 10.1016/j.compag.2018.01.013
      Issue No: Vol. 146 (2018)
  • Validated multi-wavelength simulations of light transport in healthy onion
    • Authors: Nathan Tomer; Andrew McGlone; Rainer Künnemeyer
      Pages: 22 - 30
      Abstract: Publication date: March 2018
      Source:Computers and Electronics in Agriculture, Volume 146
      Author(s): Nathan Tomer, Andrew McGlone, Rainer Künnemeyer
      Multi-wavelength light transport simulations of individual brown onions have been validated in the wavelength range 710–850 nm by comparing the simulations with experimental results. The simulations used the Finite Element Method (FEM) implemented in the software code NIRFast in Matlab. NIRFast required tissue geometry, optical scattering and absorption properties as inputs. The scattering values were obtained from Inverse Adding Doubling (IAD) measurements of onion slices. The absorption properties of several components were investigated. They included water, chlorophyll and onion absorption values measured with the IAD method or using transmission measurements on onion juice. These inputs to NIRFast enabled simulations of the transmission through model onions with different light attenuation characteristics. Error between simulation and measurement was minimized using absorption values from onion juice. An imposed realistic water concentration constraint (∼88%) removed cross talk between scattering and absorption. This resulted in a set of optical properties, based on a small set of component absorbers and Mie scattering theory, which accurately modelled light transport in each onion. The input absorption values were then checked for completeness with inverse simulations at each wavelength independently. Results indicated no major component was missing from the absorption model. The resulting optical property estimates were used to predict a range of transmission measurements from a high speed conveyor system.

      PubDate: 2018-02-26T10:42:16Z
      DOI: 10.1016/j.compag.2018.01.018
      Issue No: Vol. 146 (2018)
  • Simulating hydrologic cycle and crop production in a subsurface drained
           and sub-irrigated field in Southern Quebec using RZWQM2
    • Authors: Qianjing Jiang; Zhiming Qi; Chandra A. Madramootoo; Ajay K. Singh
      Pages: 31 - 42
      Abstract: Publication date: March 2018
      Source:Computers and Electronics in Agriculture, Volume 146
      Author(s): Qianjing Jiang, Zhiming Qi, Chandra A. Madramootoo, Ajay K. Singh
      Agricultural system models are promising tools in evaluating the agro-environmental effects of water management practices. However, very few models have been tested using a comprehensive hydrologic data set. The present study’s objective was to evaluate the hydrologic component of RZWQM2 (Root Zone Water Quality Model) using a comprehensive hydrological dataset including subsurface tile drainage, subirrigation, soil water content, sap flow and crop growth data such as leaf area index, crop yield and crop growth stages. Drawing on 2008 and 2009 data from a farm site in Southern Quebec, the RZWQM2 model showed accurate simulation in soil water content, sap flow, growth stage, leaf area index, and crop yield. While mean values for growing season tile flow under both free drainage (FD) and controlled drainage with subirrigation (CD-SI) were reasonably accurate, winter tile flow was significantly overestimated, indicating RZWQM2’s reliability to be compromised by its imperfect winter drainage process. Accordingly, a Kalman filter technique was applied to enhance model reliability and reduce predictive uncertainties. A novel RZWQM2 model equipped with a Kalman filter algorithm adequately simulated, in both calibration and validation phases, the hydrology and corn growth which occurred under both FD and CD-SI systems at the selected field site. Simulation results suggest that RZWQM2 model can be used for water management under subsurface drained and irrigated field and the Kalman filter technique significantly improved the accuracy of RZWQM2 model in simulating winter drainage in cold areas.

      PubDate: 2018-02-26T10:42:16Z
      DOI: 10.1016/j.compag.2018.01.021
      Issue No: Vol. 146 (2018)
  • A methodology for fresh tomato maturity detection using computer vision
    • Authors: Peng Wan; Arash Toudeshki; Hequn Tan; Reza Ehsani
      Pages: 43 - 50
      Abstract: Publication date: March 2018
      Source:Computers and Electronics in Agriculture, Volume 146
      Author(s): Peng Wan, Arash Toudeshki, Hequn Tan, Reza Ehsani
      Recent advancements in computer vision have provided opportunities for new applications in agriculture. Accurate yield estimation of fruit and vegetable crops is very important for better harvesting and marketing planning and logistics. This paper proposes a method for detecting the maturity levels (green, orange, and red) of fresh market tomatoes (Roma and Pear varieties) by combining the feature color value with the backpropagation neural network (BPNN) classification technique. A maturity detection device based on computer vision technology was designed specifically to acquire the tomato images in the lab. The tomato images were processed and the targets of the tomatoes were obtained based on the image processing technology. After that, the maximum inscribed circle of the tomato’s surface was identified as the color feature extraction area. The color feature extraction area was divided into five concentric circles (sub-domains). The average hue values of each sub-region were extracted as the feature color values and used to describe the maturity level of the samples. After that, the five feature color values were imported to the BPNN as input values to detect the maturity of the tomato samples. Analysis of the results shows that the average accuracy for detecting the three maturity levels of tomato samples using this method is 99.31%; and the standard deviation is 1.2%.

      PubDate: 2018-02-26T10:42:16Z
      DOI: 10.1016/j.compag.2018.01.011
      Issue No: Vol. 146 (2018)
  • On-site ion monitoring system for precision hydroponic nutrient management
    • Authors: Woo-Jae Cho; Hak-Jin Kim; Dae-Hyun Jung; Dong-Wook Kim; Tae In Ahn; Jung-Eek Son
      Pages: 51 - 58
      Abstract: Publication date: March 2018
      Source:Computers and Electronics in Agriculture, Volume 146
      Author(s): Woo-Jae Cho, Hak-Jin Kim, Dae-Hyun Jung, Dong-Wook Kim, Tae In Ahn, Jung-Eek Son
      Hydroponic solutions used in greenhouses or plant factories are usually evaluated based on their electrical conductivity (EC) and pH. However, EC and pH cannot provide sufficient information about ion imbalances in hydroponic solutions, and this may result in wastage of nutrients or poor yields. This paper reports on the development of an on-site ion monitoring system based on ion-selective electrodes (ISEs) that can automatically calibrate sensors and measure the concentrations of individual ions (NO3 −, K+, and Ca2+) in hydroponic solutions. This enables farmers to effectively manage nutrients in reused solutions by rapidly identifying any imbalances that appear in the nutrient ratios. The measurement performance of the developed system was evaluated using hydroponic solutions prepared for growing paprika crops in greenhouses. An application test was conducted to investigate the feasibility of using the developed on-site ion monitoring system for the automated measurement of three macronutrients (NO3 −, K+, and Ca2+) in a real greenhouse. The results showed that the developed system was able to measure NO3 − concentrations, showing an almost 1:1 relationship with the results of a standard instrument, i.e., ion chromatography (slope of 0.99 and R2 of 0.99). Although the developed system overestimated and underestimated the K+ and Ca2+ concentrations with slopes of 1.17 and 0.75, respectively, the high coefficients of determination of 0.99 and 0.97 made it possible to use calibration factors to compensate for differences in estimation. In fact, relatively low RMSEs of < 20 mg L−1 over the range of 40–1200 mg L−1 were obtained from a comparison of the ISE method and standard analysis when tested in hydroponic samples taken on different days during the period of paprika growing. This indicates that ISEs could be applicable to measurements where there is a strong linear relationship between the ISE method and standard analysis. In the application tests, the system could monitor the temporal changes in ionic concentrations in hydroponic solutions effectively, showing sensitive responses to changes in the concentrations of the three ions with an acceptable level of performance.

