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  Subjects -> ENGINEERING (Total: 2270 journals)
    - CHEMICAL ENGINEERING (191 journals)
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    - ELECTRICAL ENGINEERING (99 journals)
    - ENGINEERING (1199 journals)
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ENGINEERING (1199 journals)                  1 2 3 4 5 6 | Last

Showing 1 - 200 of 1205 Journals sorted alphabetically
3 Biotech     Open Access   (Followers: 7)
3D Research     Hybrid Journal   (Followers: 19)
AAPG Bulletin     Full-text available via subscription   (Followers: 5)
AASRI Procedia     Open Access   (Followers: 15)
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: 217)
Acta Geotechnica     Hybrid Journal   (Followers: 6)
Acta Metallurgica Sinica (English Letters)     Hybrid Journal   (Followers: 5)
Acta Polytechnica : Journal of Advanced Engineering     Open Access   (Followers: 2)
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: 10)
Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi     Open Access  
Adsorption     Hybrid Journal   (Followers: 4)
Advanced Engineering Forum     Full-text available via subscription   (Followers: 4)
Advanced Science     Open Access   (Followers: 4)
Advanced Science Focus     Free   (Followers: 3)
Advanced Science Letters     Full-text available via subscription   (Followers: 5)
Advanced Science, Engineering and Medicine     Partially Free   (Followers: 7)
Advanced Synthesis & Catalysis     Hybrid Journal   (Followers: 17)
Advances in Artificial Neural Systems     Open Access   (Followers: 4)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
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Advances in Complex Systems     Hybrid Journal   (Followers: 7)
Advances in Engineering Software     Hybrid Journal   (Followers: 25)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 14)
Advances in Fuzzy Systems     Open Access   (Followers: 5)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 9)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 19)
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Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 8)
Advances in Natural Sciences: Nanoscience and Nanotechnology     Open Access   (Followers: 28)
Advances in Operations Research     Open Access   (Followers: 11)
Advances in OptoElectronics     Open Access   (Followers: 5)
Advances in Physics Theories and Applications     Open Access   (Followers: 12)
Advances in Polymer Science     Hybrid Journal   (Followers: 40)
Advances in Porous Media     Full-text available via subscription   (Followers: 4)
Advances in Remote Sensing     Open Access   (Followers: 35)
Advances in Science and Research (ASR)     Open Access   (Followers: 6)
Aerobiologia     Hybrid Journal   (Followers: 1)
African Journal of Science, Technology, Innovation and Development     Hybrid Journal   (Followers: 4)
AIChE Journal     Hybrid Journal   (Followers: 28)
Ain Shams Engineering Journal     Open Access   (Followers: 5)
Akademik Platform Mühendislik ve Fen Bilimleri Dergisi     Open Access  
Alexandria Engineering Journal     Open Access   (Followers: 1)
AMB Express     Open Access   (Followers: 1)
American Journal of Applied Sciences     Open Access   (Followers: 27)
American Journal of Engineering and Applied Sciences     Open Access   (Followers: 11)
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)
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: 8)
Applied Clay Science     Hybrid Journal   (Followers: 4)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Applied Magnetic Resonance     Hybrid Journal   (Followers: 3)
Applied Nanoscience     Open Access   (Followers: 7)
Applied Network Science     Open Access  
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Physics Research     Open Access   (Followers: 3)
Applied Sciences     Open Access   (Followers: 2)
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: 4)
Archives of Foundry Engineering     Open Access  
Archives of Thermodynamics     Open Access   (Followers: 7)
Arkiv för Matematik     Hybrid Journal   (Followers: 1)
ASEE Prism     Full-text available via subscription   (Followers: 2)
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: 7)
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: 7)
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: 3)
Batteries     Open Access   (Followers: 3)
Bautechnik     Hybrid Journal   (Followers: 1)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 23)
Beni-Suef University Journal of Basic and Applied Sciences     Open Access   (Followers: 3)
BER : Manufacturing Survey : Full Survey     Full-text available via subscription   (Followers: 2)
BER : Motor Trade Survey     Full-text available via subscription   (Followers: 1)
BER : Retail Sector Survey     Full-text available via subscription   (Followers: 2)
BER : Retail Survey : Full Survey     Full-text available via subscription   (Followers: 2)
BER : Survey of Business Conditions in Manufacturing : An Executive Summary     Full-text available via subscription   (Followers: 3)
BER : Survey of Business Conditions in Retail : An Executive Summary     Full-text available via subscription   (Followers: 3)
Bharatiya Vaigyanik evam Audyogik Anusandhan Patrika (BVAAP)     Open Access   (Followers: 1)
Biofuels Engineering     Open Access  
Biointerphases     Open Access   (Followers: 1)
Biomaterials Science     Full-text available via subscription   (Followers: 9)
Biomedical Engineering     Hybrid Journal   (Followers: 16)
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: 16)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 31)
Biomedical Engineering: Applications, Basis and Communications     Hybrid Journal   (Followers: 5)
Biomedical Microdevices     Hybrid Journal   (Followers: 8)
Biomedical Science and Engineering     Open Access   (Followers: 3)
Biomedizinische Technik - Biomedical Engineering     Hybrid Journal  
Biomicrofluidics     Open Access   (Followers: 4)
BioNanoMaterials     Hybrid Journal   (Followers: 1)
Biotechnology Progress     Hybrid Journal   (Followers: 39)
Boletin Cientifico Tecnico INIMET     Open Access  
Botswana Journal of Technology     Full-text available via subscription  
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: 14)
Bulletin of Engineering Geology and the Environment     Hybrid Journal   (Followers: 3)
Bulletin of the Crimean Astrophysical Observatory     Hybrid Journal  
Cahiers, Droit, Sciences et Technologies     Open Access  
Calphad     Hybrid Journal  
Canadian Geotechnical Journal     Full-text available via subscription   (Followers: 13)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 40)
Case Studies in Engineering Failure Analysis     Open Access   (Followers: 7)
Case Studies in Thermal Engineering     Open Access   (Followers: 3)
Catalysis Communications     Hybrid Journal   (Followers: 6)
Catalysis Letters     Hybrid Journal   (Followers: 2)
Catalysis Reviews: Science and Engineering     Hybrid Journal   (Followers: 8)
Catalysis Science and Technology     Free   (Followers: 6)
Catalysis Surveys from Asia     Hybrid Journal   (Followers: 3)
Catalysis Today     Hybrid Journal   (Followers: 5)
CEAS Space Journal     Hybrid Journal  
Cellular and Molecular Neurobiology     Hybrid Journal   (Followers: 3)
Central European Journal of Engineering     Hybrid Journal   (Followers: 1)
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: 1)
CienciaUAT     Open Access  
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: 14)
City, Culture and Society     Hybrid Journal   (Followers: 21)
Clay Minerals     Full-text available via subscription   (Followers: 9)
Clean Air Journal     Full-text available via subscription   (Followers: 2)
Coal Science and Technology     Full-text available via subscription   (Followers: 3)
Coastal Engineering     Hybrid Journal   (Followers: 11)
Coastal Engineering Journal     Hybrid Journal   (Followers: 4)
Coatings     Open Access   (Followers: 2)
Cogent Engineering     Open Access   (Followers: 2)
Cognitive Computation     Hybrid Journal   (Followers: 4)
Color Research & Application     Hybrid Journal   (Followers: 1)
COMBINATORICA     Hybrid Journal  
Combustion Theory and Modelling     Hybrid Journal   (Followers: 13)
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: 23)
Composite Interfaces     Hybrid Journal   (Followers: 6)
Composite Structures     Hybrid Journal   (Followers: 252)
Composites Part A : Applied Science and Manufacturing     Hybrid Journal   (Followers: 176)
Composites Part B : Engineering     Hybrid Journal   (Followers: 222)
Composites Science and Technology     Hybrid Journal   (Followers: 164)
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Computers and Electronics in Agriculture     Hybrid Journal   (Followers: 4)
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Computing and Visualization in Science     Hybrid Journal   (Followers: 6)
Computing in Science & Engineering     Full-text available via subscription   (Followers: 25)
Conciencia Tecnologica     Open Access  
Concurrent Engineering     Hybrid Journal   (Followers: 3)
Continuum Mechanics and Thermodynamics     Hybrid Journal   (Followers: 6)
Control and Dynamic Systems     Full-text available via subscription   (Followers: 8)
Control Engineering Practice     Hybrid Journal   (Followers: 41)
Control Theory and Informatics     Open Access   (Followers: 7)
Corrosion Science     Hybrid Journal   (Followers: 24)
CT&F Ciencia, Tecnologia y Futuro     Open Access  
CTheory     Open Access  

        1 2 3 4 5 6 | Last

Journal Cover Computers and Electronics in Agriculture
  [SJR: 0.823]   [H-I: 73]   [4 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 0168-1699
   Published by Elsevier Homepage  [3031 journals]
  • Species – Transport CFD model for the gasification of rice husk (Oryza
           Sativa) using downdraft gasifier
    • Abstract: Publication date: 15 June 2017
      Source:Computers and Electronics in Agriculture, Volume 139
      Author(s): P.C. Murugan, S. Joseph Sekhar
      The biomass as a source of energy is increasing globally due to environmental pollution and shortage of fossil fuels. The process of converting the biomass into useful energy is known as gasification. The implementation of gasification technology in remote areas needs more theoretical and experimental studies. Several models have been proposed to explain the geometrical parameters and the optimization of gasification process. Hence, the paper presents the numerical simulation of the gasifier using the species-transport model in CFD by incorporating all the zones of the gasifier and the experimental studies are conducted on a 40kW downdraft gasifier with rice husk (Oryza Sativa) available in remote villages situated in South India. The factors affecting the producer gas such as equivalence ratio, gas composition, higher heating value and the temperature distribution inside the gasifier have been studied. Both the studies established that the higher heating value and the producer gas composition are 5.19MJ/Nm3 with CO–22%, CH4–1.7%, CO2–8%, H2–13% and N2–40.1% respectively. Also, it is predicted that the zone temperatures are maximum when ER is 0.30.
      Graphical abstract image

