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ENGINEERING (1206 journals)                  1 2 3 4 5 6 7 | Last

Showing 1 - 200 of 1205 Journals sorted alphabetically
3 Biotech     Open Access   (Followers: 7)
3D Research     Hybrid Journal   (Followers: 19)
AAPG Bulletin     Hybrid Journal   (Followers: 6)
AASRI Procedia     Open Access   (Followers: 15)
Abstract and Applied Analysis     Open Access   (Followers: 3)
Aceh International Journal of Science and Technology     Open Access   (Followers: 2)
ACS Nano     Full-text available via subscription   (Followers: 234)
Acta Geotechnica     Hybrid Journal   (Followers: 7)
Acta Metallurgica Sinica (English Letters)     Hybrid Journal   (Followers: 5)
Acta Polytechnica : Journal of Advanced Engineering     Open Access   (Followers: 2)
Acta Scientiarum. Technology     Open Access   (Followers: 3)
Acta Universitatis Cibiniensis. Technical Series     Open Access  
Active and Passive Electronic Components     Open Access   (Followers: 7)
Adaptive Behavior     Hybrid Journal   (Followers: 11)
Adıyaman Üniversitesi Mühendislik Bilimleri Dergisi     Open Access  
Adsorption     Hybrid Journal   (Followers: 4)
Advanced Engineering Forum     Full-text available via subscription   (Followers: 6)
Advanced Science     Open Access   (Followers: 5)
Advanced Science Focus     Free   (Followers: 3)
Advanced Science Letters     Full-text available via subscription   (Followers: 7)
Advanced Science, Engineering and Medicine     Partially Free   (Followers: 7)
Advanced Synthesis & Catalysis     Hybrid Journal   (Followers: 17)
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: 15)
Advances in Fuzzy Systems     Open Access   (Followers: 5)
Advances in Geosciences (ADGEO)     Open Access   (Followers: 10)
Advances in Heat Transfer     Full-text available via subscription   (Followers: 21)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 9)
Advances in Natural Sciences: Nanoscience and Nanotechnology     Open Access   (Followers: 28)
Advances in Operations Research     Open Access   (Followers: 11)
Advances in OptoElectronics     Open Access   (Followers: 5)
Advances in Physics Theories and Applications     Open Access   (Followers: 12)
Advances in Polymer Science     Hybrid Journal   (Followers: 41)
Advances in Porous Media     Full-text available via subscription   (Followers: 4)
Advances in Remote Sensing     Open Access   (Followers: 37)
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: 30)
Ain Shams Engineering Journal     Open Access   (Followers: 5)
Akademik Platform Mühendislik ve Fen Bilimleri Dergisi     Open Access  
Alexandria Engineering Journal     Open Access   (Followers: 1)
AMB Express     Open Access   (Followers: 1)
American Journal of Applied Sciences     Open Access   (Followers: 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: 6)
Applied Catalysis B: Environmental     Hybrid Journal   (Followers: 15)
Applied Clay Science     Hybrid Journal   (Followers: 5)
Applied Computational Intelligence and Soft Computing     Open Access   (Followers: 12)
Applied Magnetic Resonance     Hybrid Journal   (Followers: 4)
Applied Nanoscience     Open Access   (Followers: 8)
Applied Network Science     Open Access  
Applied Numerical Mathematics     Hybrid Journal   (Followers: 5)
Applied Physics Research     Open Access   (Followers: 3)
Applied Sciences     Open Access   (Followers: 2)
Applied Spatial Analysis and Policy     Hybrid Journal   (Followers: 4)
Arabian Journal for Science and Engineering     Hybrid Journal   (Followers: 5)
Archives of Computational Methods in Engineering     Hybrid Journal   (Followers: 4)
Archives of Foundry Engineering     Open Access  
Archives of Thermodynamics     Open Access   (Followers: 7)
Arkiv för Matematik     Hybrid Journal   (Followers: 1)
ASEE Prism     Full-text available via subscription   (Followers: 3)
Asian Engineering Review     Open Access  
Asian Journal of Applied Science and Engineering     Open Access   (Followers: 1)
Asian Journal of Applied Sciences     Open Access   (Followers: 2)
Asian Journal of Biotechnology     Open Access   (Followers: 8)
Asian Journal of Control     Hybrid Journal  
Asian Journal of Current Engineering & Maths     Open Access  
Asian Journal of Technology Innovation     Hybrid Journal   (Followers: 8)
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
ATZagenda     Hybrid Journal  
ATZextra worldwide     Hybrid Journal  
Australasian Physical & Engineering Sciences in Medicine     Hybrid Journal   (Followers: 1)
Australian Journal of Multi-Disciplinary Engineering     Full-text available via subscription   (Followers: 2)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 8)
Avances en Ciencias e Ingeniería     Open Access  