      PubDate: 2018-02-26T10:42:16Z
      DOI: 10.1016/j.compag.2018.01.019
      Issue No: Vol. 146 (2018)
  • Developing a temperature measuring system model for agriculture dryer with
           consideration of fringing field effect in mathematical modeling
    • Authors: Amirhossein Akbari; Shahriar Kouravand
      Pages: 59 - 65
      Abstract: Publication date: March 2018
      Source:Computers and Electronics in Agriculture, Volume 146
      Author(s): Amirhossein Akbari, Shahriar Kouravand
      Thermometer accuracy at small rise in temperature leads to increasing the knowledge about products process especially in drying of plants, fruits and biological laboratories. This developed model a thermometer based on capacitance change resulting from deflection of a bimetal cantilever. The effect of fringing field is included in the model. The deflection of the theoretical model is simulated and results are shown to closely follow the previously published results. Finally, the complete theoretical model, including the effect of fringing field, is simulated using an example. The Taguchi method with analysis of S/N ratio is used to obtain the optimal design of the sensor. Using the optimum settings, the S/N ratio improved by 24.11 dB.

      PubDate: 2018-02-26T10:42:16Z
      DOI: 10.1016/j.compag.2018.01.025
      Issue No: Vol. 146 (2018)
  • A new soil quality index based on morpho-pedological indicators as a
           site-specific web service applied to olive groves in the Province of Jaen
           (South Spain)
    • Authors: J. Calero; V. Aranda; A. Montejo-Ráez; J.M. Martín-García
      Pages: 66 - 76
      Abstract: Publication date: March 2018
      Source:Computers and Electronics in Agriculture, Volume 146
      Author(s): J. Calero, V. Aranda, A. Montejo-Ráez, J.M. Martín-García
      Soil quality has become a fundamental concept in soil science and agriculture, but it can be difficult to apply its theoretical and experimental approaches to poorly surveyed zones where precision techniques are far from being applied. In this paper, we propose a new technique that enables little-used qualitative morpho-pedological data to be managed and integrated into a single Field Soil Quality Index (FSQI). Nonlinear Principal Component Analysis (NLPCA), a technique able to handle categorical data, is applied here to deal with morpho-pedological indicators. When categorical values are transformed, they can be properly analyzed and interpreted. This procedure requires less expert knowledge, so it can help soil quality assessments by non-experts. We applied the FSQI protocol to soils in the most important olive-growing area in the world, Jaen Province (Southern Spain), which has serious problems with soil degradation and erosion. First, a soil database for the study area was compiled, including 18 morphological attributes for 131 surface horizons belonging to eight Land Use Types. Secondly, the NLPCA provides optimal scalings and attribute weights that transform and integrate morphological indicators into a simple weighted additive index (FSQI). Thirdly, the scaling functions and weights found were applied to the same attributes of an evaluation set comparing two soil management types (conventional vs. organic) in olive groves. The FSQI means for the first (conventional) were significantly lower than in the organic groves (0.278 vs. 0.463, P < .05), which supports the validity of the index. A site-specific FSQI web service has been created to assist in decision-making in the study area, whose methodology can be generalized to other zones and crops.

      PubDate: 2018-02-26T10:42:16Z
      DOI: 10.1016/j.compag.2018.01.016
      Issue No: Vol. 146 (2018)
  • Early optical detection of infection with brown rust in winter wheat by
           chlorophyll fluorescence excitation spectra
    • Authors: Ylva Katharina Tischler; Eiko Thiessen; Eberhard Hartung
      Pages: 77 - 85
      Abstract: Publication date: March 2018
      Source:Computers and Electronics in Agriculture, Volume 146
      Author(s): Ylva Katharina Tischler, Eiko Thiessen, Eberhard Hartung
      Puccinia recondita f. sp. tritici, the causative agent of brown rust disease is the reason for major yield losses in winter wheat plants (Triticum aestivum L.). Early detection of fungal infections would allow a specific fungicide application. Aim of the current study was to develop an automated and computer based device that differentiate infected and healthy plants non-invasively in an early stage of infection. To achieve the target an optical sensor device has been designed (“MultiDetExc”), which excites chlorophyll fluorescence in discrete wavelengths and detects the induced emissions. Wheat plants were infected artificially with brown rust in a climate chamber experiment to survey the capability of the instrument. The chlorophyll fluorescence excitation spectra of whole wheat plants were measured on several days after infection. As reference methods, HPLC and qPCR analysis were included in the current study to measure the polyphenol content of the leaves and the level of infection. The recently developed sensor device is an efficient technique to differentiate the infected and not infected wheat plants as soon as four days after inoculation. The measured fluorescence quotients correlate high positive with the polyphenol contents and the relative amount of fungal DNA. An untreated healthy control was measured parallelly, in order to associate the increased synthesis of polyphenols to the fungal infection definitely.

      PubDate: 2018-02-26T10:42:16Z
      DOI: 10.1016/j.compag.2018.01.026
      Issue No: Vol. 146 (2018)
  • Comparison of two immersion probes coupled with visible/near infrared
           spectroscopy to assess the must infection at the grape receiving area
    • Authors: Valentina Giovenzana; Roberto Beghi; Alessio Tugnolo; Lucio Brancadoro; Riccardo Guidetti
      Pages: 86 - 92
      Abstract: Publication date: March 2018
      Source:Computers and Electronics in Agriculture, Volume 146
      Author(s): Valentina Giovenzana, Roberto Beghi, Alessio Tugnolo, Lucio Brancadoro, Riccardo Guidetti
      The aim of this work was to investigate the applicability of visible/near infrared (vis/NIR) spectroscopy comparing two immersion probes to evaluate the phytosanitary status of must at the grape receiving area to support wineries for an objective quantification of grape infection level. The experimentation was conducted on white and red varieties (Vitis vinifera L.), employing grapes naturally infected with the major wine grape diseases. A total of 159 musts were product and analysed by using a vis/NIR spectrophotometer coupled with two kind of immersion probes: a reflectance probe and a transmittance one. Classification analysis (Partial Least Squares – Discriminant Analysis, PLS-DA) was performed on musts spectra to test the performance of the spectrophotometer, combined with the two probes, to classify healthy and infected samples. Considering three different spectral range analysed (430–1000 nm; 1000–1650 nm; 430–1650 nm), the results obtained from PLS-DA models, in cross-validation, gave values of correctly classified samples (accuracy, %) between 52.5% and 90.4%, and ranged from 68.4% to 84.3% for reflectance and transmittance probes respectively. The optical system was tested in controlled laboratory conditions, simulating the desirable final use of device. A future real scale application could be envisaged inside the analysis tank at the grape receiving area, after setting the operative conditions to perform the measurements directly coupled with the traditional and common quality analysis (soluble solid content and acidity) performed on grape musts sampled at the grape consignment.
      Graphical abstract image

      PubDate: 2018-02-26T10:42:16Z
      DOI: 10.1016/j.compag.2018.01.017
      Issue No: Vol. 146 (2018)
  • Adaptive two time-scale receding horizon optimal control for greenhouse
           lettuce cultivation
    • Authors: Dan Xu; Shangfeng Du; Gerard van Willigenburg
      Pages: 93 - 103
      Abstract: Publication date: March 2018
      Source:Computers and Electronics in Agriculture, Volume 146
      Author(s): Dan Xu, Shangfeng Du, Gerard van Willigenburg
      A two time-scale, receding horizon, optimal controller for greenhouse lettuce cultivation is extended with on-line parameter estimation to handle ill-known or time-varying parameters of the greenhouse-crop model. By means of simulations, the possible improvement of performance and reduction of constraint violation, introduced by this extension, are investigated. Moreover, uncommon issues in the adaptive controller design due to the two time-scales are considered and handled in this paper. The estimated parameters are selected based on their uncertainty and performance sensitivity. Using a recently developed very efficient algorithm, the selected parameters are checked for identifiability first. Finally the possibility of real-time implementation of the adaptive two time-scale receding horizon optimal controller is investigated.