      PubDate: 2017-05-21T16:27:35Z
       
  • An overview of current and potential applications of thermal remote
           sensing in precision agriculture
    • Abstract: Publication date: 15 June 2017
      Source:Computers and Electronics in Agriculture, Volume 139
      Author(s): Sami Khanal, John Fulton, Scott Shearer
      Precision agriculture (PA) utilizes tools and technologies to identify in-field soil and crop variability for improving farming practices and optimizing agronomic inputs. Traditionally, optical remote sensing (RS) that utilizes visible light and infrared regions of the electromagnetic spectrum has been used as an integral part of PA for crop and soil monitoring. Optical RS, however, is slow in differentiating stress levels in crops until visual symptoms become noticeable. Surface temperature is considered to be a rapid response variable that can indicate crop stresses prior to their visual symptoms. By measuring estimates of surface temperature, thermal RS has been found to be a promising tool for PA. Compared to optical RS, applications of thermal RS for PA have been limited. Until recently (i.e., before the advancement of low cost RS platforms such as unmanned aerial systems (UAVs)), the availability of high resolution thermal images was limited due to high acquisition costs. Given recent developments in UAVs, thermal images with high spatial and temporal resolutions have become available at a low cost, which has increased opportunities to understand in-field variability of crop and soil conditions useful for various agronomic decision-making. Before thermal RS is adopted as a routine tool for crop and environmental monitoring, there is a need to understand its current and potential applications as well as issues and concerns. This review focuses on current and potential applications of thermal RS in PA as well as some concerns relating to its application. The application areas of thermal RS in agriculture discussed here include irrigation scheduling, drought monitoring, crop disease detection, and mapping of soil properties, residues and tillage, field tiles, and crop maturity and yield. Some of the issues related to its application include spatial and temporal resolution, atmospheric conditions, and crop growth stages.

      PubDate: 2017-05-21T16:27:35Z
       
  • Variation analysis in spectral indices of volatile chlorpyrifos and
           non-volatile imidacloprid in jujube (Ziziphus jujuba Mill.) using
           near-infrared hyperspectral imaging (NIR-HSI) and gas chromatograph-mass
           spectrometry (GC–MS)
    • Abstract: Publication date: 15 June 2017
      Source:Computers and Electronics in Agriculture, Volume 139
      Author(s): Wen-Hao Su, Da-Wen Sun, Jian-Guo He, Ling-Biao Zhang
      Two pesticides in terms of chlorpyrifos and imidacloprid contaminating edible jujube fruits were determined using hyperspectral imaging (900–1700nm) and gas chromatograph-mass spectrometry (GC–MS). Hyperspectral images of jujube samples contaminated by pesticides at different concentrations were collected. Their spectral data extracted in reflectance (RS), absorbance (AS), exponent (ES) and Kubelka–Munck (K-MS), were respectively used to develop partial least squares discriminant analysis (PLSDA)and locally weighted partial least square regression (LWPLSR) models. Based on these spectral parameters, corresponding models defined as AS-PLSDA and ES-PLSDA acquired optimal results, with correlation coefficients of cross-validation (R CV) of more than 0.900 for recognition of chlorpyrifos concentrations and R CV of over 0.713 for identification of concentrations of the imidacloprid. The ES-LWPLSR model obtained the best R CV of 0.864 for quantitative determination of chlorpyrifos residuals, and the best R CV of 0.885 for determination of imidacloprid residuals. The feature wavelengths were selected based on the automatic weighted least squares and gap segment derivative (AWLS-GSD) coupled with regression coefficient (RC) method. The better performance was obtained by the resulting simplified ES-AWLS-GSD-RC-LWPLSR model established using only eight characteristic wavelengths, with R CV of 0.757, RMSECV of 3.75×10−3 for chlorpyrifos residuals, and R CV of 0.898, RMSECV of 0.311×10−3 for imidacloprid residuals. To summarize, hyperspectral imaging technology shows a great potential to predict pesticide residuals of jujube fruit.

      PubDate: 2017-05-21T16:27:35Z
       
  • Optimization of QR code readability in movement state using response
           surface methodology for implementing continuous chain traceability
    • Abstract: Publication date: 15 June 2017
      Source:Computers and Electronics in Agriculture, Volume 139
      Author(s): Jianping Qian, Xiaowei Du, Baoyan Zhang, Beilei Fan, Xinting Yang
      Logistics and storage is the main processing for agro-food supply chain. Because of disconnection information between the two processing, it is difficult to trace continuously. An intelligent conveyer belt provides an effective method to associate storage and logistics by QR code scanning and information recording. Improving the QR code readability in movement state is the core of implementing continuous chain traceability with this belt. In this paper, a intelligent conveyer belt including automatic conveyer unit, barcode scanning unit, fault remove unit and control display unit was designed. Four factors affected QR readability were selected and the value range was confirmed, which was reading distance, code size, coded characters and belt moving speed. Based on the belt, an Central Composite Inscribed (CCI) experiment of four factors with five levels was designed using Response Surface Methodology (RSM) to obtain the optimal reading parameters. The result shows that the main factors of reading distance, belt moving speed and the interaction between reading distance and code size have the significant effect on QR code readability. Under the optimization condition of 141.45mm reading distance, 34.58mm code size, 100 bytes coded characters and 2.98m/min belt moving speed, the average value of QR code readability was 95%. With the optimization parameters, the intelligent conveyer belt was used in an apple marketing enterprise. The result shows that the continuous traceability between storage and logistic can be implemented with the extended breadth, deepened depth and improved precision.

      PubDate: 2017-05-21T16:27:35Z
       
  • Modeling pressure drop produced by different filtering media in
           microirrigation sand filters using the hybrid ABC-MARS-based approach, MLP
           neural network and M5 model tree
    • Abstract: Publication date: 15 June 2017
      Source:Computers and Electronics in Agriculture, Volume 139
      Author(s): P.J. García Nieto, E. García-Gonzalo, J. Bové, G. Arbat, M. Duran-Ros, J. Puig-Bargués
      Granular media filters are commonly used to remove suspended solids and prevent emitter clogging in microirrigation systems. Silica sand is the standard filtering media but other granular materials can be used for this purpose. The characterization of the pressure drop produced by the clean filtering media is of practical interest for designing and managing these filters. Different models such as Ergun or Kozeny-Carman equations are usually used to predict pressure drop produced by the filtering media. However, as parameters of the media such as equivalent diameter and sphericity, that are difficult to determine, appear in these equations, the objective of this study was to construct a new model to estimate the pressure drop of different filtering materials of interest in granular filters with limited data of the physical parameters that characterize the filtering media. This paper, taking as starting point the multivariate adaptive regression splines (MARS), develops a new algorithm hybridizing it with the artificial bee colony (ABC) method, to estimate the pressure drop in granular filters broadly used in microirrigation systems for the first time with a data-driven model. Laboratory experiments were used to measure pressure drop across silica sand, crushed glass, modified glass and glass microspheres in a scaled filter at surface velocities ranging from 0.004 to 0.025ms−1. The ABC method allows the tuning of the MARS parameters during the training phase improving significantly the regression accuracy. Additionally, a multilayer perceptron network (MLP) and M5 model tree were fitted to the experimental data for comparison purposes. The results have shown that ABC-MARS-based model was the best estimation of the pressure drop with a coefficient of determination of 0.78. Therefore, ABC-MARS-based model could be easily implemented to predict pressure drop with minimal input parameters for other filtering materials used in microirrigation media filters.

      PubDate: 2017-05-21T16:27:35Z
       
  • Computer vision based method for quality and freshness check for fish from
           segmented gills
    • Abstract: Publication date: 15 June 2017
      Source:Computers and Electronics in Agriculture, Volume 139
      Author(s): Ashish Issac, Malay Kishore Dutta, Biplab Sarkar
      The quality and freshness of a fish sample is principally hampered in the post-harvested phase due to storage, handling and processing. The quality of the sample may degrade as the days pass till it finally reaches the consumers. The quality of a post harvested fish is determined mainly by two important factors namely climatic conditions and holding time. This paper presents a completely automated computer vision based segmentation of fish gills from digital images of fish samples. Post segmentation, a statistical relationship of the segmented gill region is established to design an assessment model for fish freshness identification. The fish gills are segmented using various strategic image processing techniques like contrast enhancement, adaptive intensity threshold and active contour based methods. The model for fish freshness testing is based on the image statistical features which are derived from the gills region of the saturation channel. The variation of the statistical distribution is observed to be decreasing monotonic which is basis for design of the framework for fish quality and freshness identification. This process being non-destructive provides an efficient fish quality assessment scheme in real time.

      PubDate: 2017-05-16T15:11:04Z
       
  • Inside Front Cover - Editorial Board Page/Cover image legend if applicable
    • Abstract: Publication date: 1 June 2017
      Source:Computers and Electronics in Agriculture, Volume 138


      PubDate: 2017-05-16T15:11:04Z
       
  • Fine crop mapping by combining high spectral and high spatial resolution
           remote sensing data in complex heterogeneous areas
    • Abstract: Publication date: 15 June 2017
      Source:Computers and Electronics in Agriculture, Volume 139
      Author(s): Mingquan Wu, Wenjiang Huang, Zheng Niu, Yu Wang, Changyao Wang, Wang Li, Pengyu Hao, Bo Yu
      In complex heterogeneous areas, it is difficult to map crops with high accuracy using only high spatial resolution or only high spectral resolution remote sensing data. Because the spectral resolution of high spatial resolution data is too low, the spectral differentiations of different vegetation types are very small in high spatial resolution data. It is hard to distinguish between different vegetation types using high spatial resolution data. For high spectral resolution remote sensing data, it is hard to exclude linear objects like roads, bridges and drains from crops due to the low spatial resolution of these data. To address this problem, a novel object-based fine crop mapping method by combining high spatial and high spectral resolution remote sensing data for heterogeneous areas was proposed and validated in Suzhou city, Jiangsu province, China. First, pure crop polygons were derived from a 0.5m aerial data. Due to the high spatial resolution, non-cultivated land could be easily isolated from arable land. Then, a Hyperion data was used to classify crops for each of the pure crop polygons. The results show that this method can map crops in complex heterogeneous areas with an overall accuracy higher than 95%, which is much higher than the accuracy of maps classified using only high spatial resolution data or only high spectral resolution data, which have an overall accuracy of 58.78% and 77.54%, respectively.