Balkan Region Conference on Engineering and Business Education     Open Access   (Followers: 1)
Bangladesh Journal of Scientific and Industrial Research     Open Access  
Basin Research     Hybrid Journal   (Followers: 5)
Batteries     Open Access   (Followers: 4)
Bautechnik     Hybrid Journal   (Followers: 1)
Bell Labs Technical Journal     Hybrid Journal   (Followers: 23)
Beni-Suef University Journal of Basic and Applied Sciences     Open Access   (Followers: 4)
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: 10)
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: 17)
Biomedical Engineering, IEEE Transactions on     Hybrid Journal   (Followers: 31)
Biomedical Engineering: Applications, Basis and Communications     Hybrid Journal   (Followers: 5)
Biomedical Microdevices     Hybrid Journal   (Followers: 9)
Biomedical Science and Engineering     Open Access   (Followers: 4)
Biomedizinische Technik - Biomedical Engineering     Hybrid Journal  
Biomicrofluidics     Open Access   (Followers: 4)
BioNanoMaterials     Hybrid Journal   (Followers: 2)
Biotechnology Progress     Hybrid Journal   (Followers: 39)
Boletin Cientifico Tecnico INIMET     Open Access  
Botswana Journal of Technology     Full-text available via subscription  
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     Hybrid Journal   (Followers: 14)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 41)
Case Studies in Engineering Failure Analysis     Open Access   (Followers: 8)
Case Studies in Thermal Engineering     Open Access   (Followers: 3)
Catalysis Communications     Hybrid Journal   (Followers: 6)
Catalysis Letters     Hybrid Journal   (Followers: 2)
Catalysis Reviews: Science and Engineering     Hybrid Journal   (Followers: 8)
Catalysis Science and Technology     Free   (Followers: 6)
Catalysis Surveys from Asia     Hybrid Journal   (Followers: 3)
Catalysis Today     Hybrid Journal   (Followers: 8)
CEAS Space Journal     Hybrid Journal  
Cellular and Molecular Neurobiology     Hybrid Journal   (Followers: 3)
Central European Journal of Engineering     Hybrid Journal   (Followers: 1)
CFD Letters     Open Access   (Followers: 6)
Chaos : An Interdisciplinary Journal of Nonlinear Science     Hybrid Journal   (Followers: 2)
Chaos, Solitons & Fractals     Hybrid Journal   (Followers: 3)
Chinese Journal of Catalysis     Full-text available via subscription   (Followers: 2)
Chinese Journal of Engineering     Open Access   (Followers: 2)
Chinese Science Bulletin     Open Access   (Followers: 1)
Ciencia e Ingenieria Neogranadina     Open Access  
Ciencia en su PC     Open Access   (Followers: 1)
Ciencias Holguin     Open Access   (Followers: 1)
CienciaUAT     Open Access  
Cientifica     Open Access  
CIRP Annals - Manufacturing Technology     Full-text available via subscription   (Followers: 11)
CIRP Journal of Manufacturing Science and Technology     Full-text available via subscription   (Followers: 14)
City, Culture and Society     Hybrid Journal   (Followers: 21)
Clay Minerals     Full-text available via subscription   (Followers: 10)
Clean Air Journal     Full-text available via subscription   (Followers: 2)
Coal Science and Technology     Full-text available via subscription   (Followers: 3)
Coastal Engineering     Hybrid Journal   (Followers: 11)
Coastal Engineering Journal     Hybrid Journal   (Followers: 5)
Coatings     Open Access   (Followers: 3)
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: 26)
Composite Interfaces     Hybrid Journal   (Followers: 6)
Composite Structures     Hybrid Journal   (Followers: 259)
Composites Part A : Applied Science and Manufacturing     Hybrid Journal   (Followers: 181)
Composites Part B : Engineering     Hybrid Journal   (Followers: 236)
Composites Science and Technology     Hybrid Journal   (Followers: 216)
Comptes Rendus Mécanique     Full-text available via subscription   (Followers: 2)
Computation     Open Access  
Computational Geosciences     Hybrid Journal   (Followers: 14)
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: 28)
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: 10)
Computing and Visualization in Science     Hybrid Journal   (Followers: 5)
Computing in Science & Engineering     Full-text available via subscription   (Followers: 30)
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: 9)
Control Engineering Practice     Hybrid Journal   (Followers: 42)
Control Theory and Informatics     Open Access   (Followers: 8)
Corrosion Science     Hybrid Journal   (Followers: 25)
CT&F Ciencia, Tecnologia y Futuro     Open Access  
CTheory     Open Access  
Current Applied Physics     Full-text available via subscription   (Followers: 4)
Current Science     Open Access   (Followers: 58)