      PubDate: 2018-02-26T10:42:16Z
      DOI: 10.1016/j.compag.2018.02.001
      Issue No: Vol. 146 (2018)
  • Mechatronic terrestrial LiDAR for canopy porosity and crown surface
    • Authors: Sebastián Arriagada Pfeiffer; Javier Guevara; Fernando Auat Cheein; Ricardo Sanz
      Pages: 104 - 113
      Abstract: Publication date: March 2018
      Source:Computers and Electronics in Agriculture, Volume 146
      Author(s): Sebastián Arriagada Pfeiffer, Javier Guevara, Fernando Auat Cheein, Ricardo Sanz
      The geometric characterization of tree crops is a necessary task to obtain simple though valuable variables such as height and width of tree tops and more complex variables such as porosity and tree top surface, in which this paper focuses. To obtain these variables, a ground-based mechatronic LiDAR system and a multi-size voxels algorithm have been developed. We test the LIDAR system and our algorithms for two cases: a single tree case and a multi-tree case. In the latter, we apply our algorithms in a tree row from an avocado grove in Chile, showing that our system is portable, accurate and can offer the farmer a useful tool for crop monitoring. In addition, we compare our approach with others previously published, showing that our system is more efficient when estimating porosity and crown surface, and offers more capabilities for the decision making process in agricultural activities.

      PubDate: 2018-02-26T10:42:16Z
      DOI: 10.1016/j.compag.2018.01.022
      Issue No: Vol. 146 (2018)
  • Near infrared computer vision and neuro-fuzzy model-based feeding decision
           system for fish in aquaculture
    • Authors: Chao Zhou; Kai Lin; Daming Xu; Lan Chen; Qiang Guo; Chuanheng Sun; Xinting Yang
      Pages: 114 - 124
      Abstract: Publication date: March 2018
      Source:Computers and Electronics in Agriculture, Volume 146
      Author(s): Chao Zhou, Kai Lin, Daming Xu, Lan Chen, Qiang Guo, Chuanheng Sun, Xinting Yang
      In aquaculture, the feeding efficiency of fish is of great significance for improving production and reducing costs. In recent years, automatic adjustments of the feeding amount based on the needs of the fish have become a developing trend. The purpose of this study was to achieve automatic feeding decision making based on the appetite of fish. In this study, a feeding control method based on near infrared computer vision and neuro-fuzzy model was proposed. The specific objectives of this study were as follows: (1) to develop an algorithm to extract an index that can describe and quantify the feeding behavior of fish in near infrared images, (2) to design an algorithm to realize feeding decision (continue or stop) during the feeding process, and (3) to evaluate the performance of the method. The specific implementation process of this study was as follows: (1) the quantitative index of feeding behavior (flocking level and snatching strength) was extracted by Delaunay Triangulation and image texture; (2) the adaptive network-based fuzzy inference system (ANFIS) was established based on fuzzy control rules and used to achieve automatically on-demand feeding; and (3) the performance of the method was evaluated by the specific growth rate, weight gain rate, feed conversion rate and water quality parameters. The results indicated that the feeding decision accuracy of the ANFIS model was 98%. In addition, compared with the feeding table, although this method did not present significant differences in promoting fish growth, the feed conversion rate (FCR) can be reduced by 10.77% and water pollution can also be reduced. This system provides an important contribution to realizing the real-time control of fish feeding processes and feeding decision on demand, and it lays a theoretical foundation for developing fine feeding equipment and guiding practice.

      PubDate: 2018-02-26T10:42:16Z
      DOI: 10.1016/j.compag.2018.02.006
      Issue No: Vol. 146 (2018)
  • Automation and applications of the tolerance limit method in estimating
           meat withdrawal periods for veterinary drugs
    • Authors: O. Udiani; S. Mason; G. Smith; D. Mzyk; R. Gehring; L. Tell; J.E. Riviere; R.E. Baynes
      Pages: 125 - 135
      Abstract: Publication date: March 2018
      Source:Computers and Electronics in Agriculture, Volume 146
      Author(s): O. Udiani, S. Mason, G. Smith, D. Mzyk, R. Gehring, L. Tell, J.E. Riviere, R.E. Baynes
      A program was written in R to facilitate the implementation of the tolerance limit method (TLM) for establishing regulatory withdrawal times for limiting drug residues in meat, milk, and eggs. The developed computer source code can use pharmacokinetic and regulatory data to calculate the drug withdrawal period according to United States Food and Drug Administration (U.S. FDA) guidelines. The code called the “Withdrawal Time Calculator (WTC)” applied this TLM method to meat samples. The program was tested with the data provided by the U.S. FDA guidance and other published data collected from in vivo studies. Additional algorithm validation data were flunixin and sulfamethazine liver concentration data from peer-reviewed publications generated by our laboratory. This manuscript reports the withdrawal period results from testing the developed WTC code. Moreover, the source code for the WTC contains a data removal algorithm, constructed according to U.S. FDA data elimination recommendations if the user chooses. The power of the WTC is that it bypasses the use of multiple platforms typically required to perform the TLM, including standard commercial spreadsheet software (i.e., Microsoft Excel) and Statistical Analysis System (SAS) while providing speed and usability. This novel program provides a platform to calculate a withdrawal period recommendation for any drug in any class of animal for various regulatory body standards and could be very helpful in cases of extra-label drug use in food animals.

      PubDate: 2018-02-26T10:42:16Z
      DOI: 10.1016/j.compag.2018.02.005
      Issue No: Vol. 146 (2018)
  • Reducing postharvest losses of apples: Optimal transport routing (while
           minimizing total costs)
    • Authors: J. Springael; A. Paternoster; J. Braet
      Pages: 136 - 144
      Abstract: Publication date: March 2018
      Source:Computers and Electronics in Agriculture, Volume 146
      Author(s): J. Springael, A. Paternoster, J. Braet
      Fresh products may suffer considerable damage during postharvest transportation caused by vibrations and shocks (i.e. transient vibrations that damp out over time). The Belgian apple industry is yearly worth 125–140 M euro (EBITDA to apple growers) and experiences losses between 10 and 25% corresponding to 10–25 M euro. Apple losses can be attributed to fungal diseases that enter the apple through bruised or punctured tissue and contaminate the fruit. Vibrations occurring during transports are a major contributor to bruises or punctures on apples, and, as a consequence, need to be avoided. An effective method to reduce the apple loss rate is by minimizing the number and intensity of vibrations that occur during the transport route. In this paper, we suggest planning transport routes based on transportation costs as well as costs related to the loss rate of apples. As a consequence, the transport vehicle is able to avoid road segments with poorly maintained road segments or road segments that are more susceptible to induce higher vibration amplitudes. The results of transport simulations illustrate that the Belgian apple growers can gain industry profits of 250–1500 thousand euros. Both from an economical as well as an ecological perspective our findings are substantial and relevant. The methods used in this research can be adopted by other fruit varieties by transforming the input parameters.
      Graphical abstract image