      PubDate: 2017-05-11T15:05:46Z
       
  • Streaming and 3D mapping of AGRI-data on mobile devices
    • Abstract: Publication date: 1 June 2017
      Source:Computers and Electronics in Agriculture, Volume 138
      Author(s): V. Stojanovic, Ruth E. Falconer, J. Isaacs, D. Blackwood, D. Gilmour, D. Kiezebrink, J. Wilson
      Farm monitoring and operations generate heterogeneous AGRI-data from a variety of different sources that have the potential to be delivered to users ‘on the go’ and in the field to inform farm decision making. A software framework capable of interfacing with existing web mapping services to deliver in-field farm data on commodity mobile hardware was developed and tested. This raised key research challenges related to: robustness of data steaming methods under typical farm connectivity scenarios, and mapping and 3D rendering of AGRI-data in an engaging and intuitive way. The presentation of AGRI-data in a 3D and interactive context was explored using different visualisation techniques; currently the 2D presentation of AGRI- data is the dominant practice, despite the fact that mobile devices can now support sophisticated 3D graphics via programmable pipelines. The testing found that WebSockets were the most reliable streaming method for high resolution image/texture data. From our focus groups there was no single visualisation technique that was preferred demonstrating that a range of methods is a good way to satisfy a large user base. Improved 3D experience on mobile phones is set to revolutionize the multimedia market and a key challenge is identifying useful 3D visualisation methods and navigation tools that support the exploration of data driven 3D interactive visualisation frameworks for AGRI-data.

      PubDate: 2017-05-11T15:05:46Z
       
  • Automatic plant disease diagnosis using mobile capture devices, applied on
           a wheat use case
    • Abstract: Publication date: 1 June 2017
      Source:Computers and Electronics in Agriculture, Volume 138
      Author(s): Alexander Johannes, Artzai Picon, Aitor Alvarez-Gila, Jone Echazarra, Sergio Rodriguez-Vaamonde, Ana Díez Navajas, Amaia Ortiz-Barredo
      Disease diagnosis based on the detection of early symptoms is a usual threshold taken into account for integrated pest management strategies. Early phytosanitary treatment minimizes yield losses and increases the efficacy and efficiency of the treatments. However, the appearance of new diseases associated to new resistant crop variants complicates their early identification delaying the application of the appropriate corrective actions. The use of image based automated identification systems can leverage early detection of diseases among farmers and technicians but they perform poorly under real field conditions using mobile devices. A novel image processing algorithm based on candidate hot-spot detection in combination with statistical inference methods is proposed to tackle disease identification in wild conditions. This work analyses the performance of early identification of three European endemic wheat diseases – septoria, rust and tan spot. The analysis was done using 7 mobile devices and more than 3500 images captured in two pilot sites in Spain and Germany during 2014, 2015 and 2016. Obtained results reveal AuC (Area under the Receiver Operating Characteristic–ROC–Curve) metrics higher than 0.80 for all the analyzed diseases on the pilot tests under real conditions.

      PubDate: 2017-05-11T15:05:46Z
       
  • An adaptive approach for UAV-based pesticide spraying in dynamic
           environments
    • Abstract: Publication date: 1 June 2017
      Source:Computers and Electronics in Agriculture, Volume 138
      Author(s): Bruno S. Faiçal, Heitor Freitas, Pedro H. Gomes, Leandro Y. Mano, Gustavo Pessin, André C.P.L.F. de Carvalho, Bhaskar Krishnamachari, Jó Ueyama
      Agricultural production has become a key factor for the stability of the world economy. The use of pesticides provides a more favorable environment for the crops in agricultural production. However, the uncontrolled and inappropriate use of pesticides affect the environment by polluting preserved areas and damaging ecosystems. In the precision agriculture literature, several authors have proposed solutions based on Unmanned Aerial Vehicles (UAVs) and Wireless Sensor Networks (WSNs) for developing spraying processes that are safer and more precise than the use of manned agricultural aircraft. However, the static configuration usually adopted in these proposals makes them inefficient in environments with changing weather conditions (e.g. sudden changes of wind speed and direction). To overcome this deficiency, this paper proposes a computer-based system that is able to autonomously adapt the UAV control rules, while keeping precise pesticide deposition on the target fields. Different versions of the proposal, with autonomously route adaptation metaheuristics based on Genetic Algorithms, Particle Swarm Optimization, Simulated Annealing and Hill-Climbing for optimizing the intensity of route changes are evaluated in this study. Additionally, this study evaluates the use of a ground control station and an embedded hardware to run the route adaptation metaheuristics. Experimental results show that the proposed computer-based system approach with autonomous route change metaheuristics provides more precise changes in the UAV’s flight route, with more accurate deposition of the pesticide and less environmental damage.

      PubDate: 2017-05-11T15:05:46Z
       
  • Symptom based automated detection of citrus diseases using color histogram
           and textural descriptors
    • Abstract: Publication date: 1 June 2017
      Source:Computers and Electronics in Agriculture, Volume 138
      Author(s): H. Ali, M.I. Lali, M.Z. Nawaz, M. Sharif, B.A. Saleem
      This paper presents a technique to detect and classify major citrus diseases of economic importance. Kinnow mandarin being 80% of Pakistan citrus industry was the main focus of study. Due to a little variation in symptoms of different plant diseases, the diagnosis requires the expert’s opinion in diseases detection. The inappropriate diagnosis may lead to tremendous amount of economical loss for farmers in terms of inputs like pesticides. For many decades, computers have been used to provide automatic solutions instead of a manual diagnosis of plant diseases which is costly and error prone. The proposed method applied ΔE color difference algorithm to separate the disease affected area, further, color histogram and textural features were used to classify diseases. Our method out performed and achieved overall 99.9% accuracy and similar sensitivity with 0.99 area under the curve. Moreover, the combination of color and texture features was used for experiments and achieves similar results, as compared to individual channels. Principle components analysis was applied for the features set dimension reduction and these reduced features were also tested using state of the art classifiers.

      PubDate: 2017-05-07T14:56:53Z
       
  • Yield analysis as a function of stochastic plant architecture: Case of
           Spilanthes acmella in the wet and dry season
    • Abstract: Publication date: 1 June 2017
      Source:Computers and Electronics in Agriculture, Volume 138
      Author(s): Marie Elodie Vavitsara, Sylvie Sabatier, MengZhen Kang, Hery Lisy Tiana Ranarijaona, Philippe de Reffye
      The number of organs produced by a plant varies among the individuals of a population. Taking these variations into account is an important step in understanding phenotypic variability. The aim of this study was to simulate stochastic development and growth in response to environmental change using GreenLab, an organ level functional-structural model. An annual herbaceous species, Spilanthes acmella L., was grown in pots in two climatic conditions corresponding to a wet and a dry season. Detailed records of plant development, plant architecture and organ growth were kept throughout the growing period. The concept of simple and compound organic series was introduced to target data for fitting. The model was calibrated using a mathematical model of stochastic plant development and growth. Here we describe (1) how a stochastic Functional Structural Plant Model is calibrated in two steps by first assessing the functioning parameters of meristems, and second the source-sink parameters of organs by fitting them on average organic series; (2) how dry conditions trigger the response of the plant both in the development of the inflorescence and in the allocation of biomass, quantified by model parameters. The calibration of a stochastic plant model opens a large window of opportunity to capture the common features of plant development and growth among stochastic individuals in a plant population, especially those with a branching structure. This extends the area of application of FSPM to analyzing food plants, or assisting breeding.

      PubDate: 2017-05-07T14:56:53Z
       
  • Inline discrete tomography system: Application to agricultural product
           inspection
    • Abstract: Publication date: 1 June 2017
      Source:Computers and Electronics in Agriculture, Volume 138
      Author(s): Luis F. Alves Pereira, Eline Janssens, George D.C. Cavalcanti, Ing Ren Tsang, Mattias Van Dael, Pieter Verboven, Bart Nicolai, Jan Sijbers
      X-ray Computed Tomography (CT) has been applied in agriculture engineering for quality and defect control in food products. However, conventional CT systems are neither cost effective nor flexible, making the deployment of such technology unfeasible for many industrial environments. In this work, we propose a simple and cost effective X-ray imaging setup that comprises a linear translation of the object in a conveyor belt with a fixed X-ray source and detector, with which a small number of X-ray projections can be acquired within a limited angular range. Due to the limitations of such geometry, conventional reconstruction techniques lead to misshapen images. Therefore, we apply a Discrete Tomography reconstruction technique that incorporates prior knowledge of the density of the object’s materials. Moreover, we further improve the reconstruction results with the following strategies: (i) an image acquisition involving object rotation during a linear translation in the conveyor-belt; and (ii) an image reconstruction incorporating prior knowledge of the object support (e.g., obtained from optic sensors). Experiments based on simulation as well as real data demonstrate substantial improvement of the reconstruction quality compared to conventional reconstruction methods.