        1 2 3 4 5 6 7 | 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  [3044 journals]
  • Global sensitivity and uncertainty analysis of nitrate leaching and crop
           yield simulation under different water and nitrogen management practices
    • Abstract: Publication date: November 2017
      Source:Computers and Electronics in Agriculture, Volume 142, Part A
      Author(s): Hao Liang, Zhiming Qi, Kendall C. DeJonge, Kelin Hu, Baoguo Li
      Assessing the sensitivity and uncertainty of soil-crop models is beneficial to model calibration and development of best water and N management practices. This study adopted the Morris screening method and the Sobol’ variance-based method, combined with an agricultural system model (WHCNS), to analyze the global sensitivity and uncertainty of nitrate leaching and crop yield to model input parameters under different water and N management practices. A two-year field experiment was conducted in a desert oasis of Inner Mongolia, China using a factorial combination of standard (Istd, standard, 750mm per season; Nstd, standard, 138kgNha−1) and conservation (Icsv, conservation, 570mm per season; Ncsv, conservation, 92kgNha−1) levels of irrigation and N fertilization: IstdNstd, IstdNcsv, IcsvNstd and IcsvNcsv. Sensitivity analysis (SA) based on this experiment showed that nitrate leaching demonstrated significant sensitivity to soil hydraulic and crop parameters, but generally low sensitivity to N transformation parameters. Based on Sobol’ SA, crop parameters accounted for 64.3%, 63.2%, 39.2% and 39.2% of simulated nitrate leaching variability for the IstdNstd, IstdNcsv, IcsvNstd and IcsvNcsv treatments, respectively. The greater the crop water and N stress, the stronger the parameters interaction. Uncertainty analysis showed the average amount of nitrate leaching under Istd (135.3kgNha−1) to be 2.3 times greater than under Icsv (58.0kgNha−1); however, the distributions of yield between the four treatment combinations did not show significant differences. Overall, irrigation practice was the main factor influencing the parameter sensitivities and the uncertainty of nitrate leaching and crop yield simulation.

      PubDate: 2017-09-20T02:29:01Z
  • Development of a web application for estimating carbon footprints of
           organic farms
    • Abstract: Publication date: November 2017
      Source:Computers and Electronics in Agriculture, Volume 142, Part A
      Author(s): B.R. Carlson, L.A. Carpenter-Boggs, S.S. Higgins, R. Nelson, C.O. Stöckle, J. Weddell
      Organic farmers often use complex management practices to foster a positive impact on the environment. Many tools exist to aid in estimating environmental services but few are able to properly handle the complexities of organic agriculture. We developed an online tool called OFoot to estimate the carbon footprint of organic farms located in the Pacific Northwest and to help evaluate the potential for environmental benefits. OFoot utilizes a cradle-to-gate carbon calculator and a biophysical, process-based, cropping and field management model. We present the software architecture of the tool, model descriptions, and a case study which simulates several scenarios of organic potato production. The scenario simulating potato production with organic fertilizers and a leguminous winter cover crop sequestered soil carbon. The other scenarios, either lacking fertilizer or cover crops, lost soil carbon. The usefulness of the tool as an aid to management decisions is demonstrated.