      PubDate: 2018-02-26T10:42:16Z
      DOI: 10.1016/j.compag.2018.02.007
      Issue No: Vol. 146 (2018)
  • Nondestructive assessments of carotenoids content of broadleaved plant
           species using hyperspectral indices
    • Authors: Rei Sonobe; Quan Wang
      Pages: 18 - 26
      Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Rei Sonobe, Quan Wang
      Carotenoids play important roles regarding photoprotection as well as light harvesting during the process of photosynthesis, resulting in the opportunity of quantifying carotenoids content to evaluate the productivity of vegetation. The traditional approaches such as ultraviolet and visible (UV–vis) spectroscopy are destructive and hence do not allow to determine the temporal dynamics of carotenoids content over time. As a promising alternative, hyperspectral remote sensing provides a way to evaluate carotenoid content changes over time and at multiple scales. Furthermore, it is easier to expand such approaches for large scale monitoring. However, to identify a generally applicable hyperspectral index sensitive to carotenoids remains a big challenge. In this study, we have evaluated thirteen available hyperspectral indices to quantify carotenoids, based on four independent datasets including two field datasets from Japan and two publicly available datasets (LOPEX and ANGERS). We attempted to develop a new generally applicable hyperspectral index for broadleaved plant species using the original and first derivative reflected spectra of the four datasets. We found that dND (516,744), a normalized differences type index using reflectance derivatives at 516 and 744 nm ( ( D 516 - D 744 ) / ( D 516 + D 744 ) ), had the highest robustness among all datasets and also was the best index when all data were combined (R2 = 0.475, WAIC = 2430.1, and RPD = 1.45 for all datasets), suggesting its potential for general applications. Further extensive evaluations of the proposed index in other types of plants is required to test whether it can also be applied in other than broadleaved species.

      PubDate: 2018-02-03T03:23:58Z
      DOI: 10.1016/j.compag.2017.12.022
      Issue No: Vol. 145 (2018)
  • Visible-near infrared spectrum-based classification of apple chilling
           injury on cloud computing platform
    • Authors: Ji'An Xia; YuWang Yang; HongXin Cao; Chen Han; DaoKuo Ge; WenYu Zhang
      Pages: 27 - 34
      Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Ji'An Xia, YuWang Yang, HongXin Cao, Chen Han, DaoKuo Ge, WenYu Zhang
      This paper evaluates the feasibility of applying cloud computing technology for spectrum-based classification of apple chilling injury. The reflectance spectra of Fuji apples with four different levels of chilling injury (none, slight, medium, and severe) were collected. During data processing, the spectra at 400–1000 nm were selected, and first- and second-order-derivative spectral data sets were obtained through integral transformations. Five optimal wavebands were chosen as inputs for the classification models. A cloud computing framework based on Spark and the MLlib machine learning library was used to realize multivariate classification models based on an artificial neural network (ANN) and support vector machine (SVM). The ANN and SVM classification models were used for multivariate classification and analysis of the spectral data sets (raw, first derivative, second derivative) and corresponding optimal wavebands. Of the total data samples, 70% were used for training, while the remaining 30% were used for prediction. The experimental results showed that, by using the cloud computing platform, we could establish an efficient spectrum classification model of apple chilling injury; the ANN model had slightly higher accuracy than the SVM model (not including the second-derivative spectra), but the SVM model was more efficient. Moreover, the classification accuracy using full-waveband spectral data sets was higher than that of data sets using five optimal wavebands. Furthermore, the Spark framework and MLlib were used to implement binary classification models (decision tree and random forest), and these were compared with the multivariate classification model; the binary classification method had better performance in near-infrared spectrum-based classification of apple chilling injury. Finally, we extended the existing spectrum data set to verify the efficiency of the cloud computing platform and desktop PC for handling larger data sets. The results showed that the efficiency of the cloud computing platform was significantly improved by increasing the spectral data set capacity or number of working nodes. Owing to processor and memory limitations, the classification algorithm and model of abundant spectral data sets cannot complete all of the tasks on a desktop PC.

      PubDate: 2018-02-03T03:23:58Z
      DOI: 10.1016/j.compag.2017.12.012
      Issue No: Vol. 145 (2018)
  • Automatic classification of plant electrophysiological responses to
           environmental stimuli using machine learning and interval arithmetic
    • Authors: Danillo Roberto Pereira; João Paulo Papa; Gustavo Francisco Rosalin Saraiva; Gustavo Maia Souza
      Pages: 35 - 42
      Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Danillo Roberto Pereira, João Paulo Papa, Gustavo Francisco Rosalin Saraiva, Gustavo Maia Souza
      In plants, there are different types of electrical signals involving changes in membrane potentials that could encode electrical information related to physiological states when plants are stimulated by different environmental conditions. A previous study analyzing traits of the dynamics of whole plant low-voltage electrical showed, for instance, that some specific frequencies that can be observed on plants growing under undisturbed conditions disappear after stress-like environments, such as cold, low light and osmotic stimuli. In this paper, we propose to test different methods of automatic classification in order to identify when different environmental cues cause specific changes in the electrical signals of plants. In order to verify such hypothesis, we used machine learning algorithms (Artificial Neural Networks, Convolutional Neural Network, Optimum-Path Forest, k-Nearest Neighbors and Support Vector Machine) together Interval Arithmetic. The results indicated that Interval Arithmetic and supervised classifiers are more suitable than deep learning techniques, showing promising results towards such research area.

      PubDate: 2018-02-03T03:23:58Z
      DOI: 10.1016/j.compag.2017.12.024
      Issue No: Vol. 145 (2018)
  • Development and evaluation of key ambient factors online monitoring system
           in live Urechis unicinctus transportation strategies
    • Authors: Yongjun Zhang; Xiaoshuan Zhang; Mai Thi Tuyet Nga; Liufeng; Hairui Yu
      Pages: 43 - 52
      Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Yongjun Zhang, Xiaoshuan Zhang, Mai Thi Tuyet Nga, Liufeng, Hairui Yu
      This paper puts forward the reasonable choice of live transportation strategy to guarantee survival rate and quality of Urechis unicinctus by designing its packaging mode and carrying out real-time monitoring technologies. Ambient data sensing devices are designed and deployed in water and waterless transportation facilities by which the simulation of real live transportation is tested. During the delivery process of live Urechis unicinctus, the key ambient factors – temperature, dissolved oxygen/oxygen, carbon dioxide, PH, salinity–are dynamically sampling by on-line electronic monitoring equipment. Urechis unicinctus transport ambient data and their correlations are statistically calculated and analyzed by transportation monitoring and analysis system for the two transportation strategies. Through five control groups test for water and waterless shipment, it is found that waterless transportation is more suitable for over 30 h live transportation by studying transportation facilities management, surface characteristics and survival rate, which can provide a sound statistical basis of reasonable transportation mode to guarantee consumers to eat healthier and cheaper seafood with more convenient and economical way.

      PubDate: 2018-02-03T03:23:58Z
      DOI: 10.1016/j.compag.2017.12.017
      Issue No: Vol. 145 (2018)
  • Development and implementation of a fish counter by using an embedded
    • Authors: J.M. Hernández-Ontiveros; E. Inzunza-González; E.E. García-Guerrero; O.R. López-Bonilla; S.O. Infante-Prieto; J.R. Cárdenas-Valdez; E. Tlelo-Cuautle
      Pages: 53 - 62
      Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): J.M. Hernández-Ontiveros, E. Inzunza-González, E.E. García-Guerrero, O.R. López-Bonilla, S.O. Infante-Prieto, J.R. Cárdenas-Valdez, E. Tlelo-Cuautle
      The development and implementation of an instrument for the automatic counting of ornamental fish by using an embedded system, is introduced herein. The proposed instrument is tested with two marine species, the Guppies (Poecilia Reticulata) and Mollies (Poecilia Sphenops), under conditions of controlled lighting and specimens whose sizes vary from 0.5 to 2.3 cm. The counting is done by digital image processing obtaining an average accuracy up to 96.64% using different species of fishes and different sizes. The main contributions are the theoretical and experimental study to determine the aquarium background color and the algorithm of the proposed method implemented in a low cost and high performance embedded system, specifically in a Raspberry Pi 2 executing the free GNU Octave Scientific Programming Language, thus, allowing the counting instrument to be reliable, portable and easily migratory to different operating systems. The obtained results demonstrate that the proposed method is competitive with state-of-the-art ones.

