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ENGINEERING (1197 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: 14)
Abstract and Applied Analysis     Open Access   (Followers: 3)
Aceh International Journal of Science and Technology     Open Access   (Followers: 2)
ACS Nano     Full-text available via subscription   (Followers: 207)
Acta Geotechnica     Hybrid Journal   (Followers: 6)
Acta Metallurgica Sinica (English Letters)     Hybrid Journal   (Followers: 5)
Acta Polytechnica : Journal of Advanced Engineering     Open Access   (Followers: 1)
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: 4)
Advanced Science, Engineering and Medicine     Partially Free   (Followers: 6)
Advanced Synthesis & Catalysis     Hybrid Journal   (Followers: 17)
Advances in Artificial Neural Systems     Open Access   (Followers: 3)
Advances in Calculus of Variations     Hybrid Journal   (Followers: 2)
Advances in Catalysis     Full-text available via subscription   (Followers: 5)
Advances in Complex Systems     Hybrid Journal   (Followers: 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: 18)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 22)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 7)
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: 13)
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: 34)
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  
AMB Express     Open Access   (Followers: 1)
American Journal of Applied Sciences     Open Access   (Followers: 28)
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: 5)
Applied Catalysis B: Environmental     Hybrid Journal   (Followers: 6)
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: 8)
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Physics Research     Open Access   (Followers: 4)
Applied Sciences     Open Access   (Followers: 3)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4)
Arabian Journal for Science and Engineering     Hybrid Journal   (Followers: 5)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Archives of Foundry Engineering     Open Access  
Archives of Thermodynamics     Open Access   (Followers: 8)
Arkiv för Matematik     Hybrid Journal   (Followers: 1)
ASEE Prism     Full-text available via subscription   (Followers: 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: 9)
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: 24)
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: 8)
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: 4)
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: 14)
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: 4)
Catalysis Communications     Hybrid Journal   (Followers: 6)
Catalysis Letters     Hybrid Journal   (Followers: 3)
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: 4)
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: 10)
CIRP Journal of Manufacturing Science and Technology     Full-text available via subscription   (Followers: 13)
City, Culture and Society     Hybrid Journal   (Followers: 20)
Clay Minerals     Full-text available via subscription   (Followers: 9)
Clean Air Journal     Full-text available via subscription   (Followers: 2)
Coal Science and Technology     Full-text available via subscription   (Followers: 4)
Coastal Engineering     Hybrid Journal   (Followers: 10)
Coastal Engineering Journal     Hybrid Journal   (Followers: 3)
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: 5)
Composite Structures     Hybrid Journal   (Followers: 241)
Composites Part A : Applied Science and Manufacturing     Hybrid Journal   (Followers: 174)
Composites Part B : Engineering     Hybrid Journal   (Followers: 215)
Composites Science and Technology     Hybrid Journal   (Followers: 160)
Comptes Rendus Mécanique     Full-text available via subscription   (Followers: 2)
Computation     Open Access  
Computational Geosciences     Hybrid Journal   (Followers: 12)
Computational Optimization and Applications     Hybrid Journal   (Followers: 7)
Computational Science and Discovery     Full-text available via subscription   (Followers: 2)
Computer Applications in Engineering Education     Hybrid Journal   (Followers: 6)
Computer Science and Engineering     Open Access   (Followers: 17)
Computers & Geosciences     Hybrid Journal   (Followers: 25)
Computers & Mathematics with Applications     Full-text available via subscription   (Followers: 5)
Computers and Electronics in Agriculture     Hybrid Journal   (Followers: 4)
Computers and Geotechnics     Hybrid Journal   (Followers: 8)
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: 7)
Control Engineering Practice     Hybrid Journal   (Followers: 40)
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  
Current Applied Physics     Full-text available via subscription   (Followers: 4)

        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  [3043 journals]
  • 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
  • Development of an early detection system for lameness of broilers using
           computer vision
    • Abstract: Publication date: 15 April 2017
      Source:Computers and Electronics in Agriculture, Volume 136
      Author(s): A. Aydin
      Lameness is one of the most important causes of poor welfare in poultry. Previous studies have documented approximately 30% of the chickens were seriously lame. In this research, a novel technique was developed for early detection of lameness in broilers. For this purpose, broiler chickens with five different predefined gait scores were continuously monitored by a digital camera as they walked throughout a test corridor. Then, image analysis algorithm was applied to detect some feature variables (speed, step frequency, step length and the lateral body oscillation) of broilers. Afterwards, a correlation test was performed to define the coefficient of correlation between the feature variables (step frequency, step length, speed and LBO) obtained by the proposed algorithm and the gait score levels of the birds, which respectively resulted in r=0.831, 0.882 (p<0.05), 0.844, 0.861. Furthermore, each feature variable was investigated to find statistical differences between gait scores (as a measure of lameness) of broilers. It was performed to assess the effects of gait score on speed, step length, step frequency and lateral body oscillations of the broilers. The results showed that all investigated feature variables were efficacious to detect lameness in broilers starting from GS3. Since correlations were found between the feature variables (step frequency, step length, speed and LBO) obtained by the proposed algorithm and the gait score levels of the birds on the one hand and the statistical differences between gait score levels of broilers on the other hand; the results recommend that this fully-automated detection system has potential to be used as a real-time monitoring tool for early detection of lameness in broilers starting from GS3. However, to define lower gait scores than GS3, either new feature variables like foot curls and wing use should be inserted into the proposed system or this system should be combined with other automatic behaviour analysis tools for early detection of lameness in future research. It is very important to detect lameness at an early stage because it allows farmers and veterinarians to take immediate management actions in time.

      PubDate: 2017-03-27T01:55:55Z
  • Modeling the spatial distribution of plants on the row for wheat crops:
           Consequences on the green fraction at the canopy level
    • Abstract: Publication date: 15 April 2017
      Source:Computers and Electronics in Agriculture, Volume 136
      Author(s): Shouyang Liu, Frédéric Baret, Bruno Andrieu, Mariem Abichou, Denis Allard, Benoit de Solan, Philippe Burger
      This work investigates the spatial distribution of wheat plants and its consequences on the canopy structure. A set of RGB images were taken from nadir on a total 14 plots showing a range of sowing densities, cultivars and environmental conditions. The coordinates of the plants were extracted from RGB images. Results show that the distance between-plants along the row follows a gamma distribution law, with no dependency between the distances. Conversely, the positions of the plants across rows follow a Gaussian distribution, with strongly interdependent. A statistical model was thus proposed to simulate the possible plant distribution pattern. Through coupling the statistical model with 3D Adel-Wheat model, the impact of the plant distribution pattern on canopy structure was evaluated using emerging properties such as the green fraction (GF) that drives the light interception efficiency. Simulations showed that the effects varied over different development stages but were generally small. For the intermediate development stages, large zenithal angles and directions parallel to the row, the deviations across the row of plant position increased the GF by more than 0.1. These results were obtained with a wheat functional-structural model that does not account for the capacity of plants to adapt to their local environment. Nevertheless, our work will extend the potential of functional-structural plant models to estimate the optimal distribution pattern for given conditions and subsequently guide the field management practices.

      PubDate: 2017-03-27T01:55:55Z
  • A fuzzy clustering segmentation method based on neighborhood grayscale
           information for defining cucumber leaf spot disease images
    • Abstract: Publication date: 15 April 2017
      Source:Computers and Electronics in Agriculture, Volume 136
      Author(s): Xuebing Bai, Xinxing Li, Zetian Fu, Xiongjie Lv, Lingxian Zhang
      Research reported in this paper aims to improve the extraction of cucumber leaf spot disease under complex backgrounds. An improved fuzzy C-means (FCM) algorithm is proposed in this paper. First, three runs of the marked-watershed algorithm, based on HSI space, are applied to isolate the target leaf. Second, the distance between the pixel xj and the cluster center vi is defined as ‖ x j 2 - v i 2 ‖ . Third, the pixel's neighborhood mean gray value, which constitutes a two-dimensional vector with grayscale information, is calculated as a sample point, rather than FCM grayscale. Finally, the neighborhood mean gray value and pixel gray value are weighted by matrix w. To evaluate the robustness and accuracy of the proposed segmentation method, tests were conducted for 129 cucumber disease images in vegetable disease database. Results show that average segmentation error was only 0.12%. The proposed method provides an effective and robust segmentation means for sorting and grading apples in cucumber disease diagnosis, and it can be easily adapted for other imaging-based agricultural applications.