      PubDate: 2017-09-20T02:29:01Z
  • Using chlorophyll a fluorescence gains to optimize LED light spectrum for
           short term photosynthesis
    • Abstract: Publication date: November 2017
      Source:Computers and Electronics in Agriculture, Volume 142, Part A
      Author(s): Linnéa Ahlman, Daniel Bånkestad, Torsten Wik
      When changing from the traditional high pressure sodium (HPS) lamps to light emitting diode (LED) lamps there is a quite unexplored energy saving potential in the fact that they are far better suited for control, since both spectrum and light intensity can be adjusted. This work aims at finding a way to automatically adjust the spectrum of a LED lamp, equipped with several different types of LEDs, to maximize plant growth by feedback of a remote online measure correlated with growth. A series of experiments were conducted on basil plants in order to examine whether remotely sensed steady-state chlorophyll fluorescence (F740) can be used for this purpose, and if its derivatives (fluorescence gains) w.r.t. applied powers change relative to each other for different light intensities and spectra. A strong correlation between F740 and photosynthetic rate was indeed found. However, the order (w.r.t. LED type) of the fluorescence gains was only moderately affected by the light intensities and spectra investigated. The gain was highest w.r.t. red light (630nm), though, when taking the electrical efficiencies of individual LED types into consideration, blue LEDs (450nm) were equally, or even more efficient than the red ones. An online controller to regulate optimal spectrum for basil appears to be unnecessary. However, the fluorescence gains could be used to adapt to changes in the efficiencies when crops and operating conditions change, or when the diodes degrade. The method also shows promise as a tool to find optimal light intensity levels as well as identifying plant stress.

      PubDate: 2017-09-20T02:29:01Z
  • On-the-field simulation of fertilizer spreading: Part 1 – Modeling
    • Abstract: Publication date: November 2017
      Source:Computers and Electronics in Agriculture, Volume 142, Part A
      Author(s): E.-M. Abbou-ou-cherif, E. Piron, A. Chateauneuf, D. Miclet, R. Lenain, J. Koko
      The field elevation and its variation represent a disturbance in the spreading process that is not handled yet by centrifugal spreaders. This stems in part from the knowledge gap regarding the possible application errors of fertilizer on non-flat fields. To address this issue, a new model has been developed, integrating both the field elevation and the tractor motion. The model was employed in the paper (“On-the-field simulation of fertilizer spreading: Part 2 – Uniformity investigation”). The model was based on transformation matrices to update the initial conditions of the ballistic flight of particles in the field coordinate system at each new position of the tractor, as it moves along a given trajectory on a given DEM (digital elevation model). An experimental validation was conducted using a radial bench in different static configurations, which also provided the unknown input data for the model. High correlation coefficients were found between the characteristics of the simulated and measured spread patterns, even where, in the simulation, the model parameters were fixed and the spreader inclination varied. Thus, in addition to proving the reliability of the model, the measurements also helped determining the limits of validity of the assumptions within which on-the-field simulations can be carried out.

      PubDate: 2017-09-20T02:29:01Z
  • Real-time segmentation of strawberry flesh and calyx from images of
           singulated strawberries during postharvest processing
    • Abstract: Publication date: November 2017
      Source:Computers and Electronics in Agriculture, Volume 142, Part A
      Author(s): A. Durand-Petiteville, S. Vougioukas, D.C. Slaughter
      This paper presents an image processing algorithm that automatically extracts the flesh and calyx areas from strawberry images. Images are captured by a camera included in a strawberry de-capping machine. Lighting is controlled and the background is known, conditions that are typical of postharvest processing. The goal is to extract as many flesh and calyx pixels as possible while rejecting any pixels belonging to the background. The proposed approach relies on image color segmentation in a two-dimensional color space, followed by a blob detection and selection stage. A set of 250 images is used to analyze the sensitivity of the algorithm with respect to user-defined parameters, and evaluate the performance of the approach. The algorithm appears to be easy to tune and allows accurate extraction of the areas of interest despite natural variation in strawberry shape and visual appearance. More than 98 % of the flesh area was successfully extracted by the algorithm with less than 1 % of the background pixels falsely included. Moreover, up to 79 % of the calyx area could be extracted with less than 0.25 % erroneous background pixels. Finally, the algorithm has been implemented using the C++ and Cuda languages and can be executed in real-time.