      PubDate: 2017-05-07T14:56:53Z
       
  • Automatic detection of suckling events in lamb through accelerometer data
           classification
    • Abstract: Publication date: 1 June 2017
      Source:Computers and Electronics in Agriculture, Volume 138
      Author(s): Ewa Kuźnicka, Paweł Gburzyński
      We report on an experimental study aimed at establishing a framework for automated detection of suckling episodes in lamb. Suckling turns out to be an important element of the animal’s behavior, because it occurs early in its development cycle and is directly linked to the fundamental predictors of its success. Our objective was to build an inexpensive, unobtrusive, maintenance-free, and energy-efficient device easily attachable to the lamb that would reliably detect suckling episodes and report them wirelessly to a data collection point. We demonstrate that suckling is characterized by a rather simple and distinguished acceleration signature which makes it possible to detect the event with relatively simple techniques easily implementable within low-end microcontrollers. We propose an algorithm to this end and assess its performance on acceleration data obtained from animals in a farm environment. Our algorithm has been able to detect 95% of all (actual) suckling episodes with less that 10% false indications.

      PubDate: 2017-05-07T14:56:53Z
       
  • Honey authentication based on physicochemical parameters and phenolic
           compounds
    • Abstract: Publication date: 1 June 2017
      Source:Computers and Electronics in Agriculture, Volume 138
      Author(s): Mircea Oroian, Sorina Ropciuc
      The aim of this study is to assess the usefulness of physicochemical parameters (pH, water activity, free acidity, refraction index, Brix, moisture content and ash content), color parameters (L∗, a∗, b∗, chroma, hue angle and yellow index) and phenolics (quercetin, apigenin, myricetin, isorhamnetin, kaempherol, caffeic acid, chrysin, galangin, luteolin, p-coumaric acid, gallic acid and pinocembrin) in view of classifying honeys according to their botanical origin (acacia, tilia, sunflower, honeydew and polyfloral). Thus, the classification of honeys has been made using the principal component analysis (PCA), linear discriminant analysis (LDA) and artificial neural networks (ANN). The multilayer perceptron network with 2 hidden layers classified correctly 94.8% of the cross validated samples.

      PubDate: 2017-05-07T14:56:53Z
       
  • Nonlinear parametric modelling to study how soil properties affect crop
           yields and NDVI
    • Abstract: Publication date: 1 June 2017
      Source:Computers and Electronics in Agriculture, Volume 138
      Author(s): Rebecca Whetton, Yifan Zhao, Sameh Shaddad, Abdul M. Mouazen
      This paper explores the use of a novel nonlinear parametric modelling technique based on a Volterra Non-linear Regressive with eXogenous inputs (VNRX) method to quantify the individual, interaction and overall contributions of six soil properties on crop yield and normalised difference vegetation index (NDVI). The proposed technique has been applied on high sampling resolution data of soil total nitrogen (TN) in %, total carbon (TC) in %, potassium (K) in cmol kg−1, pH, phosphorous (P) in mgkg−1 and moisture content (MC) in %, collected with an on-line visible and near infrared (VIS-NIR) spectroscopy sensor from a 18ha field in Bedfordshire, UK over 2013 (wheat) and 2015 (spring barley) cropping seasons. The on-line soil data were first subjected to a raster analysis to produce a common 5m by 5m grid, before they were used as inputs into the VNRX model, whereas crop yield and NDVI represented system outputs. Results revealed that the largest contributions commonly observed for both yield and NDVI were from K, P and TC. The highest sum of the error reduction ratio (SERR) of 48.59% was calculated with the VNRX model for NDVI, which was in line with the highest correlation coefficient (r) of 0.71 found between measured and predicted NDVI. However, on-line measured soil properties led to larger contributions to early measured NDVI than to a late measurement in the growing season. The performance of the VNRX model was better for NDVI than for yield, which was attributed to the exclusion of the influence of crop diseases, appearing at late growing stages. It was recommended to adopt the VNRX method for quantifying the contribution of on-line collected soil properties to crop NDVI and yield. However, it is important for future work to include additional soil properties and to account for other factors affecting crop growth and yield, to improve the performance of the VNRX model.

      PubDate: 2017-05-07T14:56:53Z
       
  • Characterizing apple microstructure via directional statistical
           correlation functions
    • Abstract: Publication date: 1 June 2017
      Source:Computers and Electronics in Agriculture, Volume 138
      Author(s): A. Derossi, B. Nicolai, P. Verboven, C. Severini
      Our understanding of food is tightly related to the complex food microstructure. We introduce the use of statistical correlation functions to quantitatively describe the spatial distribution of cell and void phases of ‘Braeburn’ and ‘Kanzi’ apples. The lineal-path distribution function, L(r), and the two-point correlation function, S 2(r), were measured from bi-dimensional (2D) microtomographic images. While the void fraction of both apples cultivars was similar, 0.840% and 0.853%, the pores of ‘Braeburn’ apples were bigger in size. Pores with an extension of 25μm were found in percentage of 12.6% and 4.3% for ‘Braeburn’ and ‘Kanzi’ respectively. The cell phase of ‘Braeburn’ apples was described by larger clusters as a result of a greater degree of connectivity among the individual cells. ‘Kanzi’ apple tissue was structured by more separated cell clusters due to the presence of small pores with a greater spatial distribution. The clusters showed a good homogeneity in shape for both varieties while the voids of ‘Kanzi’ apples appeared more inhomogeneous and elongated in one of the dimensions. The obtained structural information were employed to model tissue structures of apples. We found that the cell phase could be modeled by overlapping disks having a mean radius of 58 and 53mm for ‘Braeburn’ and ‘Kanzi’ apples, respectively. In this way macroscopic properties of apple tissue could be estimated precisely.

      PubDate: 2017-05-07T14:56:53Z
       
  • Interoperable agro-meteorological observation and analysis platform for
           precision agriculture: A case study in citrus crop water requirement
           estimation
    • Abstract: Publication date: 1 June 2017
      Source:Computers and Electronics in Agriculture, Volume 138
      Author(s): Suryakant Sawant, Surya S. Durbha, Adinarayana Jagarlapudi
      Advances in Internet of Things (IoT) based sensing systems have improved capabilities to precisely monitor environmental conditions. Plants are sessile organisms and are affected by biotic and abiotic stresses caused due to surrounding environmental conditions such as soil water content, pest/disease infestation, and soil health. High-resolution sensing (Wireless Sensor Networks (WSN) Systems) of agro-meteorological parameters helps to solve critical issues about the crop-weather-soil continuum. Currently, many WSN systems are deployed all over the World for precision agriculture purposes. Although there have been many improvements in the communication aspects of the WSN's, the data dissemination and near real-time analysis components for taking dynamic decision, particularly in agriculture domain has not matured. The current WSN systems do not have a standardized way of data discovery, access, and sharing, which impedes the integration of data across various distributed sensor networks. This study addresses above issues through the adaptation of a framework based on Open Geospatial Consortium (OGC) standards for Sensor Web Enablement (SWE). For precision agriculture applications a cost-effective, standardized sensing system (hardware and software) has been developed, which includes functionalities such as sensors plug-n-play, remote monitoring, tools for crop water requirement estimation, pest, disease monitoring, and nutrient management. Also, the modeling techniques were integrated with the interoperable web-enabled sensing system for addressing water management problems of horticultural crops in semi-arid areas.

      PubDate: 2017-05-07T14:56:53Z
       
  • Embedded system for real-time monitoring of foraging behavior of grazing
           cattle using acoustic signals
    • Abstract: Publication date: 1 June 2017
      Source:Computers and Electronics in Agriculture, Volume 138
      Author(s): Nestor N. Deniz, José O. Chelotti, Julio R. Galli, Alejandra M. Planisich, Marcelo J. Larripa, H. Leonardo Rufiner, Leonardo L. Giovanini
      Estimating forage intake and monitoring behavior of grazing livestock are difficult tasks. Real-time detection and classification of events like chew, bite and chew-bite are necessary to estimate that information. It is well-known that acoustic monitoring is one of the best ways to characterize feeding behavior in ruminants. Although several methods have been developed to detect and classify events, their implementation is restricted to desktop computers, fact that confines their application to off-line analysis of a reduced number of animals. In this work, we present the design and implementation of an electronic system specifically developed for real-time monitoring of feeding patterns in dairy cows. The system is based on an embedded circuit to process the sound produced by the animal in order to detect, classify and quantify events of ruminant feeding behavior. The system implements an algorithm recently developed, which was adapted to be executed on a microcontroller-based electronic system. Only the results of sound analysis are stored in flash memory units. In addition to sound information, data from a GPS receiver is also stored, thus building a package of information. A microcontroller with power management technology, combined with a high-efficiency harvesting power supply and power management firmware, enables long operational time (more than five days of continuous operation). The system was evaluated using audio signals derived from the feeding activity of dairy cows that were acquired under normal operational conditions. The system correctly detected 92% of the events (i.e. considering them as possible events without making a classification). When the three types of events (i.e. chew, bite and chew-bite) were considered for classification, the recognition rate was about 78%. These results were obtained using reference labels provided by experts in ruminant ingestive behavior. The technology presented within this publication is protected under the international patent application PCT/IB2015/053721.

      PubDate: 2017-05-07T14:56:53Z
       
  • Daily suspended sediment concentration simulation using hydrological data
           of Pranhita River Basin, India
    • Abstract: Publication date: 1 June 2017
      Source:Computers and Electronics in Agriculture, Volume 138
      Author(s): Anurag Malik, Anil Kumar, Jamshid Piri
      Simulation of suspended sediment concentration (SSC) in a river is very important for planning and management of water resources. In this study, co-active neuro-fuzzy inference system (CANFIS), multi-layer perceptron neural network (MLPNN), multiple linear and non-linear regressions (MLR and MNLR), and sediment rating curve (SRC) techniques were applied for simulating the daily SSC at Tekra gauging site on Pranhita River, a major tributary of Godavari River basin, Andhra Pradesh, India. The daily data of discharge (m3/s) and SSC (g/l) from June 2000 to November 2003 were used for SSC simulation. The appropriate combination of input variables for CANFIS, MLPNN, MLR and MNLR models were decided using the gamma test (GT). The outcomes from CANFIS, MLPNN, MLR, MNLR and SRC models were compared to observed values of SSC on the basis of root mean squared error (RMSE), Pearson correlation coefficient (r) and coefficient of efficiency (CE). The results indicate the superiority of CANFIS model than the MLPNN, MLR, MNLR and SRC models in simulating daily SSC for the study location.