      PubDate: 2017-03-27T01:55:55Z
  • Total station data assessment using an industrial robotic arm for dynamic
           3D in-field positioning with sub-centimetre accuracy
    • Abstract: Publication date: 15 April 2017
      Source:Computers and Electronics in Agriculture, Volume 136
      Author(s): Dimitris S. Paraforos, Marcus Reutemann, Galibjon Sharipov, Roland Werner, Hans W. Griepentrog
      For agricultural tasks related to precision farming, accurate in-field positioning is a necessity. The accuracy of some centimetres that the real time kinematic-global navigation satellite system (RTK-GNSS) can provide is adequate for many applications, such as auto-steering navigation and section control for spraying or fertiliser applications. Nevertheless, the demand for higher in-field accuracy at a mm level is increasing. A device that is gaining a lot of attention in the agricultural sector for its increased accuracy is a robotic total station (TS) that can track a prism mounted on a vehicle. With the aim to be able to use this device under realistic conditions for dynamic 3D in-field positioning at a sub-centimetre level, the accuracy of the TS was assessed utilising an industrial robotic arm. The robotic arm had a repeatability factor of ±0.1mm and was placed outdoors under normal environmental conditions for agriculture practice. Straight AB lines but also U-turn and Pattern-8 experiments were performed. The absolute error of the robotic arm had a maximum mean value of 0.33mm for the Pattern-8 experiment, while the highest error, equal to 1.30mm, was detected in the 95th percentile of the same experiment. The horizontal and vertical relative cross-track error (XTE) between the TS and the robotic arm data was calculated for various speeds and for two different positions of the TS. From the results, it was evident that as the speed increased so did the horizontal relative XTE. Furthermore, changing the position of the TS from in line to perpendicular, in respect to the direction of motion, proved to result in a higher accuracy. The maximum mean horizontal relative XTE value of all experiments was 4.01mm for Pattern-8, which also had the maximum value for the 95th percentile, i.e. 12.86mm. The vertical relative XTE for all experiments did not exceed 10mm including the outliers.

      PubDate: 2017-03-27T01:55:55Z
  • Rapid measurement of total non-structural carbohydrate concentration in
           grapevine trunk and leaf tissues using near infrared spectroscopy
    • Abstract: Publication date: 15 April 2017
      Source:Computers and Electronics in Agriculture, Volume 136
      Author(s): R. De Bei, S. Fuentes, W. Sullivan, E.J. Edwards, S. Tyerman, D. Cozzolino
      Carbohydrate assays are commonly used in crops and plant research to understand the way in which carbohydrates are allocated within the vine and to assess its influence on the physiology and phenology of the plant. Total non-structural carbohydrate (TNC) concentration is normally assessed by wet chemistry methods which are time consuming and costly, especially when studying carbohydrate dynamics over seasons. Near infrared (NIR) spectroscopy is a fast and easy technique that has lately gained wide acceptance for the analysis of the chemical composition of grain, food, wine, pharmaceutical products, among others. Near infrared is the region of the electromagnetic spectrum between 750nm and 2500nm and it is used to gather information on the relative proportions of CH, NH and OH bonds of the organic molecules. This study collected NIR spectra from grapevine trunk and leaf tissues, measured TNC concentration of the same samples using a wet chemical method and compared the results using multivariate data analysis to develop a rapid procedure for the estimation of TNC concentration in grapevine tissues. Results showed that NIR spectroscopy could be used to predict starch and TNC concentration in freeze-dried and ground grapevine trunk and leaf tissues. Moreover, it has been demonstrated that a robust universal model could be applied to the prediction of TNC in both leaves and trunks. Therefore, this method could be used as a practical tool for a rapid screening of TNC concentration for high temporal and spatial assessment of grapevine tissues at given phenological stages. The main advantages of this technique over traditional methods are the rapidity and the ease-of-use protocol in routine analysis, which allows a considerable reduction of costs and time.

      PubDate: 2017-03-27T01:55:55Z
  • Development of an electric-driven control system for a precision planter
           based on a closed-loop PID algorithm
    • Abstract: Publication date: 15 April 2017
      Source:Computers and Electronics in Agriculture, Volume 136
      Author(s): X. He, T. Cui, D. Zhang, J. Wei, M. Wang, Y. Yu, Q. Liu, B. Yan, D. Zhao, L. Yang
      This study presented the design of an electric-driven control system for the seed meter of a precision planter to avoid the issues of poor planting quality and low travel speed limitations associated with conventional ground wheel and chain driven planters. A closed-loop proportional-integral-derivative (PID) algorithm was deployed to control the seed plate rotation speed. The performance of three PID tuning methods (Ziegler-Nichols step response method (ZNM), Cohen-Coon method (CCM), and Chien–Hrones–Reswick method (CHRM)) was compared by Matlab-Simulink simulation, and results testified that the CCM had a better performance with smallest rise time of 0.018s, settling time of 0.082s and maximum overshoot of 26.1%. Field experiments indicated that a four-row planter equipped with the developed electric-driven control system had significantly better quality of feed index (QFI), miss index (MI), and precision index (PREC) values compared with those of a ground wheel and chain driven planter under equivalent working conditions. For a travel speed of 8.6km/h, the average values of the four rows for the QFI, MI, and the PREC were 98.62%, 1.29%, and 14.51%, respectively. For a high travel speed of 13.0km/h, the average QFI still achieved a value of 97.09%. Most of the components employed in the system were made in China, and the overall system cost was much less than similar systems obtained from abroad. As such, the proposed system is accessible to precision planters in developing countries.

      PubDate: 2017-03-27T01:55:55Z
  • PastureBase Ireland: A grassland decision support system and national
    • Abstract: Publication date: 15 April 2017
      Source:Computers and Electronics in Agriculture, Volume 136
      Author(s): Liam Hanrahan, Anne Geoghegan, Michael O'Donovan, Vincent Griffith, Elodie Ruelle, Michael Wallace, Laurence Shalloo
      PastureBase Ireland (PBI) is a web-based grassland management application incorporating a dual function of grassland decision support and a centralized national database to collate commercial farm grassland data. This database facilitates the collection and storage of vast quantities of grassland data from grassland farmers. The database spans across ruminant grassland enterprises – dairy, beef and sheep. To help farmers determine appropriate actions around grassland management, we have developed this data informed decision support tool to function at the paddock level. Individual farmers enter data through the completion of regular pasture cover estimations across the farm, allowing the performance of individual paddocks to be evaluated within and across years. To evaluate the PBI system, we compared actual pasture cut experimental data (Etesia cuts) to PBI calculated outputs. We examined three comparisons, comparing PBI outputs to actual pasture cut data, for individual DM yields at defoliation (Comparison 1), for cumulative annual DM yields including silage data (Comparison 2) and, for cumulative annual DM yields excluding silage data (Comparison 3). We found an acceptable accuracy between PBI outputs and pasture cut data when statistically analyzed using relative prediction error and concordance correlation coefficients for the measurement of total annual DM yield (Comparison 2), with a relative prediction error of 15.4% and a concordance correlation coefficient of 0.85. We demonstrated an application of the PBI system through analysis of commercial farm data across two years (2014–2015) for 75 commercial farms who actively use the system. The analysis showed there was a significant increase in DM yield from 2014 to 2015. The results indicated a greater variation in pasture growth across paddocks within farms than across farms.

      PubDate: 2017-03-27T01:55:55Z
  • Predicting bull behavior events in a multiple-sire pasture with video
           analysis, accelerometers, and classification algorithms
    • Abstract: Publication date: 15 April 2017
      Source:Computers and Electronics in Agriculture, Volume 136
      Author(s): Kaitlynn M. Abell, Miles E. Theurer, Robert L. Larson, Brad J. White, David K. Hardin, Richard F. Randle
      Parentage data from beef calves has shown that in multiple-sire pastures a disproportionate number of calves are born from a single bull. Investigating and accurately quantifying bull behavior within multiple-sire pastures will begin to determine reason(s) for the variability in the number of calves sired. The study objective was to assess accelerometer data and various classification algorithms to accurately predict bull behavior events in a multiple-sire pasture. Behavior events of interest in this study included lying, standing, walking, and mounting. Two bulls and ten estrous synchronized cows were used. True behavior events were determined during daylight hours with video analysis, and matched with accelerometer data. Accelerometers were attached to both ears, withers, and neck of both bulls. Accelerometer data were recorded for every second over 3days. Accelerometer data were used to generate algorithms and accuracy was evaluated compared to known video behavioral data. The prevalence based on the raw video data for lying was 32.6%, standing was 59.4%, walking was 7.4%, and mounting was 0.6%. The random forest classifier had the highest accuracy compared to other classifiers (random tree and decision tree) for each tag location and behavior of interest. The accuracies from the random forest algorithms ranged from 92 to 99% for lying, 85 to 90% for standing, 73 to 77% for walking, and 74% to 80% for mounting. The classification algorithm was able to accurately predict a lying and standing event, and predict a walking and mounting event with a lower accuracy. Further research is needed to determine how behaviors between bulls affects overall parentage data.