      PubDate: 2018-02-03T03:23:58Z
      DOI: 10.1016/j.compag.2017.12.023
      Issue No: Vol. 145 (2018)
  • Simulating peanut (Arachis hypogaea L.) growth and yield with the use of
           the Simple Simulation Model (SSM)
    • Authors: Seyyed Ali Noorhosseini; Afshin Soltani; Hossein Ajamnoroozi
      Pages: 63 - 75
      Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Seyyed Ali Noorhosseini, Afshin Soltani, Hossein Ajamnoroozi
      The use of crop simulation models for interpreting experiments and analyzing production systems in different management and environmental conditions is common in the literature. In this study, parameterization and evaluation of the Simple Simulation Model (SSM) for the prediction of peanut (Arachis hypogaea L.) growth and yield was conducted for the first time. Data from different field experiments from Astaneh Ashrafieh of northern Iran were used for coefficient estimation and model evaluation for the Virginia-type peanut variety North Carolina 2 (cv. NC2). After estimation of genetic parameters, the model was tested using independent data. The SSM simulated peanut growth and yield with reasonable accuracy, using data of more than 10 field experiments from different environmental conditions (11 experiments in the parameterization stage and 15 experiments in the evaluation stage). Based on data of independent experiments that were not used for parameterization, the model predicted an acceptable percentage of the observed results concerning days to harvest maturity (r = 0.46, CV = 5%), accumulated dry matter (r = 0.66, CV = 15%), grain yield (r = 0.55, CV = 21%), and pod yield (r = 0.45, CV = 18%). Local sensitivity analysis with 23 parameters indicated that two parameters related to leaf development and a parameter related to yield formation were the most sensitive cultivar-specific parameters; thus, estimation of the parameters need to be done with care for new cultivars. The SSM provided an adequate level of peanut growth simulation and based both on its transparency and easiness-to-use can be used as a valid tool for simulating growth of peanut Virginia-type varieties.

      PubDate: 2018-02-03T03:23:58Z
      DOI: 10.1016/j.compag.2017.12.020
      Issue No: Vol. 145 (2018)
  • Predicting the ripening of papaya fruit with digital imaging and random
    • Authors: Luiz Fernando Santos Pereira; Sylvio Barbon; Nektarios A. Valous; Douglas Fernandes Barbin
      Pages: 76 - 82
      Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Luiz Fernando Santos Pereira, Sylvio Barbon, Nektarios A. Valous, Douglas Fernandes Barbin
      Papaya grading is performed manually which may lead to misclassifications, resulting in fruit boxes with different maturity stages. The objective is to predict the ripening of the papaya fruit using digital imaging and random forests. A series of physical/chemical analyses are carried out and true maturity stage is derived from pulp firmness measurements. Imaging and image analysis provides hand-crafted color features computed from the peel and random decision forests are implemented to predict ripening stage. More specifically, a total of 114 samples from 57 fruits are used for the experiments, and classified into three stages of maturity. After image acquisition and analysis, twenty-one hand-crafted color features (comprising seven groups) that have low computational cost are extracted and evaluated. Random forests with two datasets (cross-validation and prediction set) are employed for the experiments. Concerning all image features, 94.3% classification performance is obtained over the cross-validation set. The prediction set obtained 94.7% misclassifying only a single sample. For the group comparisons, the normalized mean of the RGB (red, green, blue) color space achieved better performance (78.1%). Essentially, the technique can mature into an industrial application with the right integration framework.

      PubDate: 2018-02-03T03:23:58Z
      DOI: 10.1016/j.compag.2017.12.029
      Issue No: Vol. 145 (2018)
  • A pattern recognition approach for detecting and classifying jaw movements
           in grazing cattle
    • Authors: José O. Chelotti; Sebastián R. Vanrell; Julio R. Galli; Leonardo L. Giovanini; H. Leonardo Rufiner
      Pages: 83 - 91
      Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): José O. Chelotti, Sebastián R. Vanrell, Julio R. Galli, Leonardo L. Giovanini, H. Leonardo Rufiner
      Precision livestock farming is a multidisciplinary science that aims to manage individual animals by continuous real-time monitoring their health and welfare. Estimation of forage intake and monitoring the feeding behavior are key activities to evaluate the health and welfare state of animals. Acoustic monitoring is a practical way of performing these tasks, however it is a difficult task because masticatory events (bite, chew and chew-bite) must be detected and classified in real-time from signals acquired in noisy environments. Acoustic-based algorithms have shown promising results, however they were limited by the effects of noises, the simplicity of classification rules, or the computational cost. In this work, a new algorithm called Chew-Bite Intelligent Algorithm (CBIA) is proposed using concepts and tools derived from pattern recognition and machine learning areas. It includes (i) a signal conditioning stage to attenuate the effects of noises and trends, (ii) a pre-processing stage to reduce the overall computational cost, (iii) an improved set of features to characterize jaw-movements, and (iv) a machine learning model to improve the discrimination capabilities of the algorithm. Three signal conditioning techniques and six machine learning models are evaluated. The overall performance is assessed on two independent data sets, using metrics like recognition rate, recall, precision and computational cost. The results demonstrate that CBIA achieves a 90% recognition rate with a marginal increment of computational cost. Compared with state-of-the-art algorithms, CBIA improves the recognition rate by 10%, even in difficult scenarios.

      PubDate: 2018-02-03T03:23:58Z
      DOI: 10.1016/j.compag.2017.12.013
      Issue No: Vol. 145 (2018)
  • Distributed monitoring system for precision enology of the Tawny Port wine
           aging process
    • Authors: Raul Morais; Emanuel Peres; J. Boaventura-Cunha; Jorge Mendes; Fernanda Cosme; Fernando M. Nunes
      Pages: 92 - 104
      Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Raul Morais, Emanuel Peres, J. Boaventura-Cunha, Jorge Mendes, Fernanda Cosme, Fernando M. Nunes
      Aging of Tawny Port wine is a multifactorial process critical for attaining the desired quality. Real time monitoring of important intrinsic and extrinsic factors that are known to affect the time and quality of the aging process are important to optimize and to manage the natural variability between wines aged in different long-used wood barrels. For this study, a distributed monitoring system was installed in sixteen oak barrels, placed in two adjacent wineries – one of them with controlled temperature – in the Douro Demarcated Region, Portugal. The monitoring process was performed using a RS-485 industrial network, which interconnects sensors that continuously measure wine temperature, pH, redox potential and wine’s dissolved oxygen, as well as other sensors that measure parameters related to the barrels’ environmental context, such as room temperature and relative humidity. This work presents the design, development and implementation of a remote distributed system to monitor such parameters, aiming to determine the existence of behaviour models for Port Tawny wine during aging in long-used oak barrels, depending on their storage history and to understand the evolution of wine pH, dissolved oxygen and redox potential in real winery conditions as well as their dependence on the wine’s storage temperature. This approach is based on easy-to-use embedded systems, with the aim of giving a relevant contribution to other projects in the area of precision enology.