      PubDate: 2017-05-01T16:40:05Z
       
  • Evaluation of MODIS and Landsat multiband vegetation indices used for
           wheat yield estimation in irrigated Indus Basin
    • Abstract: Publication date: 1 June 2017
      Source:Computers and Electronics in Agriculture, Volume 138
      Author(s): Muhammad Usman Liaqat, Muhammad Jehanzeb Masud Cheema, Wenjiang Huang, Talha Mahmood, Muhammad Zaman, Muhammad Mohsin Khan
      Crop yield estimation has significant importance for policy makers to make timely dicisions on import/export of particular crop. Traditionally, in Pakistan crop yield estimation is being carried out by Village Master Sampling (VMS) that is laborious and time-consuming. Satellite imagery is also being used as an alternative to estimate vegetation health and yield. Various vegetation indices are being used for the purpose however, their efficiency to estimate yield has not been tested. In this study, a comparison was performed among various satellite-based vegetation indices e.g. Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI) Normalized Difference Vegetation Index (NDVI), Enhanced Vegetation Index (EVI), to evaluate most appropriate index that performs better in cropping area of irrigated Indus Basin (a complex basin with spatially heterogeneous land use). A stepwise regression based model was developed for remotely sensed crop (i.e. Wheat) using multi-band MODIS and Landsat 8 products based on Land use and Land cover map developed by Semi-Supervised Classification. The results revealed that SAVI showed a fairly acceptable association with reported yield data as compared to other indices. The correlation coefficient (R 2) was estimated at 0.60. Yield estimated by SAVI obtained from Landsat 8 showed good results with R2 and Pearson correlation (r), estimated at 0.74 and 0.88 as compared to SAVI obtained from MODIS with 0.63 and 0.79 respectively. The results support that SAVI vegetation indices is reliable for quick and efficient wheat area mapping under Pakistani’s farm conditions.

      PubDate: 2017-05-01T16:40:05Z
       
  • Toward more efficient model development for farming systems research
           – An integrative review
    • Abstract: Publication date: 1 June 2017
      Source:Computers and Electronics in Agriculture, Volume 138
      Author(s): E. Reinmuth, S. Dabbert
      Bio-economic simulation models are widely established in Farming Systems Research; they are used to investigate complex real-world phenomena in agricultural production. Such simulation models are largely designed and created by scientists from different disciplines who are not modeling experts. Thus, IT knowledge is required, but this area of expertise falls outside of most agricultural researchers’ background. IT knowledge is essential for the maintenance, development, and applicability of simulation models. Often, bio-economic simulation models require a fair amount of time to ensure basic functionality before specific research questions can be answered. Researchers who contribute to the creation of a bio-economic simulation model often spend the majority of their time ensuring basic model functionality. This integrative literature review provides a few basic rules that are intended to ensure more efficient model development. There is an increased need for support from IT personnel who are not researchers in their own field but who can increase the quality of such models and their reusability in different contexts.

      PubDate: 2017-05-01T16:40:05Z
       
  • Embedded vision detection of defective orange by fast adaptive lightness
           correction algorithm
    • Abstract: Publication date: 1 June 2017
      Source:Computers and Electronics in Agriculture, Volume 138
      Author(s): Dian Rong, Yibin Ying, Xiuqin Rao
      Automatic detection of defective fruit by computer vision system still faces challenge due to the uneven lightness distribution on the surface. A fast adaptive lightness correction algorithm implementation which is simpler and easier in real-time approach is proposed to overcome interferences from non-uniform reflectance intensity distribution on moving fruit surface and avoids error detection. The algorithm is tested by on-line and static defective orange images in the different lighting conditions. This study also compares other lightness correction algorithm implementations for defect detection. Recently, embedded vision systems are more and more popular because of low-cost, compact size and stability. A low-cost embedded vision system based on an industry gigabit ethernet camera and embedded Linux system in an arm processor with limited computing power compared with high-performance major PC is also originally developed to test and prove the performance of fast adaptive lightness correction algorithm using the most common surface defect of Navel orange in China. The time consumption of adaptive lightness correction algorithm of an image is below 6ms. The processing time of an orange image is below 30ms.
      Graphical abstract image

      PubDate: 2017-05-01T16:40:05Z
       
  • LSSA_CAU: An interactive 3d point clouds analysis software for body
           measurement of livestock with similar forms of cows or pigs
    • Abstract: Publication date: 1 June 2017
      Source:Computers and Electronics in Agriculture, Volume 138
      Author(s): Hao Guo, Xiaodong Ma, Qin Ma, Ke Wang, Wei Su, DeHai Zhu
      As increasing number of studies for shape measurement purposes in livestock farming by using consumer depth cameras, many software have been developed in order to measure livestock conformation. However, many of these softwares were designed only for specific livestock or body part of specific livestock with very limited body measurements. To be more flexible and general compared to the current software provided in the literature, an interactive software LSSA_CAU is developed to estimate body measurements of livestock based on 3d point clouds data. Livestock with similar forms of cows or pigs and standing with her head forward is assumed for designing algorithm used in LSSA_CAU. This software provides a set of tools for loading, rendering, segmenting, pose normalizing, measuring point clouds data of whole body surface of livestock in a semiautomatic manner. In order to validate the software, both synthetic and real world point clouds data of livestock were processed by using the LSSA_CAU. Our experiments show that the proposed software generalizes well across livestock species and supports customized body measurements. An updated LSSA_CAU version can be downloaded freely from https://github.com/LiveStockShapeAnalysis to livestock industry and research.

      PubDate: 2017-05-01T16:40:05Z
       
  • A cognitive architecture for automatic gardening
    • Abstract: Publication date: 1 June 2017
      Source:Computers and Electronics in Agriculture, Volume 138
      Author(s): Alejandro Agostini, Guillem Alenyà, Andreas Fischbach, Hanno Scharr, Florentin Wörgötter, Carme Torras
      In large industrial greenhouses, plants are usually treated following well established protocols for watering, nutrients, and shading/light. While this is practical for the automation of the process, it does not tap the full potential for optimal plant treatment. To more efficiently grow plants, specific treatments according to the plant individual needs should be applied. Experienced human gardeners are very good at treating plants individually. Unfortunately, hiring a crew of gardeners to carry out this task in large greenhouses is not cost effective. In this work we present a cognitive system that integrates artificial intelligence (AI) techniques for decision-making with robotics techniques for sensing and acting to autonomously treat plants using a real-robot platform. Artificial intelligence techniques are used to decide the amount of water and nutrients each plant needs according to the history of the plant. Robotic techniques for sensing measure plant attributes (e.g. leaves) from visual information using 3D model representations. These attributes are used by the AI system to make decisions about the treatment to apply. Acting techniques execute robot movements to supply the plants with the specified amount of water and nutrients.

      PubDate: 2017-05-01T16:40:05Z
       
  • Validation of an open source CFD code to simulate natural ventilation for
           agricultural buildings
    • Abstract: Publication date: 1 June 2017
      Source:Computers and Electronics in Agriculture, Volume 138
      Author(s): Se-Woon Hong, Vasileios Exadaktylos, In-Bok Lee, Thomas Amon, Ali Youssef, Tomas Norton, Daniel Berckmans
      To study natural ventilation for agricultural buildings, OpenFOAM, an open source CFD code, enables to develop tools that automate complex simulation procedures for nonprofessional CFD users. Prior to developing such tools, this paper investigates the accuracy of simulated air velocity in three test buildings according to different mesh sizes and turbulence models. The computed results, that were obtained using 13 different mesh sizes and 4 different k-ε turbulence models, were qualitatively and quantitatively compared to wind tunnel measurements by two validation metrics. The results showed that the smaller mesh size leads to slightly higher simulation accuracy, but an increase in the number of meshes above a certain level did not improve the simulation accuracy due to the problem of solution convergence. The mesh size larger than 0.25m at building walls was appropriate for the standard k-ε model and the RNG k-ε model, while a small mesh size of 0.125m was preferable for the low Re k-ε model. The simulation results, especially obtained from the RNG k-ε model, were highly influenced by whether the solution converged showing the relative error of on average 6.4% higher in the oscillatory solutions, while those from the low Re k-ε model were more dependent on the mesh size.