      PubDate: 2017-03-27T01:55:55Z
  • Interpolation selection index for delineation of thematic maps
    • Abstract: Publication date: 15 April 2017
      Source:Computers and Electronics in Agriculture, Volume 136
      Author(s): Vanderlei Artur Bier, Eduardo Godoy de Souza
      Precision agriculture aims at the precise management of agricultural inputs toward increasing profits, decreasing losses, and preserving the environment. Thus, the use of thematic maps to understand the behavior of involved attributes is important, and the construction of these maps usually involves some type of interpolation. Choosing the best interpolation method can be difficult, and the use of cross-validation is not trivial. Therefore, the purpose of this study is to propose a selection index of interpolation methods that will help in choosing the best deterministic and stochastic model among those evaluated. The study was conducted within a 15.5-ha area in Southern Brazil, and an interpolation selection index (ISI) was applied to data on clay, copper, and manganese content, and apparent electrical conductivity of the soil using four interpolation methods: inverse distance, inverse distance squared, ordinary kriging, and cokriging. Using the ISI, choosing between deterministic and stochastic interpolation methods is simplified and less subjective. In cases where the deterministic interpolator (inverse of distance squared) was chosen, the spatial dependency was moderated. Note that the proposed statistic (ISI) does not quantify the difference between the analyzed methods.

      PubDate: 2017-03-27T01:55:55Z
  • Industrial scale electromagnetic grain bin monitoring
    • Abstract: Publication date: 15 April 2017
      Source:Computers and Electronics in Agriculture, Volume 136
      Author(s): Colin Gilmore, Mohammad Asefi, Jitendra Paliwal, Joe LoVetri
      We present an Electromagnetic Imaging (EMI) system capable of detecting spoiled grain regions inside a large-scale grain storage bin. Stored grain represents significant economic and nutritional value to humankind, but despite this value, storage losses are common (estimated to vary from 2% to 30%). While there are many mechanisms that cause storage losses, virtually all of them involve higher temperature and/or moisture content of the stored grain. Increases in temperature and/or moisture both raise the complex permittivity of the grain. Our EMI system creates a 3D image of the complex permittivity through 24 antennas mounted on the side of the bin operating at a frequency of 93MHz, combined with a 3D Finite-Element inversion/imaging code. The antennas are designed to have both the desired electrical characteristics, as well as withstand the significant forces caused by the loading and unloading of the grain. Results with 55 tonnes of hard-red winter wheat in a ≈ 2500 bushel (80 tonne) bin show that our system is capable of detecting a small spoilage region (0.24% of total grain volume, 2/5 of a wavelength in size) inside dry bulk grain. The 3D EMI system is a viable method of detecting spoiled grain in industrial grain storage facilities.

      PubDate: 2017-03-27T01:55:55Z
  • Developing and testing an algorithm for site-specific N fertilization of
           winter oilseed rape
    • Abstract: Publication date: 15 April 2017
      Source:Computers and Electronics in Agriculture, Volume 136
      Author(s): Ingo Pahlmann, Ulf Böttcher, Henning Kage
      Winter oilseed rape (WOSR) is a major crop in Germany, combining economic benefits with a high value in crop rotation, but it still lacks agronomically sound concepts for site-specific nitrogen (N) fertilization. Since ecological challenges resulting from high optimal N rates and a low N harvest index are approaching on WOSR cropping systems, optimizing N fertilization becomes crucial. Recent studies showed the importance of taking autumnal N uptake into account when estimating optimal N rates for WOSR, thus autumnal N is pivotal in the algorithm that is introduced in this study. The algorithm was parameterized by using data from site-specific N fertilization trials and optimized to reduce N fertilizer amounts. Afterwards it was tested on different commercial farms in northern Germany. The autumnal N uptake was estimated using hyperspectral reflection measurements gained from tractor-mounted devices, and the data was processed to N application maps used for the N application in spring. In addition, a uniform optimal fertilization and a uniform application of average N rates calculated by the algorithm were applied to provide control treatments. Yield, N balance and economic net-revenue were evaluated for each treatment. Yields from site-specific fertilization were slightly lower (0.06t/ha) than from uniform optimal treatment but 0.22t/ha higher than from the uniform application of the site-specific N amount (not significant in both cases). The N balance was significantly lower when fertilizing site-specifically instead of applying uniform optimal N rate, while the net-revenues were slightly higher.

      PubDate: 2017-03-27T01:55:55Z
  • Modeling reference evapotranspiration using extreme learning machine and
           generalized regression neural network only with temperature data
    • Abstract: Publication date: 15 April 2017
      Source:Computers and Electronics in Agriculture, Volume 136
      Author(s): Yu Feng, Yong Peng, Ningbo Cui, Daozhi Gong, Kuandi Zhang
      Accurate estimation of reference evapotranspiration (ET0) is essential to agricultural water management. The present study developed two artificial intelligence models for daily ET0 estimation only with temperature data, including extreme learning machine (ELM) and generalized regression neural network (GRNN) in 6 meteorological stations of Sichuan basin, southwest China, and compared the proposed ELM and GRNN with the corresponding temperature-based Hargreaves (HG) model and its calibrated version considering FAO-56 Penman-Monteith ET0 as benchmark. Two data management scenarios were evaluated for estimation of ET0: (1) the models were trained/calibrated and tested using the local data of each station; and (2) the models were trained/calibrated using the pooled data from all the stations and tested in each station. In the first scenario, the results showed that the temperature-based ELM model provided the better estimation than the GRNN, HG and calibrated HG models, with average relative root mean square error (RRMSE) of 0.198, mean absolute error (MAE) of 0.267mm/d and Nash-Sutcliffe coefficient (NS) of 0.891, respectively. In the second scenario, GRNN model provided the most accurate results among the considered models, with average RRMSE of 0.194, MAE of 0.263mm/d and NS of 0.895, respectively. Both of the temperature-based GRNN and ELM performed much better than the HG and calibrated HG models for the two scenarios, and the temperature-based GRNN and ELM models are appropriate alternatives for accurate estimation of ET0 for Sichuan basin of southwest China, which is very helpful for farmers or irrigation system operators to improve their irrigation scheduling.

      PubDate: 2017-03-20T09:19:08Z
  • The optimization of crop seeds packaging production planning based on
           dynamic lot-sizing model
    • Abstract: Publication date: 15 April 2017
      Source:Computers and Electronics in Agriculture, Volume 136
      Author(s): Yihang Zhu, Jingjin Zhang, Danfeng Huang, Na Geng
      With the competitive seeds market and development needs of seed companies, the production of crop seeds, including raw seeds procurement, processing, storage, packaging, and logistics, is complicated. Seeds packaging is one of the most important phases, including automatic and manual packaging. Automatic packaging is highly efficient with high setup costs, whereas manual packaging is the opposite. It is difficult for the managers to make the packaging planning, i.e., the number of automatic and manual packaging, based on the forecasted demand. This problem is considered to be a special dynamic lot-sizing problem but it is more complex compared to the general ones. To deal with this problem, this paper proposes a mathematical programming model with the objective of minimizing the total costs. Because the problem is Non-Deterministic Polynomial (NP) hard, the computational time for solving this model is very long. Therefore, this paper proposes a heuristic algorithm (HA) to solve the problem. Numerical results showed that the total costs calculated by the proposed HA are close enough (8.12%) to the optimal total costs of the proposed model and 22.6% lower than the traditional planning. At different scenarios, the HA can keep beneficial and reliable to a certain extent. The proposed model and HA provided detailed illustration for understanding the complexity of the seeds packaging planning and were able to deal with real situations in the seed company.

      PubDate: 2017-03-20T09:19:08Z
  • An improved moth flame optimization algorithm based on rough sets for
           tomato diseases detection
    • Abstract: Publication date: 15 April 2017
      Source:Computers and Electronics in Agriculture, Volume 136
      Author(s): Aboul Ella Hassanien, Tarek Gaber, Usama Mokhtar, Hesham Hefny
      Plant diseases is one of the major bottlenecks in agricultural production that have bad effects on the economic of any country. Automatic detection of such disease could minimize these effects. Features selection is a usual pre-processing step used for automatic disease detection systems. It is an important process for detecting and eliminating noisy, irrelevant, and redundant data. Thus, it could lead to improve the detection performance. In this paper, an improved moth-flame approach to automatically detect tomato diseases was proposed. The moth-flame fitness function depends on the rough sets dependency degree and it takes into a consideration the number of selected features. The proposed algorithm used both of the power of exploration of the moth flame and the high performance of rough sets for the feature selection task to find the set of features maximizing the classification accuracy which was evaluated using the support vector machine (SVM). The performance of the MFORSFS algorithm was evaluated using many benchmark datasets taken from UCI machine learning data repository and then compared with feature selection approaches based on Particle Swarm Optimization (PSO) and Genetic Algorithms (GA) with rough sets. The proposed algorithm was then used in a real-life problem, detecting tomato diseases (Powdery mildew and early blight) where a real dataset of tomato disease were manually built and a tomato disease detection approach was proposed and evaluated using this dataset. The experimental results showed that the proposed algorithm was efficient in terms of Recall, Precision, Accuracy and F-Score, as long as feature size reduction and execution time.