      PubDate: 2018-02-03T03:23:58Z
      DOI: 10.1016/j.compag.2017.12.019
      Issue No: Vol. 145 (2018)
  • Distributed network for measuring climatic parameters in heterogeneous
           environments: Application in a greenhouse
    • Authors: Javier López-Martínez; José L. Blanco-Claraco; José Pérez-Alonso; Ángel J. Callejón-Ferre
      Pages: 105 - 121
      Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Javier López-Martínez, José L. Blanco-Claraco, José Pérez-Alonso, Ángel J. Callejón-Ferre
      In Mediterranean countries of Southern Europe, the climatic conditions are usually favourable to cultivate greenhouse vegetables but not always for workers. The aim of this study was to design a network of weather stations capable of gathering data of environmental parameters related to the wellbeing of workers in greenhouses in south-eastern Spain. The unevenness of the thermal environment was studied both vertically as well as horizontally following guideline ISO 7726. The results indicate that the greenhouse should be considered a heterogeneous environment, implying that, for an evaluation of the environmental conditions related to thermal stress of the workers inside the greenhouse, measurements should be taken at different points within the greenhouse at three heights (ankle, abdomen, and head).
      Graphical abstract image

      PubDate: 2018-02-03T03:23:58Z
      DOI: 10.1016/j.compag.2017.12.028
      Issue No: Vol. 145 (2018)
  • Wine grape cultivar influence on the performance of models that predict
           the lower threshold canopy temperature of a water stress index
    • Authors: B.A. King; K.C. Shellie
      Pages: 122 - 129
      Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): B.A. King, K.C. Shellie
      The calculation of a thermal based Crop Water Stress Index (CWSI) requires an estimate of canopy temperature under non-water stressed conditions (Tnws ). The objective of this study was to assess the influence of different wine grape cultivars on the performance of models that predict T nws . Stationary infrared sensors were used to measure the canopy temperature of the wine grape cultivars Malbec, Syrah, Chardonnay and Cabernet franc under well-watered conditions over multiple years and modeled as a function of climatic parameters – solar radiation, air temperature, relative humidity and wind speed using multiple linear regression and neural network modeling. Despite differences among cultivars in Tnws , both models provided good prediction results when all cultivars were collectively modeled. For all cultivars, prediction error variance was lower in neural network models developed from cultivar-specific datasets than regression models developed from multi-cultivar datasets. Overall, the cultivar-specific models had less prediction error variance than multi-cultivar models. Multi-cultivar models generally resulted in prediction bias whereas cultivar-specific models eliminated the prediction bias. All predictive models had an uncertainty of ±0.1 in calculation of the CWSI despite significantly different prediction error variance between models.

      PubDate: 2018-02-03T03:23:58Z
      DOI: 10.1016/j.compag.2017.12.025
      Issue No: Vol. 145 (2018)
  • Open geospatial infrastructure for data management and analytics in
           interdisciplinary research
    • Authors: Jacob Høxbroe Jeppesen; Emad Ebeid; Rune Hylsberg Jacobsen; Thomas Skjødeberg Toftegaard
      Pages: 130 - 141
      Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Jacob Høxbroe Jeppesen, Emad Ebeid, Rune Hylsberg Jacobsen, Thomas Skjødeberg Toftegaard
      The terms Internet of Things and Big Data are currently subject to much attention, though the specific impact of these terms in our practical lives are difficult to apprehend. Data-driven approaches do lead to new possibilities, and significant improvements within a broad range of domains can be achieved through a cloud-based infrastructure. In the agricultural sector, data-driven precision agriculture shows great potential in facilitating the increase in food production demanded by the increasing world population. However, the adoption rate of precision agriculture technology has been slow, and information and communications technology needed to promote the implementation of precision agriculture is limited by proprietary integrations and non-standardized data formats and connections. In this paper, an open geospatial data infrastructure is presented, based on standards defined by the Open Geospatial Consortium (OGC). The emphasis in the design was on improved interoperability, with the capability of using sensors, performing cloud processing, carrying out regional statistics, and provide seamless connectivity to machine terminals. The infrastructure was implemented through open source software, and was complemented by open data from governmental offices along with ESA satellite imagery. Four use cases are presented, covering analysis of nearly 50 000 crop fields and providing seamless interaction with an emulated machine terminal. They act to showcase both for how the infrastructure enables modularity and interoperability, and for the new possibilities which arise from this new approach to data within the agricultural domain.

      PubDate: 2018-02-03T03:23:58Z
      DOI: 10.1016/j.compag.2017.12.026
      Issue No: Vol. 145 (2018)
  • Fusion of dielectric spectroscopy and computer vision for quality
           characterization of olive oil during storage
    • Authors: Alireza Sanaeifar; Abdolabbas Jafari; Mohammad-Taghi Golmakani
      Pages: 142 - 152
      Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Alireza Sanaeifar, Abdolabbas Jafari, Mohammad-Taghi Golmakani
      Oxidation level and quality characteristics of olive oil require monitoring during storage to ensure that their amounts are maintained in the lawful thresholds. It is especially important for licensing their commercialization as high-value virgin olive oils. The present research proposes a novel approach based on the fusion of dielectric spectroscopy and computer vision for the characterization of olive oil quality indices during storage in order to reduce the time of analysis, reagent consumption, manpower and high-cost equipment. Colour features in RGB, HSV and L∗a∗b∗ spaces were extracted as well as dielectric features in the frequency range of 40 kHz to 20 MHz for each olive oil sample. After data pre-processing, classification and prediction models were developed and compared. Several machine learning techniques were investigated for storage time classification and quality indices prediction including artificial neural network (ANN), support vector machine (SVM), Bayesian network (BN) and multiple linear regression (MLR). The best result in the classification of olive oils during the storage period was obtained by BN technique with 100% accuracy. Among predictive models, the SVM with RBF kernel had the best results (R = 0.969, 0.988 and 0.976) for prediction of peroxide value (PV), UV absorbance at 232 nm (K232) and chlorophyll, respectively. Also, the SVM with normalized polynomial kernel had the best results (R = 0.989, 0.976, 0.969 and 0.969) for prediction of p-Anisidine value (AV), total oxidation value (TOTOX), UV absorbance at 268 nm (K268) and carotenoid, respectively. The ANN with 40-2-1 topology gave the best result (R = 0.977) for modelling free acidity (FA). Results of this research can be utilized for developing an efficient and reliable system for olive oil quality evaluation and monitoring by industry.

      PubDate: 2018-02-03T03:23:58Z
      DOI: 10.1016/j.compag.2017.12.035
      Issue No: Vol. 145 (2018)
  • Evaluation of support vector machine and artificial neural networks in
           weed detection using shape features
    • Authors: Adel Bakhshipour; Abdolabbas Jafari
      Pages: 153 - 160
      Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Adel Bakhshipour, Abdolabbas Jafari
      Weed detection is still a challenging problem for robotic weed removal. Small tolerance between the cutting tine and main crop position requires highly precise discrimination of the weed against the main crop. Close similarities between the shape features of sugar beet and common weeds make it impossible to define an exclusive feature to be able to efficiently detect all the weeds with acceptable accuracy. Therefore in this study, it was tried to integrate several shape features to establish a pattern for each variety of the plants. To enable the vision system in the detection of the weeds based on their pattern, support vector machine and artificial neural networks were employed. Four species of common weeds in sugar beet fields were studied. Shape feature sets included Fourier descriptors and moment invariant features. Results showed that the overall classification accuracy of ANN was 92.92%, where 92.50% of weeds were correctly classified. Higher accuracies were obtained when the SVM was used as the classifier with an overall accuracy of 95.00% whereas 93.33% of weeds were correctly classified. Also, 93.33% and 96.67% of sugar beet plants were correctly classified by ANN and SVM respectively.