      PubDate: 2017-05-01T16:40:05Z
       
  • Inside Front Cover - Editorial Board Page/Cover image legend if applicable
    • Abstract: Publication date: May 2017
      Source:Computers and Electronics in Agriculture, Volume 137


      PubDate: 2017-05-01T16:40:05Z
       
  • Computer vision detection of surface defect on oranges by means of a
           sliding comparison window local segmentation algorithm
    • Abstract: Publication date: May 2017
      Source:Computers and Electronics in Agriculture, Volume 137
      Author(s): Dian Rong, Xiuqin Rao, Yibin Ying
      Automatic detection of defective oranges by computer vision system is not easy because of the uneven lightness distribution on the surface of oranges. It means that the methods onlydirectly using global segmentation provide unsatisfactory results when orange images present faint defect characters or inhomogeneous surface. The contrast between sound and defective regions can be used to produce more accurate segmentation results, which is more capable of detecting pixels lying around the defect boundary on orange surface based on the local segmentation method. In this paper, we study and propose a sliding comparison window local segmentation algorithm and also presents the detailed image processing procedure including removal of background pixels, image binarization using local segmentation, image subtraction, image morphological modification, removal of stem end pixels for detecting surface defect in an orange gray-level image. This method is an original contribution that allows successful segmentation of various types of surface defects (e.g., insect injury, wind scarring, thrips scarring, scale infestation, canker spot, dehiscent fruit, copper burn, phytotoxicity).The image segmentation algorithm was tested with 1191 samples of oranges. The proposed algorithm was able to correctly detect 97% of the defective orange. Future work will be focused on whole surface and fast on-line inspection.
      Graphical abstract image

      PubDate: 2017-05-01T16:40:05Z
       
  • Appropriate data visualisation is key to Precision Livestock Farming
           acceptance
    • Abstract: Publication date: 1 June 2017
      Source:Computers and Electronics in Agriculture, Volume 138
      Author(s): T. Van Hertem, L. Rooijakkers, D. Berckmans, A. Peña Fernández, T. Norton, D. Berckmans, E. Vranken
      Most farmers do not have the skills and time to utilize new Precision Livestock Farming (PLF) technologies effectively. It is time consuming to combine and analyse the data coming from sensors in different formats and frequencies. As part of the EU-PLF project, the authors have developed a visualisation tool to bring together and analyse the scattered data, and present them in an easy to use format to the end user. The correct use of these data might improve animal welfare, and reduce emissions through the application of PLF techniques. Data were collected at five broiler farms and ten pig farms across Europe. At the farms, a number of variables were automatically measured including climate data, production data, environmental data, and data on animal behaviour coming from cameras and microphones. Simultaneously, the welfare of the animals was assessed by trained assessors on a regular basis by using the standardized Welfare Quality protocol. All data were gathered, stored and processed on a daily basis, and visualised on a web-based tool. End-users of the tool were trained on how to interpret the available information on the visualisation tool. This paper presents the development of this PLF data visualisation tool. The farmer’s use of this tool and the early warning capabilities are described by six case studies. The selected farmers participated actively in evaluating its usefulness, resulting in a web-based visualisation tool that is practical and useful for both the farmer and other stakeholders (e.g. vets, advisors, researchers, etc.).

      PubDate: 2017-04-24T16:25:04Z
       
  • Factors influencing the adoption of Farm Management Information Systems
           (FMIS) by Brazilian citrus farmers
    • Abstract: Publication date: 1 June 2017
      Source:Computers and Electronics in Agriculture, Volume 138
      Author(s): Marcelo José Carrer, Hildo Meirelles de Souza Filho, Mário Otávio Batalha
      This paper examined the determining factors in decisions of citrus farmers on adoption of computers and Farm Management Information Systems (FMIS). Primary data were collected from a random representative sample of 98 citrus farmers from the state of São Paulo, Brazil. The data was analyzed using logit and count data (Poisson regression) models, which enabled testing hypotheses on the effect of ten variables on the decisions of farmers. The results of the logit model showed that education and production size had a positive and statistically significant effect on the adoption of computers, while experience had a negative effect. The adoption and intensity of use of FMIS were influenced positively by overconfidence in management, production size and use of technical assistance. Contract adjustments and farmers’ experience have a negative impact on the adoption of FMIS. The results confirmed the main hypotheses and can contribute to the development of new strategies for greater diffusion of FMIS in Brazilian citrus industry, which is relevant to increasing farm efficiency.

      PubDate: 2017-04-24T16:25:04Z
       
  • Differentiation of deciduous-calyx and persistent-calyx pears using
           hyperspectral reflectance imaging and multivariate analysis
    • Abstract: Publication date: May 2017
      Source:Computers and Electronics in Agriculture, Volume 137
      Author(s): Haijiang Hu, Leiqing Pan, Ke Sun, Sicong Tu, Ye Sun, Yingying Wei, Kang Tu
      Deciduous-calyx pears of Korla fragrant pear (Pyrus sinkiangensis Yu) have a significant economic value in Xinjiang Uygur Autonomous Region, China. This study developed a non-destructive method based on hyperspectral imaging using a combination of existing analytical techniques to differentiate the deciduous-calyx pear (DCF) and persistent-calyx pear (PCF). The degrees of circularity of DCP and PCP were extracted according to its morphological characteristic; similarly, the reflectance spectra of DCP and PCP were obtained by hyperspectral imaging technology. Successive projections algorithm (SPA) combined with support vector machine (SVM) established a classification model. The DCF and PCF could be differentiated by SPA-SVM model with accuracy of 93.3% and 96.7% respectively. Our findings suggest that hyperspectral imaging can be applied to non-destructively differentiate pears, and meet the packaging standards.

      PubDate: 2017-04-17T03:00:35Z
       
  • Remote monitoring of the Bactrocera oleae (Gmelin) (Diptera: Tephritidae)
           population using an automated McPhail trap
    • Abstract: Publication date: May 2017
      Source:Computers and Electronics in Agriculture, Volume 137
      Author(s): Lefteris Doitsidis, George N. Fouskitakis, Kyriaki N. Varikou, Iraklis I. Rigakis, Savvas A. Chatzichristofis, Androniki K. Papafilippaki, Athanasia E. Birouraki
      Remote pest population monitoring is of major importance within the context of precision agriculture. Information acquired from the field has been proved essential for proper decision making and pest management against various cultivation threats. Bactrocera oleae (Gmelin) consists the major pest for olive orchards. The key factor for a successful pest management is the on-time, accurate, valid and unbiased pest population monitoring. In this paper, a novel automated McPhail e-trap is presented. It is based on a custom electronic design capable of capturing pictures from its interior thus providing real-time information from the field. The pictures are easily accessible from a tailor-made web-based system which provides to the expert entomologists the capability to remotely assess the potential threat at any time and rate, thus neglecting the need for visiting and collecting data on site. The web-based system also supports automatic insect counting. The proposed system has been tested in real in-field conditions for an extensive period of time. The results of the study indicated its robustness and reliability. The attractiveness of the automated trap is comparable with that of the traditional reference glass-type McPhail trap, while the automatic insect counting technique offers an accuracy of almost 75%.

      PubDate: 2017-04-10T12:29:15Z
       
  • Review of the use of air-coupled ultrasonic technologies for
           nondestructive testing of wood and wood products
    • Abstract: Publication date: May 2017
      Source:Computers and Electronics in Agriculture, Volume 137
      Author(s): Yiming Fang, Lujun Lin, Hailin Feng, Zhixiong Lu, Grant W. Emms
      Air-coupled ultrasonic (ACU) is a contactless ultrasonic measurement method which has become increasingly popular for material characterization. This is due to a growing number of advanced materials which cannot be contaminated during the testing processes by coupling agents utilized in conventional ultrasonic testing. This paper provides a review of the applications of ACU to wood and wood products. The ACU fundamentals, including principles, working modes and commercial transducers used for this purpose, is briefly described. The emphasis of this paper is on approaches of inspection and characterization. The applications of ACU to wood characterization with reference to wood quality aspects are summarized. Correlations between the ACU parameters (i. e. amplitude, velocity, and spectrum) and the wood properties (i.e. density, moisture content, strength, and stiffness) as well as the wood defects (i. e. knots, cracks, decay, insect damage, and delamination) are dealt with in detail. Finally, a discussion of apparent future research directions completes this review.

      PubDate: 2017-04-10T12:29:15Z
       
  • A computer vision system for early stage grape yield estimation based on
           shoot detection
    • Abstract: Publication date: May 2017
      Source:Computers and Electronics in Agriculture, Volume 137
      Author(s): Scarlett Liu, Steve Cossell, Julie Tang, Gregory Dunn, Mark Whitty
      Counting grapevine shoots early in the growing season is critical for adjusting management practices but is challenging to automate due to a range of environmental factors. This paper proposes a completely automatic system for grapevine yield estimation, comprised of robust shoot detection and yield estimation based on shoot counts produced from videos. Experiments were conducted on four vine blocks across two cultivars and trellis systems over two seasons. A novel shoot detection framework is presented, including image processing, feature extraction, unsupervised feature selection and unsupervised learning as a final classification step. Then a procedure for converting shoot counts from videos to yield estimates is introduced. The shoot detection framework accuracy was calculated to be 86.83% with an F1-score of 0.90 across the four experimental blocks. This was shown to be robust in a range of lighting conditions in a commercial vineyard. The absolute predicted yield estimation error of the system when applied to four blocks over two consecutive years ranged from 1.18% to 36.02% when the videos were filmed around E-L stage 9. The developed system has an advantage over traditional PCD mapping techniques in that yield variation maps can be obtained earlier in the season, thereby allowing farmers to adjust their management practices for improved outputs. The unsupervised feature selection algorithm combined with unsupervised learning removed the requirement for any prior training or labeling, greatly enhancing the applicability of the overall framework and allows full automation of shoot mapping on a large scale in vineyards.

      PubDate: 2017-04-10T12:29:15Z
       
  • Smart frost control in greenhouses by neural networks models
    • Abstract: Publication date: May 2017
      Source:Computers and Electronics in Agriculture, Volume 137
      Author(s): Alejandro Castañeda-Miranda, Víctor M. Castaño
      Thermal comfort in greenhouses is a key fact to enhance productivity, due to the excess demand of energy for heating, ventilation and agroclimatic conditioning. Frost, in particular, represents a serious technological challenge if the crop sustainability is to be ensured. A Multi-Layer Perceptron artificial neural network, trained by a Levenberg-Marquardt backpropagation algorithm was designed and implemented for the smart frost control in greenhouses in the central region of Mexico, with the outside air temperature, outside air relative humidity, wind speed, global solar radiation flux, and inside air relative humidity as the input variables. The results showed a 95% confidence temperature prediction, with a coefficient of determination of 0.9549 and 0.9590, for summer and winter, respectively.