      PubDate: 2017-03-20T09:19:08Z
  • Robust learning-based prediction for timber-volume of living trees
    • Abstract: Publication date: 15 April 2017
      Source:Computers and Electronics in Agriculture, Volume 136
      Author(s): Dong Zhang, Liyan Zhang, Qiaolin Ye, Honghua Ruan
      Many existing timber volume calculating applications are applied by solving a single or multi-variables formulation, while most of those methods are based on the felled trees, which lead to a number of living trees is cut down yearly in different areas. In this paper, a novel learning-based nonlinear timber volume predicted model is proposed, which based on the least squares support vector machine (LSSVM) algorithm, and a modified particle swarm optimization (MPSO) algorithm is used to optimize the parameters involved in the LSSVM. Specifically, the initial weight coefficient in classical particle swarm optimization (PSO) is modified, such that the global optimal solution can be obtained more fleetly and accurately, meanwhile, the timber volume predicted model is established based on the modified algorithm. The experiments are carried out on our collected data, which are obtained from Xiashu plantation of Jurong in Jiangsu Province of China. Three kinds of trees, named Populus, Liriodendron and Soapberry, are selected as the experiment samples. The historical timber volume data of the same kinds, used as the training set in the proposed MPSO-LSSVM model, are obtained from the management of Xiashu plantation. The two properties from the manually measured data, including tree height and diameter at breast height (DBH), are used as the input parameters in the testing set of MPSO-LSSVM. Furthermore, the virtual trees, generated by computers, provide a novel approach to estimate the predicted accuracy of the learning-based model in forest inventory. The experiment results in comparisons with the solutions from volume equation, taper function, felled trees and the virtual trees demonstrate the availability and efficiency of the proposed model in prediction of timber volume.

      PubDate: 2017-03-20T09:19:08Z
  • The description of parcel geometry and its application in terms of land
           consolidation planning
    • Abstract: Publication date: 15 April 2017
      Source:Computers and Electronics in Agriculture, Volume 136
      Author(s): Andrzej Kwinta, Jacek Gniadek
      Land consolidation is the current issue occurring around the world. In the course of consolidation works the state of fragmentation of farms and its impact on their productivity is analyzed. Various methods of fragmentation assessing and variants of implementation of consolidation procedures are being developed. There are many factors of a different nature affecting the need for consolidation. One of the basic elements needed in these works is a quantitative description of flawed shape of plots based on the parameters of their spatial shape. GIS systems allow quick collection of spatial data of the plots or any other surface structures, including parcels. In this paper we focus only on the geometric shape of a parcel as a condition to carry out the consolidation. The paper proposes a methodology for obtaining parameters of the geometry of parcel description, which supports the process of decision-making in the field of land consolidation works. In this paper we proposed an algorithm to replace parcel with equivalent rectangle (ER). The presented solution enables quick processing of the large amounts of data. The results of algorithm use are shown on the example of the fragment of village in the Southern Poland.

      PubDate: 2017-03-20T09:19:08Z
  • On precisely relating the growth of Phalaenopsis leaves to greenhouse
           environmental factors by using an IoT-based monitoring system
    • Abstract: Publication date: 15 April 2017
      Source:Computers and Electronics in Agriculture, Volume 136
      Author(s): Min-Sheng Liao, Shih-Fang Chen, Cheng-Ying Chou, Hsun-Yi Chen, Shih-Hao Yeh, Yu-Chi Chang, Joe-Air Jiang
      Traditional methods for monitoring the environmental factors of a greenhouse and the growth of Phalaenopsis orchids often suffer from low spatiotemporal resolution, high labor-intensity, requiring much time, and a lack of automation and synchronization. To solve these problems, this study develops an Internet of Things (IoT)-based system to monitor the environmental factors of an orchid greenhouse and the growth status of Phalaenopsis at the same time. The whole system consists of an IoT-based environmental monitoring system and an IoT-based wireless imaging platform. An image processing algorithm based on the Canny edge detection method, the seeded region growing (SRG) method, and the mathematical morphology is also developed to estimate the leaf area of Phalaenopsis. The long-term experiments with respect to four different environmental conditions for cultivating Phalaenopsis are conducted. The statistical analysis methods, including the one-way ANOVA, two-way ANOVA, and Games-Howell test, are performed to examine the relation between the growth of Phalaenopsis leaves and the environmental factors in the greenhouse. The optimal cultivation conditions for Phalaenopsis can be easily identified. The experimental results indicate that the daily average growth rate of the leaf area of Phalaenopsis is approximately 79.41mm2/day when the temperature and relative humidity in the greenhouse are controlled at 28.83±2.58 (°C) and 71.81±8.88 (%RH), respectively. The proposed system shows a great potential to provide quantitative information with high spatiotemporal resolution to floral farmers. It is promisingly expected that the proposed system will effectively contribute to updating farming strategies for Phalaenopsis in the future.

      PubDate: 2017-03-20T09:19:08Z
  • webXTREME: R-based web tool for calculating agroclimatic indices of
           extreme events
    • Abstract: Publication date: 15 April 2017
      Source:Computers and Electronics in Agriculture, Volume 136
      Author(s): Tommy Klein, Argyrios Samourkasidis, Ioannis N. Athanasiadis, Gianni Bellocchi, Pierluigi Calanca
      We document the release of webXTREME, a new online tool for the evaluation of indices of climatic extremes (extreme temperatures and aridity) having impact on agricultural production. The tool is globally available and can be operated with either observed weather data or time series representing future climatic conditions. It is thus suitable for risk evaluation under climate change. webXTREME was implemented using Shiny, an open-source programming framework for creating web applications on the basis of the R Statistical Language.

      PubDate: 2017-03-20T09:19:08Z
  • Integrating a multiple crop year routing design for sugarcane harvesters
           to plant a new crop
    • Abstract: Publication date: 15 April 2017
      Source:Computers and Electronics in Agriculture, Volume 136
      Author(s): Kallaya Kittilertpaisan, Supachai Pathumnakul
      This paper discusses the integration of a multiple crop year routing design for a sugarcane harvester and planning of the planting of a new crop. A multiple crop year routing design (i.e., a three year harvesting plan) for a sugarcane crop was formulated and solved by the use of heuristics based on a VRPTW mathematical model (HVRPTW) and a dynamic programming algorithm (HDPA). The three-year harvesting period was determined from the number of years that sugarcane can normally be harvested after a crop is planted in Thailand (one planted crop and two ratoons). The model solution consisted of the harvesting sequence, the harvesters’ travelling routes, the harvest starting time and the number of harvesters required. The results of two methods were compared with respect to the maximum profit and computational time. The results showed that solving the problem using HDPA reduced the maximum profit by only 0.28% on average from the solution provided HVRPTW, and the average computational time was also reduced dramatically. The multiple crop year routing design was integrated with the planting of a new crop to ensure that it contained an ideal solution for the 3rd year plan so it would be effective for all three years. We recommend that the growers use a sugarcane cultivar with a similar maturation time in all of the fields that shared the same harvester’s route to maintain the ideal routes. Furthermore, the same agricultural practices must be applied to all of the sugarcane crops, such as the planting method, cultivar and fertilization.

      PubDate: 2017-03-15T09:09:54Z
  • 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
  • System specification and validation of a noseband pressure sensor for
           measurement of ruminating and eating behavior in stable-fed cows
    • Abstract: Publication date: 15 April 2017
      Source:Computers and Electronics in Agriculture, Volume 136
      Author(s): Nils Zehner, Christina Umstätter, Joël J. Niederhauser, Matthias Schick
      Rumination and eating behavior are important indicators for assessing health and well-being in cattle. The objective of this study was to develop and validate a novel scientific monitoring device for automated measurement of ruminating and eating behavior in stable-fed cows to provide research with a measuring instrument for automated health and activity monitoring. The RumiWatch noseband sensor (Itin+Hoch GmbH, Liestal, Switzerland) incorporates a noseband pressure sensor, a data logger with online data analysis, and software. Automated measurements of behavioral parameters are based on generic algorithms without animal-specific learning data. Thereby, the system records and classifies the duration of chewing activities and enables users to quantify individual ruminating and eating jaw movements performed by the animal. During the course of the development, two releases of the system-specific software RumiWatch Converter (RWC) were created and taken into account for the validation study. The results generated by the two software versions, RWC V0.7.2.0 and RWC V0.7.3.2, were compared with direct behavioral observations. Direct observations of cow behavior were conducted on 14 Swiss dairy farms with an observation time of 1h per animal, resulting in a total sample of 60 dairy cows. Agreement of sensor measurement and direct observation was expressed as Spearman correlation coefficients (rs) for the pooled sample. For consolidated classification of sensor data (1-h resolution), correlations for rumination time were rs =0.91 (RWC V0.7.2.0) and rs =0.96 (RWC, and for eating time rs =0.86 (RWC and rs =0.96 (RWC V0.7.3.2). Both software versions provide a high standard of validity and measuring performance for ruminating and eating behavior. The high to very high correlations between direct observation and sensor data demonstrate that the RumiWatch noseband sensor was successfully developed and validated as a scientific monitoring device for automated measurement of ruminating and eating activity in stable-fed dairy cows.