      PubDate: 2018-02-03T03:23:58Z
      DOI: 10.1016/j.compag.2017.12.032
      Issue No: Vol. 145 (2018)
  • Comparison of voltammetry and digital bridge methods for electrical
           resistance measurements in wood
    • Authors: Shan Gao; Zhenyu Bao; Lihai Wang; Xiaoquan Yue
      Pages: 161 - 168
      Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Shan Gao, Zhenyu Bao, Lihai Wang, Xiaoquan Yue
      The comparison of accuracy and ease of operation was made between voltammetry and digital bridge method for electrical resistance measurement in Populus davidiana wood specimens and the factors influencing voltammetry were examined. The results showed that current types, waveforms, voltages and frequency had different effects on the resistance values of voltammetry. The measured DC resistance decreased with the increasing voltage. DC resistance presented a turning point at the voltage of 8 V, while AC impedance remained constant over the entire voltage range. The effects of waveform on resistance was minor. No remarkable difference in resistances was found between the two methods above fiber saturated point (FSP) and voltammetry was relatively stable below FSP. The relationship between MC and resistances confirmed the previous findings from other scholars. Compared to the digital bridge, the voltammetry of AC with 1000 Hz sine waves was found to be the superior method for wood resistance measurement.

      PubDate: 2018-02-03T03:23:58Z
      DOI: 10.1016/j.compag.2017.11.004
      Issue No: Vol. 145 (2018)
  • Ultrasound, microwave and Box-Behnken Design amalgamation offered superior
           yield of gum from Abelmoschus esculentus: Electrical, chemical and
           functional peculiarity
    • Authors: Meenu Nagpal; Geeta Aggarwal; Manish Jindal; Ashish Baldi; Upendra Kumar Jain; Ramesh Chandra; Jitender Madan
      Pages: 169 - 178
      Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Meenu Nagpal, Geeta Aggarwal, Manish Jindal, Ashish Baldi, Upendra Kumar Jain, Ramesh Chandra, Jitender Madan
      Background and objective In present investigation, ultrasonic assisted followed by microwave irradiation involving extraction process was developed under the umbrella of Box-Behnken design for gaining superior yield of gum from okra fruit, Abelmoschus esculentus. Methods and results Stationed on single factor layout, Box-Behnken design was employed to calculate the optimized conditions for isolating the okra fruit gum (OFG) using ultrasonic waves and microwave radiations. The extracted gum was further characterized for particle size, zeta-potential, surface morphology, thermal stability, functional groups, and polymorphism. The optimized conditions like water to raw material ratio of 44.98 ml/g, extraction time of 40 min and ultrasonic power of 60 W provided the uppermost extraction yield of 31.52% ± 0.22% that was analogous to the predicted value. The average mean diameter of OFG was measured to be 256.3 ± 18.4 nm in addition to the zeta potential of −9.85 ± 0.12 mV. SEM image of OFG powder revealed irregular, rough surfaced and amorphous structure of OFG powder. The degree of esterification was measured to be 7.8 with high thermal stability, as exposed by DSC. The FT-IR spectrum of OFG displayed a broad peak at 3405.20 cm−1 announcing presence of OH group and hydrophilicity attribute. The spectrum also presented the small peak at 1605.20 cm−1 (CO) owing to the presence of galacturonic acid besides galactose and rhamnose. Conclusion In conclusion, ultrasound and microwave irradiation assisted extraction process under the shed of Box-Behnken design offered superior yield of OFG that may be used as a pharmaceutical excipient for designing medicated or health products.
      Graphical abstract image

      PubDate: 2018-02-03T03:23:58Z
      DOI: 10.1016/j.compag.2017.12.036
      Issue No: Vol. 145 (2018)
  • A novel compressed sensing based quantity measurement method for grain
    • Authors: Enes Yigit
      Pages: 179 - 186
      Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Enes Yigit
      The quantity of grain in silos has commercial and crucial importance. That’s why many researches have been implemented to detect the quantity of the grain. Although the traditional methods can measure the level of the grain from one measurement point, there has not yet been an effective method regarding to 3 dimensional (3D) volume measurement. Available thru-air radar (TAR) based systems can be adapted to 3D perception by increasing the beamwidth of the illumination. But, to achieve pure grain reflections from cluttered noisy signal (containing multi-path (MP) scatterings and mirror scatterings that suppressed the grain reflections) is a challenging problem. In this study, a new wide-beamwidth radar based level measurement method is firstly proposed to determine the amount of grain in silos. Based on the proposed CS-based method, the back-scattering information of the grain surface is obtained accurately. In this way, Cartesian coordinates of the powerful scattering points of grain surfaces, illuminated electromagnetically by three antennas, are identified and 3D height information belongs to the surface are obtained. According to the dominant scattering point’s coordinates and the probable smooth conical stack of the grain, a heuristic volume expression is derived and the volume of the stack grain is estimated with high accuracy. The success of the developed measurement method is confirmed through a real data of a commercial test silo.

      PubDate: 2018-02-03T03:23:58Z
      DOI: 10.1016/j.compag.2017.12.041
      Issue No: Vol. 145 (2018)
  • Real time laboratory and field monitoring of the effect of the operational
           parameters on seed falling speed and trajectory of pneumatic planter
    • Authors: Zahra Abdolahzare; Saman Abdanan Mehdizadeh
      Pages: 187 - 198
      Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Zahra Abdolahzare, Saman Abdanan Mehdizadeh
      Laboratory tests are usually carried out with different technologies and under quite different conditions to field tests. In this study, a laboratory test was proposed to assess seed spacing uniformity which is applicable in a field situation. In this method, a high-speed camera system was used to detect seed falling trajectory, which is an effective factor on uniformity of seed spacing in both conditions of laboratory and field. Experiments were performed with four pressures (30, 40, 50 & 60 kPa) two forward speed ranges (3 to 4.5 km/h and 6 to 8.5 km/h) and two types of seed (maize and castor) under two conditions of laboratory and field. Results revealed that the highest value of quality of feed for the seed of castor with mean of 98.31% (at forward speed of 3.0 to 4.5 km/h and 4.0 kPa vacuum pressures), and for maize seed with mean of 80% (at 3.0–4.5 km/h and 4.0 kPa) were obtained under laboratory and field conditional, respectively. To determine the locations of seed in the different frames, equations of seed trajectories were first obtained for every treatment and then a general equation was extracted from the entire data set of treatments. This equation is: y = 3.523 e - 0.077 x with value for coefficient of determination, R2 = 0.902. The results show that under operating speed range of 3 to 4.5 km/h, seed spacing uniformity for different treatments of both seeds is statistically the same at 95% confidence level both in the laboratory and in the field. At a forward speed of 6 to 8.5 km/h and 4 levels of vacuum pressure, the difference among calculated mean and actual mean of speeds of fall is statistically significant at 5% probability level.

      PubDate: 2018-02-26T10:42:16Z
      DOI: 10.1016/j.compag.2018.01.001
      Issue No: Vol. 145 (2018)
  • Abnormal shapes of production function: Model interpretations
    • Authors: A. Topaj; W. Mirschel
      Pages: 199 - 207
      Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): A. Topaj, W. Mirschel
      An abnormal non-monotonic shape of production function (response of obtained yield to increasing rates of mineral nitrogen fertilizers) has been observed in experimental field trials. Often, the observed effect (an inflection point, or intermediate plateau or even local undershoot of the “yield-fertilization” curve) is treated as a test distortion and will be ignored or sorted out. This article presents the authors’ efforts to interpret and to explain similar phenomenon by means of investigating two mechanistic crop simulation models – AGROSIM and AGROTOOL. It is demonstrated that an imitation model can be used as a valuable tool of scientific research, allowing for the hypothesising of alternative understandings of non-trivial natural phenomena.