      PubDate: 2017-04-10T12:29:15Z
       
  • Hybrid centrifugal spreading model to study the fertiliser spatial
           distribution and its assessment using the transverse coefficient of
           variation
    • Abstract: Publication date: May 2017
      Source:Computers and Electronics in Agriculture, Volume 137
      Author(s): S. Villette, E. Piron, D. Miclet
      Studying centrifugal spreading by carrying out field or in-door experiments using fertiliser collection trays is tedious and labour intensive. This is particularly true when several implementation methods need to be compared, numerous replications are required or fertiliser sample characterisation is required. To circumvent cumbersome experiments, an alternative approach consists in performing in silico studies. In order to reach this objective, a hybrid centrifugal spreading model is designed by combining theoretical fertiliser motion equations with statistical information. The use of experimental measurements to characterise fertiliser properties, outlet velocity, angular mass flow distribution and spread pattern deposition, ensure a realistic calibration of the model. Based on this model, static spread patterns and transverse distributions are computed for a virtual twin-disc spreader. The number of fertiliser granules used to compute a spread pattern is deduced from the target application rate while the granule properties and their motion parameters are randomly selected from pre-established statistical distributions. This Monte Carlo process reproduces the random variability of fertiliser spread pattern depositions. Using this model, simulations demonstrate the mean and standard deviation of CV value decrease with the application rate. The CV mean value also decreases with the collection tray surface, while the standard deviation decreases with the collection tray length. Mathematical relationships are deduced from simulation results to express the mean and standard deviation of the CV as functions of the application rate and collection tray surface or length. The simulation model is also used to compare spreader test methods and study the influence of some fertiliser particles properties on the transverse distribution.

      PubDate: 2017-04-10T12:29:15Z
       
  • Detection of Silybum marianum infection with Microbotryum silybum using
           VNIR field spectroscopy
    • Abstract: Publication date: May 2017
      Source:Computers and Electronics in Agriculture, Volume 137
      Author(s): X.E. Pantazi, A.A. Tamouridou, T.K. Alexandridis, A.L. Lagopodi, G. Kontouris, D. Moshou
      Microbotryum silybum is a smut fungus infecting Silybum marianum (milk thistle) weed and is currently investigated as a means for its biological control. Although the fungus' detection is important for the evaluation of biological control effectiveness and decision making, in-situ diagnosis is not always possible. The presented approach describes the identification of systemically infected S. marianum plants by using field spectroscopy and hierarchical self-organizing maps. An experimental field that contained both healthy and artificially inoculated S. marianum plants was used to acquire leaf spectra using a handheld visible and near-infrared spectrometer (310–1100nm). Three supervised hierarchical self-organizing models, including Supervised Kohonen Network (SKN), Counter propagation Artificial Neural Network (CP-ANN) and XY-Fusion network (XY-F) were utilized for the identification of the systemically infected S. marianum plants. As input features to the classifiers, the pre-processed spectral signatures were used. The pre-processing of the spectra included normalisation, second derivative and principal component extraction. The systemically infected S. marianum identification rates using SKN and CP-ANN reached high overall accuracy (up to 90%) and even higher using the XY-F (95.16%). The results demonstrate the potential for a high accuracy identification of systemically infected S. marianum plants during vegetative growth, with the assistance of hierarchical self-organizing maps.

      PubDate: 2017-04-10T12:29:15Z
       
  • An intelligent integrated control of hybrid hot air-infrared dryer based
           on fuzzy logic and computer vision system
    • Abstract: Publication date: May 2017
      Source:Computers and Electronics in Agriculture, Volume 137
      Author(s): Mohammad Hossein Nadian, Mohammad Hossein Abbaspour-Fard, Alex Martynenko, Mahmood Reza Golzarian
      In this study, an intelligent fuzzy-machine vision control system (FMCS) was developed to control the operating variables throughout a hybrid hot air-infrared drying process. The total discoloration and the shrinkage of thin layer kiwifruit slices were monitored in real time using a computer vision system (CVS). These values along with calculated energy consumption obtained from preliminary experiments, were fed into a genetic algorithm (GA) framework to optimize a fuzzy logic control system. The performance of the fuzzy controller was evaluated for kiwifruit drying using a laboratory-scale hot air-infrared dryer. The results indicated that the hybrid drying could significantly reduce the drying load/time compared with the hot air drying. The FMCS application showed a good balance between energy consumption (0.158kWh) and product quality (ΔE=2.32).

      PubDate: 2017-04-10T12:29:15Z
       
  • Quantification of simulated cow urine puddle areas using a thermal IR
           camera
    • Abstract: Publication date: May 2017
      Source:Computers and Electronics in Agriculture, Volume 137
      Author(s): Dennis J.W. Snoek, Jan Willem Hofstee, Arjen W. van Dueren den Hollander, Roel E. Vernooij, Nico W.M. Ogink, Peter W.G. Groot Koerkamp
      In Europe, National Emission Ceilings (NEC) have been set to regulate the emissions of harmful gases, like ammonia (NH3). From NH3 emission models and a sensitivity analysis, it is known that one of the major variables that determines NH3 emission from dairy cow houses is the urine puddle area on the floor. However, puddle area data from cow houses is scarce. This is caused by the lack of appropriate measurement methods and the challenging measurement circumstances in the houses. In a preliminary study inside commercial dairy cow houses, an IR camera was successfully tested to distinguish a fresh urine puddle from its background to determine a puddle’s area. The objective of this study was to further develop, improve and validate the IR camera method to determine the area of a warm fluid layer with a measurement uncertainty of <0.1m2. In a laboratory set-up, 90 artificial, warm, blue puddles were created, and both an IR and a colour image of each puddle was taken within 5s after puddle application. For the colour images, three annotators determined the ground truth puddle areas ( A p , GT ). For the IR images, an adaptive IR threshold algorithm was developed, based on the mean background temperature and the standard deviation of all temperature values in an image. This IR algorithm was able to automatically determine the IR puddle area ( A p , IR ) in each IR image. The agreement between the two methods was assessed. The A p , IR underestimated the A p , GT by 2.53% for which is compensated by the model A p , GT = 1.0253 · A p , IR . This regression model intercepted with zero and the noise was only 0.0651m2, so the measurement uncertainty was <0.1m2. In addition, the A p , IR was not affected by the mean background temperature.

      PubDate: 2017-04-03T02:05:01Z
       
  • A pragmatic, automated approach for retroactive calibration of soil
           moisture sensors using a two-step, soil-specific correction
    • Abstract: Publication date: May 2017
      Source:Computers and Electronics in Agriculture, Volume 137
      Author(s): Caley K. Gasch, David J. Brown, Erin S. Brooks, Matt Yourek, Matteo Poggio, Douglas R. Cobos, Colin S. Campbell
      Soil moisture sensors are increasingly deployed in sensor networks for both agronomic research and precision agriculture. Soil-specific calibration improves the accuracy of soil water content sensors, but laboratory calibration of individual sensors is not practical for networks installed across heterogeneous settings. Using daily water content readings collected from a sensor network (42 locations×5 depths=210 sensors) installed at the Cook Agronomy Farm (CAF) near Pullman, Washington, we developed an automated calibration approach that can be applied to individual sensors after installation. As a first step, we converted sensor-based estimates of apparent dielectric permittivity to volumetric water content using three different calibration equations (Topp equation, CAF laboratory calibration, and the complex refractive index model, or CRIM). In a second, “re-calibration” step, we used two pedotransfer functions based upon particle size fractions and/or bulk density to estimate water content at wilting point, field capacity, and saturation at each sensor insertion point. Using an automated routine, we extracted the same three reference points, when present, from each sensor’s record, and then bias-corrected and re-scaled the sensor data to match the estimated reference points. Based on validation with field-collected cores, the Topp equation provided the most accurate calibration with an RMSE of 0.074m3 m−3, but automated re-calibration with a local pedotransfer function outperformed any of the calibrations alone, yielding a network-wide RMSE of 0.055m3 m−3. The initial calibration equation used in the first step was irrelevant when the re-calibration was applied. After correcting for the reference core measurement error of 0.026m3 m−3 used for calibration and validation, the error of the sensors alone (RMSEadj ) was computed as 0.049m3 m−3. Sixty-five percent of individual sensors exhibited re-calibration errors less than or equal to the network RMSEadj . The incorporation of soil physical information at sensor installation sites, applied retroactively via an automated routine to in situ soil water content sensors, substantially improved network sensor accuracy.

      PubDate: 2017-04-03T02:05:01Z
       
  • Potato feature prediction based on machine vision and 3D model rebuilding
    • Abstract: Publication date: May 2017
      Source:Computers and Electronics in Agriculture, Volume 137
      Author(s): Qinghua Su, Naoshi Kondo, Minzan Li, Hong Sun, Dimas Firmanda Al Riza
      Machine vision based on color, multispectral, and hyperspectral cameras to develop potato quality grading can be used to predict length, width, and mass, as well as defects on the interior and exterior of a sample. However, the images obtained by these cameras are limited by two-dimensional shape information, including width, length, and boundary. Other vital elements of appearance data related to potato mass and quality, including thickness, volume, and surface gradient changes are difficult to detect due to slight surface color differences and device limitations. In this study, we recorded the depth images of 110 potatoes using a depth camera, including samples with uniform shapes or with deformations (e.g., bumps and divots). A novel method was developed for estimating potato mass and shape information and three-dimensional models were built utilizing a new image processing algorithm for depth images. Other features, including length, width, thickness, and volume were also calculated as mass prediction related factors. Experimental results indicate that the proposed models accurately predict potato length, width, and thickness; the mean absolute errors for these predictions were 2.3mm, 2.1mm, and 2.4mm, respectively, while the mean percentage errors were 2.5%, 3.5%, and 4.4%. Mass prediction based on a 3D volume model for both normal and deformed potato samples proved to be more accurate compared to models based on area calculation. Thus 93% of samples were graded by the correct size group using the volume density model while only 73% were graded correctly using the area density. This depth image processing is an effective potential method for future non-destructive post-harvesting grading, especially for products where size, shape, and surface condition are important factors.