      PubDate: 2017-03-08T21:41:58Z
  • Optimizing fresh food logistics for processing: Application for a large
           Chilean apple supply chain
    • Abstract: Publication date: 15 April 2017
      Source:Computers and Electronics in Agriculture, Volume 136
      Author(s): Wladimir E. Soto-Silva, Marcela C. González-Araya, Marcos A. Oliva-Fernández, Lluís M. Plà-Aragonés
      This research paper presents optimization models that deal with three kinds of related decisions in horticulture, which are purchasing, transporting and storing fresh produce. The study is intended to assist in decision making in a fresh apple processing plant in order to ensure its annual supply. First, a fresh produce purchasing model is proposed to minimize purchasing costs, producer administration costs and costs for transport to the classification center while taking into consideration the fresh produce offered by each producer, storage capacity and the type of storage for the fruit. These parameters will aid in selecting the producers providing the best price-storage time-distance combination for the purchase. Second, a fresh produce storage model is proposed for minimizing the cost of storage and transport to the classification center (located in the actual processing plant) for each fresh product purchased. Finally, a third integrated model is proposed to give a joint solution to purchasing, transporting and storing the fresh produce. The models are applied in a real case study in an apple dehydration plant in the Maule region of Chile, where average savings were obtained of about 8% with respect to the real costs of purchasing, storing and transporting the fresh produce during the processing period.

      PubDate: 2017-03-08T21:41:58Z
  • Dairy Energy Prediction (DEP) model: A tool for predicting energy use and
           related emissions and costs in dairy farms
    • Abstract: Publication date: 1 April 2017
      Source:Computers and Electronics in Agriculture, Volume 135
      Author(s): Giuseppe Todde, Lelia Murgia, Maria Caria, Antonio Pazzona
      The need of reducing energy consumption in agriculture through more efficient working methods came first into focus in the 1970s as a consequence of oil crisis and the sharp increase of the energy price. Today, besides the economic issues, other aspects connected to a large use of fossil energies are becoming prominent: the depletion of nonrenewable resources and the pollution of the environment. The consumption of direct energy, as fuels and electricity, in dairy farming is a source of greenhouse gas emissions and contributes significantly to increasing the carbon footprint of milk. The objectives of this study were: (a) to build linear models to estimate the consumption of diesel fuel and electricity in dairy farms; (b) to develop a calculation tool in order to assess efficiency indicators associated to energy consumption, emissions of carbon dioxide and energy costs in dairy farms. Data used in the model development were collected from 285 dairy farms located in southern Italy. Two linear regression models were developed using total fuel (TF, kgyear−1) and electricity consumption (TE, kWhyear−1) as responses and total number of heads, total number of lactating cows, milk produced, and cultivated land as primary independent variables. Model’s parameters were then implemented in a spread sheet to develop the Dairy Energy Prediction (DEP) tool. Entering some basic information about dairy farms characteristics, DEP is able to predict diesel fuel and electricity consumptions, to list several Energy Utilization Indices (EUIs), to estimate carbon dioxide emissions from energy uses (kg CO2-eq), to evaluate the costs of energy purchase. DEP may be used by farmers, to evaluate the energy performances of their farms, and by researchers and stakeholders to compare the impact of different energy scenarios (i.e. LCA studies, economic evaluation, environmental assessment, etc.). DEP tool is available online at this link:

      PubDate: 2017-03-01T15:58:17Z
  • Quantifying time-series of leaf morphology using 2D and 3D photogrammetry
           methods for high-throughput plant phenotyping
    • Abstract: Publication date: 1 April 2017
      Source:Computers and Electronics in Agriculture, Volume 135
      Author(s): Nan An, Stephen M. Welch, R.J. Cody Markelz, Robert L. Baker, Christine M. Palmer, James Ta, Julin N. Maloof, Cynthia Weinig
      Conventional phenotyping methods impose a significant bottleneck to the characterization of genotypic and environmental effects on trait expression in plants. In particular, invasive and destructive sampling methods along with manual measurements widely used in conventional studies are labor-intensive, time-consuming, costly, and can lack consistency. These experimental features impede large-scale genetic studies of both crops and wild plant species. Here, we present a high-throughput phenotyping pipeline using photogrammetry and 3D modeling techniques in the model species, Arabidopsis thaliana. We develop novel photogrammetry and computer vision algorithms to quantify 2D and 3D leaf areas for a mapping population of 1050 Arabidopsis thaliana lines, and use 2D areas to analyze plant nastic movements and diurnal cycles. Compared to the 2D leaf areas, 3D leaf areas show an uncorrupted growth trend regardless of plant nastic movement. With optimized algorithms, our pipeline throughput is very computationally efficient for screening a large number of plants. The pipeline not only supports measurement of organ-level growth and development over time, but also enables analysis of whole-plant phenotypes and, thus, identification of genotype-specific performance. Further, the accuracy results evaluating the relationship between physical dimensions and 3D measurements indicate an R2 =0.99, and the average 3D area processing time per plant is 0.02s. Our algorithms provide both high accuracy and throughput in plant phenotyping, thereby, enabling progress in plant genotypic modeling.

      PubDate: 2017-03-01T15:58:17Z
  • Urine patch detection using LiDAR technology to improve nitrogen use
           efficiency in grazed pastures
    • Abstract: Publication date: 1 April 2017
      Source:Computers and Electronics in Agriculture, Volume 135
      Author(s): R.L. Roten, J. Fourie, J.L. Owens, J.A.K. Trethewey, D.C. Ekanayake, A. Werner, K. Irie, M. Hagedorn, K.C. Cameron
      In grazed dairy pastures, the largest N source for both nitrate (NO3 −) leaching and nitrous oxide (N2O) emissions is urine-N excreted by the animals. Additional application of N on urine patches as fertilizer may increase these losses. Identification of urine patches could reduce N losses in grazed pastures through more efficient fertilizer application and improved fertilizer N use efficiency (NUE). The aim of this study was to determine if remote sensing using Light Detection and Ranging (LiDAR) technology could accurately identify urine patches in grazed pastures based on height variation of the grass canopy in close proximity. Synthetic cow urine (7gNL−1) was applied to two blocks (20m×20m) in a well-established pasture in Canterbury, New Zealand, which had no recent exposure to grazing animals or N fertilization. Urine patches were scanned weekly for five weeks. LiDAR based contour maps of the pasture were shown to accurately detect the asymmetric urine patches as well as calculate a percent area of urine based high N as early as one week after a simulated grazing event.

      PubDate: 2017-02-22T15:49:58Z
  • An automatic active contour method for sea cucumber segmentation in
           natural underwater environments
    • Abstract: Publication date: 1 April 2017
      Source:Computers and Electronics in Agriculture, Volume 135
      Author(s): Xi Qiao, Jianhua Bao, Lihua Zeng, Jian Zou, Daoliang Li
      Sea cucumbers have become an important sector of the marine industry in northern China, with a culture area exceeding one million acres and a production value over one hundred and twenty million dollars. However, sea cucumber culture and fishing are mainly dependent on manual work. To promote the development of sea cucumber culture automation, it is necessary to research sea cucumber automatic segmentation based on machine vision in natural underwater environments. Sea cucumbers usually live in an environment where lighting, visibility and stability are generally not controllable, which cause underwater images of sea cucumbers to be distorted, blurred, and severely attenuated. Moreover, sea cucumbers are flexible and colored much like sandy sediments. Therefore, it is difficult to fully separate a cucumber from the background in an underwater image. For fast and accurate automatic segmenting of sea cucumbers, an improved method based on active contour is presented in this paper. Image fusion based on the RGB color space and the contrast limited adaptive histogram equalization (CLAHE) method are used to increase the contrast of the sea cucumber thorns and body, respectively. Then, an edge detection algorithm is proposed to extract the edge of the sea cucumber thorns as an initial contour for the thorn segmentation, and a rectangular contour based on the edge information is built as the initial contour for the body segmentation. Finally, the results of the thorn and body are fused. All the procedures are automatically completed without human intervention. Qualitative and quantitative analysis indicates that the proposed method outperformed the other two compared methods in sea cucumber segmentation. A test with 120 samples showed that for the proposed method, the mean values of Euclidean distance, sensitivity, specificity, and accuracy were 12.7, 84.51, 96.97, and 96.54, respectively. The average time to run the algorithm for all images is 4.27s. Thus, the proposed method could work for sea cucumber monitoring and fishing in real time.