      PubDate: 2018-02-03T03:23:58Z
      DOI: 10.1016/j.compag.2017.12.039
      Issue No: Vol. 145 (2018)
  • Uncertainty of weight measuring systems applied to weighing lysimeters
    • Authors: Alisson M. Amaral; Fernando R. Cabral Filho; Lucas M. Vellame; Marconi B. Teixeira; Frederico A.L. Soares; Leonardo N.S. dos Santos
      Pages: 208 - 216
      Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Alisson M. Amaral, Fernando R. Cabral Filho, Lucas M. Vellame, Marconi B. Teixeira, Frederico A.L. Soares, Leonardo N.S. dos Santos
      The determination of measurement reliability in weighing lysimeters via error analysis is essential for scientific research and irrigation management. The objective of this study was to evaluate four different weight measuring systems (MSs) applied to load cell weighing lysimeters and compare the results with the expected uncertainty values obtained from data provided by manufacturers. A weighing lysimeter with an area of 0.385 m2 and a volume of 0.289 m3 was used, installed on three load cells. In MS1, the load cells were connected to a junction box and the box to a weighing indicator module in a six-wire configuration. In MS2, a four-wire connection was used between the junction box and a datalogger, whereas in MS3, there was a six-wire connection. For MS4, the connection between the load cells and datalogger was direct. The uncertainties of the measurement systems were determined from the calibration results. MS1 presented the lowest measurement errors and uncertainties, resulting in performance superior to those of the other MSs. After MS1, the best performances were obtained by MS2 and MS3, and MS4 presented the worst performance. The effect of the signal measurement uncertainties and the excitation by the datalogger had the greatest effects on the overall uncertainty of the system compared with the influence of temperature on the load cells. The measurement system may be selected according to the technical data supplied by the manufacturer; however, periodic calibration of the effective measuring range is necessary to verify and compensate for systematic errors, which are accentuated during the operation time.

      PubDate: 2018-02-03T03:23:58Z
      DOI: 10.1016/j.compag.2017.12.033
      Issue No: Vol. 145 (2018)
  • Suitability evaluation system for the production and sourcing of
           agricultural commodities
    • Authors: Isabel Jaisli; Patrick Laube; Sonja Trachsel; Pascal Ochsner; Sarah Schuhmacher
      Abstract: Publication date: Available online 23 February 2018
      Source:Computers and Electronics in Agriculture
      Author(s): Isabel Jaisli, Patrick Laube, Sonja Trachsel, Pascal Ochsner, Sarah Schuhmacher
      This article presents CONSUS (Connecting for Sustainable Sourcing), a modular GIS-based decision-support system for producing and sourcing agricultural commodities. The system extends the classic FAO land evaluation approaches in three specific dimensions: (i) the sustainability dimension: the extended suitability analysis reaches beyond purely biophysical suitability and integrates ecological, economic and social suitability; (ii) the value chain dimension: the focus of suitability analysis includes further upstream activities relevant for product trading; (iii) the spatio-temporal dimension: the inclusion of an adaptive global crop cycle model and scale-specific suitability modules allow for multi-scale suitability evaluation that considers cropping seasons; The system was implemented as a flexible tool set, featuring knowledge databases, GIS toolboxes, and supporting data processing modules. CONSUS emerged from a series of third party funded applied research projects. Two of these serve in this article as case studies illustrating the capabilities of the system: one global case study on the sourcing of hazelnuts, and one regional case study on suitability of soybean production in Rwanda.

      PubDate: 2018-02-26T10:42:16Z
      DOI: 10.1016/j.compag.2018.02.002
  • Determination of egg storage time at room temperature using a low-cost NIR
           spectrometer and machine learning techniques
    • Authors: Julian Coronel-Reyes; Ivan Ramirez-Morales; Enrique Fernandez-Blanco; Daniel Rivero; Alejandro Pazos
      Pages: 1 - 10
      Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Julian Coronel-Reyes, Ivan Ramirez-Morales, Enrique Fernandez-Blanco, Daniel Rivero, Alejandro Pazos
      Currently, consumers are more concerned about freshness and quality of food. Poultry egg storage time is a freshness and quality indicator in industrial and consumer applications, even though egg marking is not always required outside the European Union. Other authors have already published works using expensive laboratory equipment in order to determine the storage time and freshness of eggs. This paper presents a novel alternative method based on low-cost devices for the rapid and non-destructive prediction of egg storage time at room temperature (23 ± 1 °C). H&N brown flock with 49-week-old hens were used as a source for the sampled eggs. Samples were scanned for a period of 22 days beginning from the time the egg was laid. The spectral acquisition was performed using a low-cost near-infrared reflectance (NIR) spectrometer which has a wavelength range between 740 nm and 1070 nm. The resulting dataset of 660 samples was randomly split according to a 10-fold cross-validation in order to be used in a contrast and optimization process of two machine learning algorithms. During the optimization, several models were tested to develop a robust calibration model. The best model used a Savitzky Golay pre-processing technique with a third derivative order and an artificial neural network with ten neurons in one hidden layer. Regressing the storage time of the eggs, tests achieved a coefficient of determination (R-squared) of 0.8319 ± 0.0377 and a root mean squared error in cross-validation test set (RMSECV) of 1.97 days. Although further work is needed, this technique shows industrial potential and consumer utility to determine an egg's freshness using a low-cost spectrometer connected to a smartphone.

      PubDate: 2017-12-27T09:55:02Z
      DOI: 10.1016/j.compag.2017.12.030
      Issue No: Vol. 145 (2017)
  • Development of an electro-mechanic control system for seed-metering unit
           of single seed corn planters Part II: Field performance
    • Authors: Anil Cay; Habib Kocabiyik; Sahin May
      Pages: 11 - 17
      Abstract: Publication date: February 2018
      Source:Computers and Electronics in Agriculture, Volume 145
      Author(s): Anil Cay, Habib Kocabiyik, Sahin May
      Using single seed planters is important for a uniform distribution of plant growing area. Seed metering units of planters receive their motion from the drive wheel pass through various transmission members such as the chains, gears, shafts and belts. While the planter is being operated, the transmission system of the machine and drive system of the seed metering units naturally push the driving wheel. Because of this effect, the wheel experiences a loss of mobility or some sort of slipping. Consequently, all seed metering units are being affected due to the common mobility transmission system and changes in the desired plant spacing occur. In order to overcome these negativities, an electro-mechanic drive system (EMDS) alternative to classic driving system (CDS) was developed. Detailed information regarding the system design and laboratory simulation results of EMDS were provided in Part I of this study. In this part, it was aimed to investigate the effect of EMDS on the planting quality (plant spacing uniformity, variation among rows) and operational parameters (fuel consumption and negative slippage) in the field and compared with the CDS. While the quality of feed index (Iqf) 90.63%, multiple index (Imult) 0.94%, missing index (Imiss) 8.44% and precision index (Ip) 17.63% were obtained in trials performed by the EMDS, Iqf 88.13%, Imult 2.50%, Imiss 9.38% and Ip 17.81% were found in trials performed by the CDS. Plant spacing uniformity in the EMDS was found as “good” while it was “moderate” in the CDS, according to related criteria. Plant distribution uniformity in the EMDS were better than the CDS. Furthermore, the experimental plant spacing values obtained by the EMDS were closer to the theoretical (set) value than the values obtained by the CDS. The negative slipping in the planter’s drive wheel was found as 1.33% at trials with the EMDS while it was 6.79% with the CDS. When the EMDS used in the field operations, it provided approximately 22% fuel saving compared with the CDS. The results promise that the developed system can be used as an alternative to the CDS for single seed planters. However, in order to provide a complete mechanical rapport between the EMDS and the planter, future studies, various structural improvements in the seed metering unit designs and optimization of the seed plate thickness, number of holes and connection methods may be required.

      PubDate: 2017-12-27T09:55:02Z
      DOI: 10.1016/j.compag.2017.12.021
      Issue No: Vol. 145 (2017)
School of Mathematical and Computer Sciences
Heriot-Watt University
Edinburgh, EH14 4AS, UK
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Fax: +00 44 (0)131 4513327
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