      PubDate: 2017-04-03T02:05:01Z
       
  • Vision-based pest detection based on SVM classification method
    • Abstract: Publication date: May 2017
      Source:Computers and Electronics in Agriculture, Volume 137
      Author(s): M.A. Ebrahimi, M.H. Khoshtaghaza, S. Minaei, B. Jamshidi
      Automatic pest detection is a useful method for greenhouse monitoring against pest attacks. One of the more harmful pests that threaten strawberry greenhouses is thrips (Thysanoptera). Therefore, the main objective of this study is to detect of thrips on the crop canopy images using SVM classification method. A new image processing technique was utilized to detect parasites that may be found on strawberry plants. SVM method with difference kernel function was used for classification of parasites and detection of thrips. The ratio of major diameter to minor diameter as region index as well as Hue, Saturation and Intensify as color indexes were utilized to design the SVM structure. Also, mean square error (MSE), root of mean square error (RMSE), mean absolute error (MAE) and mean percent error (MPE) were used for evaluation of the classification. Results show that using SVM method with region index and intensify as color index make the best classification with mean percent error of less than 2.25%.

      PubDate: 2017-04-03T02:05:01Z
       
  • Phenotypic identification of plum varieties (Prunus domestica L.) by
           endocarps morpho-colorimetric and textural descriptors
    • Abstract: Publication date: 15 April 2017
      Source:Computers and Electronics in Agriculture, Volume 136
      Author(s): Marco Sarigu, Oscar Grillo, Marisol Lo Bianco, Mariano Ucchesu, Guy d'Hallewin, Maria Cecilia Loi, Gianfranco Venora, Gianluigi Bacchetta
      The identification of plum varieties is generally done on the base of distinctive plant traits such as shape, size, and fruit drupe color identified during the variety registration, following official descriptors. In this paper, image analysis techniques were applied to study endocarps variability of 23 Prunus domestica cultivars from Sardinia. Digital images were acquired and analysed using a macro specifically developed to measure morpho-colorimetric endocarps features. The data were later statistically processed applying the stepwise Linear Discriminant Analysis (LDA) to implement a statistical classifier able to classify each variety and identify plausible synonymy groups. The present study represent the first attempt to investigate the morphology and morphometry of plum endocarps in order to characterize the whole Sardinian plum agrobiodiversity. It is also the evidence of the usefulness of image analysis techniques in taxonomic investigations too, as well as for the conservation and enhancement of traditional plums for consumer satisfaction.

      PubDate: 2017-04-03T02:05:01Z
       
  • In situ measurements and simulation of oxygen diffusion and heat transfer
           in maize silage relative to the silo surface
    • Abstract: Publication date: May 2017
      Source:Computers and Electronics in Agriculture, Volume 137
      Author(s): Y. Sun, M. Li, H. Zhou, G. Shan, Q. Cheng, K.H. Jungbluth, W. Buescher, C. Maack, A. Lipski, Z. Wang, Y. Fan
      Aerobic deterioration is a major concern for silage production and quality change in unloading phase. To simulate silage aerobic deterioration relative to an exposure surface of bunker silo, a partial differential equation system model including oxygen (O2) concentration, silage temperature (Tsi) rise and microbial activity was presented. There is still a need to assess the predictability of the developed model at different bulk densities (BDs). For this study, the Clark oxygen electrodes (COE) was employed for the in situ simultaneous measurements of O2 and Tsi within maize silage samples, which were packed into twelve barrels (i.d.: 35.7cm, length: 60cm, vol. 60L) at three BD levels (low: 520–550kgm−3; medium: 660–730kgm−3; high: 860–950kgm−3). To assure the COE to be insensitive to CO2, a cross calibration for O2 concentrations (0–20% vol.) was made at 15% vol. of CO2 in advance of performing the experiment. For each barrel, two of the COEs were installed at 10cm and 40cm behind the exposure surface, respectively. The model was computed taking the in situ measurements of O2 and Tsi to be targets. Our study showed general well-agreements between the model simulations and the in situ measurements of O2 and Tsi for all BD levels. Some uncertainties and relevant reasons were also addressed. Based on these results, we concluded that the model has sufficient ability to predict aerobic deterioration in silage for bunker silos being unloaded.

      PubDate: 2017-03-27T01:55:55Z
       
  • Automatic fruit count on coffee branches using computer vision
    • Abstract: Publication date: May 2017
      Source:Computers and Electronics in Agriculture, Volume 137
      Author(s): P.J. Ramos, F.A. Prieto, E.C. Montoya, C.E. Oliveros
      In this article, a non-destructive method is proposed to count the number of fruits on a coffee branch by using information from digital images of a single side of the branch and its growing fruits. In order to do this, 1018 coffee branches at different ripening stages. They had different numbers of fruits, harvest dates, were of different varieties, and were at different stages of coffee tree’s life. A Machine Vision System (MVS) was constructed, which was capable of counting and identifying harvestable and not harvestable fruits in a set of images corresponding to a specific coffee branch was constructed. This MVS consists of an image acquisition system, based on mobile devices (it does not require to control of the environmental conditions), and an image processing algorithm to classify and detect each one of the fruits in the acquired images. After obtaining information regarding the number of fruits identified by the MVS, linear estimation models were constructed between the detected fruits automatically and the ones observed on the coffee branch. These models were calculated for fruits in three categories: harvestable, not harvestable, and fruits whose maturation stage were disregarded. These models link the fruits that are counted automatically to the ones actually observed with an R 2 higher than 0.93 one-to-one. Not only is the MVS used to estimate the number of fruits on the branch but also to estimate their maturation percentage and weight. The MVS was validated in four Variedad Castillo® coffee plots, in different stages of development and with different densities. We found that MVS neither overestimates nor underestimates the number of fruits and that it shows a correlation higher than 0.90 at early stages of crop development, when tree fruits are still not harvestable. The information obtained in this research will spawn a new generation of tools for coffee growers to use. It is an efficient, non-destructive, and low-cost method which offers useful information for them to plan agricultural work and obtain economic benefits from the correct administration of resources.

      PubDate: 2017-03-27T01:55:55Z
       
  • Hyperspectral data mining to identify relevant canopy spectral features
           for estimating durum wheat growth, nitrogen status, and grain yield
    • Abstract: Publication date: 15 April 2017
      Source:Computers and Electronics in Agriculture, Volume 136
      Author(s): K.R. Thorp, G. Wang, K.F. Bronson, M. Badaruddin, J. Mon
      While hyperspectral sensors describe plant canopy reflectance in greater detail than multispectral sensors, they also suffer from issues with data redundancy and spectral autocorrelation. Data mining techniques that extract relevant spectral features from hyperspectral data will aid the development of novel sensors for plant trait estimation. The objectives of this research were to (1) compare broad-band reflectance, narrow-band reflectance, and spectral derivatives for estimation of durum wheat traits in the field and (2) develop a genetic algorithm to identify the most relevant spectral features for durum wheat trait estimation. Experiments at Maricopa, Arizona during the winters of 2010–2011 and 2011–2012 tested six durum wheat cultivars with six split-applied nitrogen (N) fertilization rates. Durum wheat traits, including leaf area index, canopy dry weight, and plant N content, were measured from destructive biomass samples on four occassions in each growing season. Grain yield and grain N content were also measured. Canopy spectral reflectance data in 701 narrow wavebands from 350nm to 1050nm were collected weekly using a field spectroradiometer. First- and second-order spectral derivatives were calculated using Savitzky-Golay filtering. The narrow-band data were also used to estimate reflectance in broad wavebands, as typically collected by two commercial multispectral instruments. Partial least squares regression (PLSR) compared the ability of each spectral data set to estimate each measured durum wheat trait. A genetic algorithm was developed to mine narrow-band canopy reflectance and spectral derivative data for spectral features that improved estimates of durum wheat traits. Multispectral data in 4 broad bands estimated leaf area index, canopy dry weight, and plant N content with root mean squared errors of cross validation (RMSECV) between 33.0% and 67.6%, while hyperspectral data in 701 narrow bands reduced RMSECV to values between 19.3% and 36.3%. Use of the genetic algorithm to identify less than 25 relevant spectral features further reduced RMSECV to values between 15.1% and 30.7%. Grain yield was optimally estimated from canopy spectral measurements between 110 and 130days after planting with RMSECV less than 7.6% using the genetic algorithm approach. The timing corresponded to anthesis and early grain fill when presence of wheat heads likely affected canopy spectral reflectance. By using a genetic algorithm to mine hyperspectral reflectance and spectral derivative data, durum wheat traits were optimally estimated from a subset of relevant canopy spectral features.

      PubDate: 2017-03-08T21:41:58Z
       
  • Analysis of the effects of package design on the rate and uniformity of
           cooling of stacked pomegranates: Numerical and experimental studies
    • Abstract: Publication date: 15 April 2017
      Source:Computers and Electronics in Agriculture, Volume 136
      Author(s): A. Ambaw, Matia Mukama, U.L. Opara
      Computational fluid dynamics (CFD) model was developed, validated and used to analyse cooling characteristics of two different package designs (CT1 and CT2) used for postharvest handling of pomegranate fruit. The model incorporated geometries of fruits, packaging box, tray and plastic liner. Thin layer of plastic material with conservative interface heat flux was used to model liners. The accuracy of the model to predict airflow and temperature distributions were validated against experimental data. The model predicted airflow through the stacks and cooling rates within experimental error. Stack design markedly affected the airflow profile, rate and uniformity of cooling. The cooling rate of the two package designs differed by 30% and plastic lining increased the average 7/8th cooling times from 4.0 and 2.5h to 9.5 and 8.0h for the CT1 and CT2 stacks, respectively. Profile of high and low temperature regions depended considerably on packaging box design.

      PubDate: 2017-03-08T21:41:58Z
       
 
 
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