      PubDate: 2017-02-22T15:49:58Z
  • Groundwater monitoring and management: Status and options in Pakistan
    • Abstract: Publication date: 1 April 2017
      Source:Computers and Electronics in Agriculture, Volume 135
      Author(s): Muhammad Tousif Bhatti, Arif A. Anwar, Muhammad Aslam
      Due to extensive groundwater development in the recent past, Pakistan now faces enormous challenges of groundwater management as it struggles to ensure food security for its rapidly growing population. These management challenges require a re-balancing of surface and groundwater monitoring objectives and approaches in the country. This article presents the current status of the groundwater monitoring and management in Pakistan. A compelling case is presented for optimization of material resources in improving groundwater level and quality data by proposing to use farmer organizations as a source of crowd sourced groundwater information. The authors showcase new methods to collect groundwater data and demonstrate use of automatic recording instruments for groundwater monitoring in a tertiary canal command area in the Pakistan’s Punjab. The results suggest that the potential for broader impact by engaging farmer organization and expanding monitoring networks is attractive. A common concern about long term deployment of automatic instruments is that the observation wells are not purged before extracting water quality samples. The authors address this concern through a field experiment by utilizing capabilities of automatic recording instruments.

      PubDate: 2017-02-22T15:49:58Z
  • Hyperspectral imaging for classification of healthy and gray mold diseased
           tomato leaves with different infection severities
    • Abstract: Publication date: 1 April 2017
      Source:Computers and Electronics in Agriculture, Volume 135
      Author(s): Chuanqi Xie, Ce Yang, Yong He
      This study used hyperspectral imaging technique to classify healthy and gray mold diseased tomato leaves. Hyperspectral images of diseased samples at 24h, 48h, 72h, 96h and 120h after inoculation and healthy samples were taken in the wave range of 380–1023nm. A total of ten pixels from each sample were identified as the region of interest (ROI), and the mean reflectance values of ROI were calculated. The dependent variables of healthy samples were set as 0, and diseased samples were set as 1, 2, 3, 4 and 5 according to infection severities, respectively. K-nearest neighbor (KNN) and C5.0 models were built to classify the samples using the full wave band set. To reduce data volume, features ranking (FR) was used to select sensitive bands. Then, the KNN classification model was built based on just the selected bands. This later procedure of reducing spectral dimensionality and classifying infection stages was defined as FR-KNN. Performances of KNN classifier on all wave bands and FR-KNN were compared. The overall classification results in the testing sets were 61.11% for KNN, 54.17% for C5.0 and 45.83% for FR-KNN model. When differentiating infected samples from control, the testing results were 94.44%, 94.44% and 97.22% for each model, respectively. In addition, early disease detection (1dpi) obtained the results of 66.67% for KNN, 66.67% for C5.0 and 41.67% for FR-KNN. Therefore, it demonstrated that hyperspectral imaging has the potential to be used for early detection of gray mold disease on tomato leaves.

      PubDate: 2017-02-22T15:49:58Z
  • Assessing sampling designs for determining fertilizer practice from yield
    • Abstract: Publication date: 1 April 2017
      Source:Computers and Electronics in Agriculture, Volume 135
      Author(s): S.E. Muhammed, B.P. Marchant, R. Webster, A.P. Whitmore, G. Dailey, A.E. Milne
      Many farmers sample their soil to measure the concentrations of plant nutrients, so as to decide how much fertilizer to apply. Now that fertilizer can be applied at variable rates farmers want to know whether maps of nutrient concentration made from grid samples or of field subdivisions (zones within their fields) are merited: do such maps lead to greater profit than would a single measurement on a bulked sample for each field when all costs are taken into account? We have examined the merits of grid-based and zone-based sampling strategies over single field-based averages using continuous spatial data on wheat yields at harvest in six fields in southern England and simulated concentrations of phosphorus (P) in the soil. We have taken into account current prices of wheat, P fertilizer and sampling and laboratory analysis. Variograms of yield provide guides for sampling. We show that where variograms have large variances and long effective ranges grid-sampling and mapping are feasible and have large probabilities of being cost-effective. Where effective ranges are short, sampling must be dense to reveal the spatial variation and be expensive, and variable-rate application of fertilizer is likely to be impracticable and almost certainly not cost-effective. We found zone-based sampling was less likely to be cost effective in a similar situation when the management zones were poorly correlated to P concentrations.

      PubDate: 2017-02-22T15:49:58Z
  • Evaluating the impact of soil conservation measures on soil organic carbon
           at the farm scale
    • Abstract: Publication date: 1 April 2017
      Source:Computers and Electronics in Agriculture, Volume 135
      Author(s): Andrea Pezzuolo, Benjamin Dumont, Luigi Sartori, Francesco Marinello, Massimiliano De Antoni Migliorati, Bruno Basso
      No-tillage (NT) is considered the least invasive conservation agriculture technique and has shown to be the effective in increasing soil C stocks, and reducing losses compared to others tillage systems. In Italy, the Veneto Region was the first to establish a subsidies scheme aimed at promoting the adoption of NT practices. This program encourages farmers to perform direct seeding, alternate autumn and winter crops and maintain soil cover throughout the year by leaving crop residues or sowing cover crops. The goals of this study were to: (i) compare the CO2 emission and soil C sequestration patterns of agricultural soils under CT and NT management practices in the Veneto region and (ii) analyse the potential mid-term benefits (2010–2025) of NT management in terms of soil organic C dynamics and CO2 balance. Agronomic data and soil organic carbon levels were measured from 2010 to 2014 in eight farms in the Veneto region that had adopted CT and NT techniques. Field measurements were used to calibrate first and then validate the SALUS model to compare the mid-term impact of CT and NT practices using climate projections. SOC carbon pools in the model were initialized using the procedure described in Basso et al. (2011c). This is the first study to employ a model using such an extensive dataset at the farm level to assess the CT and NT strategies within this region. Results of this research will assist farmers and policy makers in the region to define the tillage systems most suited to improve soil C stocks and thereby minimize CO2 emissions from agricultural soils. Overall, simulations indicated that SOC stocks can decrease under both CT and NT regimes, however SOC oxidation rates were substantially lower under NT. Critically, the greatest reduction in CO2 emission was observed when NT was adopted in soil with high levels of SOM. This highlights the benefits of NT adoption in terms of soil fertility preservation and CO2 emissions mitigation.

      PubDate: 2017-02-22T15:49:58Z
  • A new approach for estimating mangrove canopy cover using Landsat 8
    • Abstract: Publication date: 1 April 2017
      Source:Computers and Electronics in Agriculture, Volume 135
      Author(s): Hesham Abd-El Monsef, Scot E. Smith
      Due to background reflectance, it is difficult to accurately map sparse canopy vegetation using moderate-resolution satellite imagery. Information contained in virtually all the pixels is a mix of leaf vegetation, soil, branches and shadow. Presented in this paper is a novel approach to improving the accuracy of mapping mangrove canopy using Landsat 8 imagery by incorporating seven indices: Normalized Difference Vegetation Index, Infrared Index, Leaf Area Index, Green Atmospherically Resistant Index, Optimized Soil Adjusted Vegetation Index, Normalized Difference Built-up Index and Normalized Difference Water Index. Results demonstrated that the accuracy of mapping mangrove can be significantly improved using this approach.

      PubDate: 2017-02-22T15:49:58Z
  • Development and evaluation on a wireless multi-gas-sensors system for
           improving traceability and transparency of table grape cold chain
    • Abstract: Publication date: 1 April 2017
      Source:Computers and Electronics in Agriculture, Volume 135
      Author(s): Xiang Wang, Qile He, Maja Matetic, Tomislav Jemric, Xiaoshuan Zhang
      There is increasing requirement to improve traceability and transparency of table grapes cold chain. Key traceability indicators including temperature, humidity and gas microenvironments (e.g., CO2, O2, and SO2) based on table grape cold chain management need to be monitored and controlled. This paper presents a Wireless Multi-Gas-Sensors System (WGS2) as an effective real-time cold chain monitoring system, which consists of three units: (1) the WMN which applies the 433MHz as the radio frequency to increase the transmission performance and forms a wireless sensor network; (2) the WAN which serves as the intermediary to connect the users and the sensor nodes to keep the sensor data without delay by the GPRS remote transmission module; (3) the signal processing unit which contains embedded software to drive the hardware to normal operation and shelf life prediction for table grapes. Then the study evaluates the WGS2 in a cold chain scenario and analyses the monitoring data. The results show that the WGS2 is effective in monitoring quality, and improving transparency and traceability of table grape cold chains. Its deploy ability and efficiency in implantation can enable the establishment of a more efficient, transparent and traceable table grape supply chain.