      PubDate: 2017-09-20T02:29:01Z
  • Development of high-speed camera hardware and software package to evaluate
           real-time electric seed meter accuracy of a variable rate planter
    • Abstract: Publication date: November 2017
      Source:Computers and Electronics in Agriculture, Volume 142, Part A
      Author(s): Devin L. Mangus, Ajay Sharda, Daniel Flippo, Ryan Strasser, Terry Griffin
      Electric drive seed metering systems have become common for planting row-crop seed to accommodate increased machine size and planting speeds and to allow individual row unit-control that enable site-specific planting for spatially sensitive areas and contour farming. Seed singulation (a measurement of singulated seeds, misses, and multiples) is critical requirement when adopting high speed planting. However, current planting controllers fail to indicate whether singulation errors occurred due to operator-based behaviors such as speed changes, headland operation, point rows and contour farming at varying speed transitions (accelerations/decelerations). Therefore this study was conducted to understand a seed metering system’s ability to singulate seed under typical scenarios with specific objectives to (1) quantify electric seed metering accuracy using high-speed imaging and (2) identify machine operating states that impact seeding accuracy. A Horsch Maestro 24.30 planter was sent commands to plant at constant speeds of 7.2, 9.7, 12.0kph while accelerating/decelerating at 2.4 and 4.8kph/s from/to a stop and between speeds. The planter was sent commands to plant around contours at varying radii (20, 40, 80, 150m) at varying speeds (i.e., 0, 2.4, 4.8, 6.4, 7.2, 9.7, 12.1, 12.9, 14.5, 16.1kph). Simulations were conducted at two rates (44,550 and 89,110seedsha−1). A high-speed imaging system was developed using LabVIEW to record real-time seed meter singulation at 300frames/s by combining planting machine states with seed tube sensor data and vision based seed measurements to quantify single count seeds, misses, and multiples. When planting from 2.4kph to 16.1kph, results showed an average singulation of 98.45% where errors nearly doubled with fast accelerations and decelerations and abrupt changes such as a shift during headland turns. Overall, planting above 1250seeds per minute resulted in an increased number of singulation errors. The vision based measurements were within 0.8±0.2% of the commercial seed tube sensors. The seed per minute value which provided optimal seed singulation can be used as a control parameter by technology users and manufactures to select optimal operating parameters to achieve target singulation rates. The methodology provided optimal machine conditions and operator behaviors to achieve a target percent singulation by identifying scenarios which increase singulation by minimizing misses and multiples.

      PubDate: 2017-09-20T02:29:01Z
  • A practical approach to comparative design of non-contact sensing
           techniques for seed flow rate detection
    • Abstract: Publication date: November 2017
      Source:Computers and Electronics in Agriculture, Volume 142, Part A
      Author(s): Hadi Karimi, Hossein Navid, Bahram Besharati, Hossein Behfar, Iraj Eskandari
      This study, as a part of a broader research project on development of a seed drill performance monitoring system, seeks to make a practical approach to comparative design of non-contact sensing techniques for seed flow rate detection. To determine actual flow rate, various non-contact sensing techniques have been proposed by other researchers. The methods of light dependent resistors (LDR), infrared (IR), and laser diodes (LD) drew more attention. LD, IR, and LDR sensing units were successfully designed and developed. Each of these sensors has a type of LEDs, consist of infrared, visible light and laser-LED as well as an element as a radiation receiver. When the seeds pass through the seed sensor and through the band of light beams, their shades fall on the receiver elements, resulting in output voltage changes. Thus, the seed flow rate could be estimated by investigating signal information corresponding to the passing seeds. A particular test apparatus was designed to compare proposed sensing units ability in confronting with the same seed flow. For each seed flow rate in experiments, individual LDR, IR, and LD, pulse signals and discharged seeds mass were recorded. Results show that there is a strong linear relationship (r=0.87) between the actual seed mass changes and the corresponding voltages of IR sensing unit. Due to obtained results in comparison with other investigated sensing methods, it is recommended that IR detection technique is a more proper non-contact sensing technique for estimating of the seed flow rate.