      PubDate: 2017-02-22T15:49:58Z
  • Multi-camera surveillance systems for time and motion studies of timber
           harvesting equipment
    • Abstract: Publication date: 1 April 2017
      Source:Computers and Electronics in Agriculture, Volume 135
      Author(s): Marco Contreras, Rafael Freitas, Lucas Ribeiro, Jeffrey Stringer, Chase Clark
      We evaluated the feasibility of using a multi-camera security system to conduct time and motion studies. It was installed on a John Deere 540G cable skidder and connected to the skidder’s battery for continuous recording with minimal effort and intervention. After recording the skidder’s work for eleven experimental skidding cycles, time stamped video footage was visually inspected to obtain time consumption of work tasks, which provided for accurate calculation of total cycle times and delays. Several advantages of the security camera system including quick and non-invasive installation, large memory storage, transferability, resistance to weather elements, and the capacity to capture different views, offer a great potential for this method to be adopted as a reliable approach to accurately conduct time and motion studies. Along with distance and gradient information for skid-trail segments, we also explored the influence of gradient on travel time for loaded and unloaded skidding. There is a need for future studies to formally explore this relationship and develop more detailed cycle time equations that explicitly take into account skid-trail gradient for individual segments.

      PubDate: 2017-02-22T15:49:58Z
  • Image classification for detection of winter grapevine buds in natural
           conditions using scale-invariant features transform, bag of features and
           support vector machines
    • Abstract: Publication date: 1 April 2017
      Source:Computers and Electronics in Agriculture, Volume 135
      Author(s): Diego Sebastián Pérez, Facundo Bromberg, Carlos Ariel Diaz
      In viticulture, there are several applications where bud detection in vineyard images is a necessary task, susceptible of being automated through the use of computer vision methods. A common and effective family of visual detection algorithms are the scanning-window type, that slide a (usually) fixed size window along the original image, classifying each resulting windowed-patch as containing or not containing the target object. The simplicity of these algorithms finds its most challenging aspect in the classification stage. Interested in grapevine buds detection in natural field conditions, this paper presents a classification method for images of grapevine buds ranging 100–1600 pixels in diameter, captured in outdoor, under natural field conditions, in winter (i.e., no grape bunches, very few leaves, and dormant buds), without artificial background, and with minimum equipment requirements. The proposed method uses well-known computer vision technologies: Scale-Invariant Feature Transform for calculating low-level features, Bag of Features for building an image descriptor, and Support Vector Machines for training a classifier. When evaluated over images containing buds of at least 100 pixels in diameter, the approach achieves a recall higher than 0.9 and a precision of 0.86 over all windowed-patches covering the whole bud and down to 60% of it, and scaled up to window patches containing a proportion of 20–80% of bud versus background pixels. This robustness on the position and size of the window demonstrates its viability for use as the classification stage in a scanning-window detection algorithms.

      PubDate: 2017-02-15T15:35:38Z
  • Sensing-cloud: Leveraging the benefits for agricultural applications
    • Abstract: Publication date: 1 April 2017
      Source:Computers and Electronics in Agriculture, Volume 135
      Author(s): Tamoghna Ojha, Sudip Misra, Narendra Singh Raghuwanshi
      The advent of the sensor-cloud framework empowers the traditional wireless sensor networks (WSNs) in terms of dynamic operation, management, storage, and security. In recent times, the sensor-cloud framework is applied to various real-world applications. In this paper, we highlight the benefits of using sensor-cloud framework for the efficient addressing of various agricultural problems. We address the specific challenges associated with designing a sensor-cloud system for agricultural applications. We also mathematically characterize the virtualization technique underlying the proposed sensor-cloud framework by considering the specific challenges. Furthermore, the energy optimization framework and duty scheduling to conserve energy in the sensor-cloud framework is presented. The existing works on sensor-cloud computing for agriculture does not specifically define the specific components associated with it. We categorize the distinct features of the proposed model and evaluated its applicability using various metrics. Simulation-based results show the justification for choosing the framework for agricultural applications.

      PubDate: 2017-02-15T15:35:38Z
  • Online detection and localisation of piglet crushing using vocalisation
           analysis and context data
    • Abstract: Publication date: 1 April 2017
      Source:Computers and Electronics in Agriculture, Volume 135
      Author(s): Christian Manteuffel, Eberhard Hartung, Mariana Schmidt, Gundula Hoffmann, Peter Christian Schön
      Fatal piglet crushing by the mother sow is a pervasive economic and animal welfare issue in piglet production. To keep the mother sow in a farrowing cage is the established countermeasure. This facility is a compromise that results in an impairment of the sows’ welfare to the benefit of her piglets and the farmer. A natural behaviour pattern which is demonstrated by most but not all sows is to free the trapped piglet by a posture change. Promoting this behaviour through aversive stimulations is an alternative approach to reduce piglet mortality. This approach requires an identification and localisation of ongoing piglet trapping in real-time. The present study investigates the online analysis of piglet vocalisation for this purpose. The results show, that trapping related stress articulations are outnumbered by other stress related articulations by a factor of 1:140 in a farrowing compartment with only 4 sows. Theoretical calculations for larger compartments indicate that this ratio becomes even worse due to an increasing influence of vocalisation from neighbouring pens. However, the specificity could be increased to more than 95% and precision to approximately 30% while maintaining a sensitivity of approximately 70% by retrospectively applying context based event filters. This specificity would be sufficient to limit the average number of erroneous trapping detections to one detection per sow within 3days without a substantial loss of sensitivity. Effective parameters for filtering were the age of the piglets and the sows’ body posture history. Calculations with hypothetical spatial event filters showed that this classification performance could be maintained even in much larger farrowing compartments. Combined with an aversive stimulation principle that can be applied to a whole region, this detection technology could be useful to reduce piglet mortality in loose farrowing applications. An already known and effective stimulation principle of this type is floor vibration. Such an active piglet rescue system would allow limiting the impairment of welfare to only those sows that actually crush piglets and to the time when piglets are being crushed.

      PubDate: 2017-02-15T15:35:38Z
  • Coffee plantation area recognition in satellite images using Fourier
    • Abstract: Publication date: 1 April 2017
      Source:Computers and Electronics in Agriculture, Volume 135
      Author(s): Du-Ming Tsai, Wan-Ling Chen
      In this study, a machine vision scheme is proposed for coffee plantation area recognition in satellite images. It automatically segments the row-planted coffee field from forest trees and irrelevant areas in the image. The result can be used for coffee yield estimation to improve the supply and demand of coffee commodity in the market. Commercial coffee plantation grows coffee trees in rows along a specific direction to increase the production yield and management efficiency. The coffee plants and forest trees present the same color tone in the image and, thus, color cannot be used for the discrimination. The row-planting pattern of coffee trees shows structural texture in the satellite image. This study presents a Fourier transform-based method to extract structural features in the spectral domain for image segmentation. Row-planted coffee fields generate high-energy frequency components in a single direction, while naturally-growing plants present omnidirectional frequency components in the spectral domain image. The main frequency in the power spectrum indicates the number of parallel lines in a small patch window and, thus, gives the density feature. The density feature for the row-planted coffee filed is equivalent to the number of rows in a unit square area, whereas it is only one for the randomly-growing plants. This study analyzes the satellite images of coffee plantation regions in different times with varying illuminations and growing stages in Brazil, Africa, Vietnam and Hawaii. The experimental results have shown that the Fourier-based structural and density features can provide correct segmentation to distinguish the row-planted coffee field from irrelevant vegetation areas in the satellite image.

      PubDate: 2017-02-15T15:35:38Z
  • Water cycle estimation by neuro-fuzzy approach
    • Abstract: Publication date: 1 April 2017
      Source:Computers and Electronics in Agriculture, Volume 135
      Author(s): Milos Ilic, Srdjan Jovic, Petar Spalevic, Igor Vujicic
      Water cycle shows the continuous movement of water above and below surface. The water moves could involves the energy exchange which can lead to temperature changes. It is crucial to elaborate the energy exchange in relation to climate changing. Evapotranspiration is one of the most important part of the water cycle. Evaporation presents the water movement to the air and transpiration presents the water movements within a plant. Since the evapotranspiration is very important parameter for climate change, in this article the main aim was to estimate the evapotranspiration based on different climatic parameters such as air temperature, vapor pressure and humidity. Neuro-fuzzy approach was used for the process modeling since the evapotranspiration is very unpredictable factor with strong fluctuation through year. The results could be used for evapotranspiration estimation based on the climate data in order to improve water resources management for agricultural production and irrigation scheduling.

      PubDate: 2017-02-09T11:49:59Z
  • Using 3D vision camera system to automatically assess the level of
           inactivity in broiler chickens
    • Abstract: Publication date: 1 April 2017
      Source:Computers and Electronics in Agriculture, Volume 135
      Author(s): A. Aydin
      In this study, a new and non-invasive method was developed to automatically assess the lameness of broilers. For this aim, images of broiler chickens were recorded by a 3D vision camera, which has a depth sensor as they walked along a test corridor. Afterwards, the image-processing algorithm was applied to detect the number of lying events (NOL) based on the information of the distance between animal and the depth sensor of 3D camera. In addition to that, latency to lie down (LTL) of broilers was detected by 3D camera. Later on, the data obtained by proposed system were compared with visually assessed manual labelling data (reference method) and the relation between these measures and lameness was investigated. 93% of NOL were correctly classified by the proposed 3D vision camera system when compared to manual labelling using a data set collected from 250 broiler chickens. Furthermore, the results showed a significant correlation between NOL and gait score (R2 =0.934) and a significant negative correlation between LTL and gait score level of broiler chickens (R2 =−0.949). Because of the strong correlations were found between NOL, LTL and gait score level of broilers on the one hand and between the results obtained by 3D system and manual labelling on the other hand, the results indicate that this 3D vision monitoring method can be used as a tool for assessing lameness of broiler chickens.
      Graphical abstract image

      PubDate: 2017-02-09T11:49:59Z
  • Strategic design for dynamic multi-zone truckload shipments: A study of
           OTOP agricultural products in Thailand
    • Abstract: Publication date: 1 April 2017
      Source:Computers and Electronics in Agriculture, Volume 135
      Author(s): Pongchanun Luangpaiboon
      The one-tambon-one-product (OTOP) project in Thailand has been successful according to statistical data from the Interior Ministry's Department of Community Development. However, there is a concern that dispatchers in the supporting trucking industry carry out both tasks of truckload assignment and driver scheduling. It is necessary to investigate more efficient and reliable alternatives to handle the growing and fluctuating quantities of inbound and outbound goods in each service area. This paper focuses on determining alternative ways for enhancing the efficiency of the transportation management system based on multi-zone dispatching (MZD) with the objective of minimising total load imbalances. This problem is considered to be a special case of the MZD but it is complex when compared to the conventional MZD, especially since planning horizons are considered and modified as a dynamic MZD (dMZD). In this paper an intelligent water drop optimisation (IWD) was used to solve the problem. In order to improve the solution quality, the novel two-phase approximated algorithm (2-PAA) with two additional processes from the variable neighbourhood and dynamic programming algorithms, were proposed. Experimental results with preferable settings showed the 2-PAA was computationally effective and feasible to generate dispatching alternatives for both static and dynamic problems. Using a set of real data, the simulation suggested a 3-period dMZD configuration for driver scheduling with preferable levels of tour length and imbalance statistics.

      PubDate: 2017-02-09T11:49:59Z
  • Stereo vision with Equal Baseline Multiple Camera Set (EBMCS) for
           obtaining depth maps of plants
    • Abstract: Publication date: 1 April 2017
      Source:Computers and Electronics in Agriculture, Volume 135
      Author(s): Adam L. Kaczmarek
      This paper presents a method of improving the estimation of distances between an autonomous harvesting robot and plants with ripe fruits by using the vision system based on five cameras. The system is called Equal Baseline Multiple Camera Set (EBMCS). EBMCS has some features of a camera matrix and a camera array. EBMCS is regarded as a set of stereo cameras for estimating distances by obtaining disparity maps and depth maps. This paper introduces Exceptions Excluding Merging Method (EEMM) which makes it possible to improve the quality of disparity maps by integrating maps acquired from individual stereo cameras included in EBMCS. The method was tested with eight different stereo matching algorithms including Efficient Large-scale Stereo Matching (ELAS), algorithms implemented in the OpenCV library and algorithms available in Middlebury Stereo Vision Page. Experiments were performed on input data sets which contained images of strawberry plants, cherry trees and redcurrant plants. The bad matching pixels (BMP) metric was used for measuring the error rate in disparity maps used in the distance estimation. The results of experiments showed that, on average, the EEMM merging method used with EBMCS consisting of five cameras reduces the error rate of the distance estimation by 26.55% in comparison to results obtained from stereoscopy based on a single stereo camera. The best results were acquired by using with five cameras a stereo matching algorithm based on a graph cut.

      PubDate: 2017-02-09T11:49:59Z
  • Spatial monitoring and zoning water quality of Sistan River in the wet and
           dry years using GIS and geostatistics
    • Abstract: Publication date: 1 April 2017
      Source:Computers and Electronics in Agriculture, Volume 135
      Author(s): Atefeh Mir, Jamshid Piri, Ozgur Kisi
      Water quality and quantity is considered as one of the main pillars of sustainable development. On the other hand, rivers are regarded as one of the main and accessible resources that provide the human needs, that in addition to the water quantity, water quality is also one of the important determinative parameters. This study deals with spatial monitoring of chemical parameters of Sistan River water in the dry and wet years in order to follow the variations in the water chemical quality, determine the most suitable sites to extract potable water and irrigation, and optimize management of water resources in Sistan Plain. For this purpose, climate variables including monthly precipitations (1981–2010) were used to determine the index years of dry and wet together with the results of chemical decomposition of Sistan River water. First, mapping of the parameters of TDS, SAR, EC, Na, Ca, Mg, Cl, SO4 and HCO3 was done in GIS using Geostatistical methods, and then maps were classified according to international standards Wilcox and Schoeller. Finally, using the overlapping method, the river's water quality was determined to extract potable water and irrigation during the wet and dry years. The Wilcox standard results indicated that the quality of the Sistan River's water for irrigation during the drought period lies within the five classes of C3S2, C4S1, C2S2, C3S1and C4S2, and in the wet year period, it lies in the classes of C2S1 and C1S1. The Schoeller diagram results revealed that in the wet year period, the river's water quality is in the range of good and acceptable for drinking purposes. On the other hand, during the drought period, it is in the status of acceptable, moderate, unfavorable, and absolutely unfavorable, acceptable status, the highest percentage is allocated.

      PubDate: 2017-02-09T11:49:59Z
  • A multivariate dynamic linear model for early warnings of diarrhea and pen
           fouling in slaughter pigs
    • Abstract: Publication date: 1 April 2017
      Source:Computers and Electronics in Agriculture, Volume 135
      Author(s): Dan Børge Jensen, Nils Toft, Anders Ringgaard Kristensen
      We present a method for providing early, but indiscriminant, predictions of diarrhea and pen fouling in grower/finisher pigs. We collected data on dispensed feed amount, water flow, drinking bouts frequency, temperature at two positions per pen, and section level humidity from 12 pens (6 double pens) over three full growth periods. The separate data series were co-modeled at pen level with time steps of one hour, using a multivariate dynamic linear model. The step-wise forecast errors of the model were unified using Cholesky decomposition. An alarm was raised if the unified error exceeded a set threshold a sufficient number of times, consecutively. Using this method with a 7day prediction window, we achieved an area under the receiver operating characteristics curve of 0.84. Shorter prediction windows yielded lower performances, but longer prediction windows did not affect the performance.

      PubDate: 2017-02-09T11:49:59Z
  • Weed identification based on K-means feature learning combined with
           convolutional neural network
    • Abstract: Publication date: 1 April 2017
      Source:Computers and Electronics in Agriculture, Volume 135
      Author(s): JingLei Tang, Dong Wang, ZhiGuang Zhang, LiJun He, Jing Xin, Yang Xu
      Aiming at the problem that unstable identification results and weak generalization ability in feature extraction based on manual design features in weed identification, this paper take the soybean seedlings and its associated weeds as the research object, and construct a weed identification model based on K-means feature learning combined with Convolutional neural network. Combining advantages of multilayer and fine-turning of parameters of the convolutional neural network, this paper set k-means unsupervised feature learning as pre-training process, and replaced the random initialization weights of traditional CNN parameters. This method make the parameters can be obtained more reasonable values before optimization to gain higher weed identification accuracy. The experimental results show that this method with K-means pre-training achieved 92.89% accuracy, beyond 1.82% than convolutional neural network with random initialization and 6.01% than the two layer network without fine-tuning. Our results suggest that identification accuracy might be improved by fine-tuning of parameters.

      PubDate: 2017-02-09T11:49:59Z
  • A crop trait information acquisition system with multitag-based
           identification technologies for breeding precision management
    • Abstract: Publication date: 1 April 2017
      Source:Computers and Electronics in Agriculture, Volume 135
      Author(s): Yan-yun Han, Kai-yi Wang, Zhong-qiang Liu, Qi Zhang, Shou-hui Pan, Xiang-yu Zhao, Shu-feng Wang
      This paper aims to establish a crop trait information acquisition system by combining barcode and radio frequency-based electronic identification (RFID) and near-field communication (NFC) technology applications. The system was devised to ensure (a) correct identification of each crop material and plot, (b) quick query and positioning for information collection, (c) correct combination of crop phenotypic data and images, and (d) reliable recording of periodic crop trait data with dependable transmission to the main server. This system, with multitag-based identification technology, was developed on an Android platform using the Java language. A type of RFID/NFC tag with a high-frequency chip was applied in the core, and a quick response (QR) code was used on the surface for material identification. A smartphone with NFC function can be used as an RFID reader, and its built-in camera can be used to decode QR codes. The system was used in some seed industries’ commercial breeding and in national crop variety regional trial, with its functions including reading/writing of RFID/NFC tags, decoding QR codes, acquiring crop trait information, creating field maps, and uploading data. This system provided a low-cost and highly efficient solution for crop trait information collection.

      PubDate: 2017-02-09T11:49:59Z
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