      PubDate: 2017-09-13T14:32:25Z
  • Dispersion and migration of agricultural particles in a variable-amplitude
           screen box based on the discrete element method
    • Abstract: Publication date: November 2017
      Source:Computers and Electronics in Agriculture, Volume 142, Part A
      Author(s): Zheng Ma, Yaoming Li, Lizhang Xu, Jin Chen, Zhan Zhao, Zhong Tang
      Many relevant studies about screening of agriculture materials have been conducted from different perspectives such as mechanism design, optimization of parameters, and particle motion. Some studies suggest that a screen driven by parallel mechanisms is more adaptable than a traditional one while the traditional reciprocating screen still plays a positive role in agricultural production. Based on previous research regarding the variable-amplitude screening method, 4 indices of particle movement have been defined and computed in this paper to investigate the quantitative dispersion and migration characteristics of agricultural particles by using DEM (Discrete Element Method) simulation data. It shows that the turning angle of the front swing bar has a significant effect on the horizontal expansion coefficient δ x ( t ) , and the particles will be thin quickly at first and be stabilize within the next process. It also shows that the increase in the turning angle of the front swing bar leads to a negative stratification effect, but results in a positive migration effect of all particles on the screen. This research could provide a useful reference for solving the retention problem of agricultural particles in any position on the screen.

      PubDate: 2017-09-13T14:32:25Z
  • Morphometric analysis of stream as one of resources for agricultural lands
    • Abstract: Publication date: November 2017
      Source:Computers and Electronics in Agriculture, Volume 142, Part A
      Author(s): Marzieh Mokarram, Majid Hojati
      The aim of the study in this area is the extraction of streams using a sub-pixel spatial attraction model as a new method for increasing spatial resolution of digital elevation model (DEM) in the north of Darab, Fars province, Iran. The sub-pixel attraction models convert the pixel to sub-pixels based on the fraction values in neighboring pixels that can be attracted only by a central pixel. Based on this approach only a maximum of eight neighboring pixels can be selected for the attraction. In the model, other pixels are supposed to be far from the central pixel to have any attraction. In this study, the spatial resolution of digital elevation models (DEM) was increased by using sub-pixel attraction model. The design of the algorithm is accomplished through using digital elevation model (DEM) with spatial resolution of 30m (Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER)) and 90m (Shuttle Radar Topography Mission (SRTM)) in the north of Darab, Fars province, Iran. In attraction model scale factors of (2,3,4) with two neighboring methods of touching and quadrant are applied to DEMs using Matlab. The algorithm is evaluated using 487 sample points that are measured by researchers. As the result, it was revealed that the spatial attraction model with scale factor of (S=2) gives better results compared to scale factors that are greater than 2 and also touching neighborhood method proved to be more accurate then quadrant. In fact, subtracting each pixel to more than two sub-pixels caused to the decrease of the accuracy of resulted DEM which leads to the increase of the value of root-mean-square error (RMSE) and showed that attraction models could not be used for S which is greater than 2. So according to the results, it is suggested that the model can be used for increasing spatial resolution of DEM in the studies catchment.

      PubDate: 2017-09-13T14:32:25Z
  • Using centers of pressure tracks of sows walking on a large force platform
           in farm conditions for locomotion classification
    • Abstract: Publication date: November 2017
      Source:Computers and Electronics in Agriculture, Volume 142, Part A
      Author(s): L. Puigdomenech, J.R. Rosell-Polo, G. Blanco, D. Babot
      This study examines the feasibility of using a 3.0-m-long, 1.5-m-wide force platform to group into clusters the centers of the ground pressure tracks of sows walking on it. The clusters were created according to variables related to the symmetry and cadence of the sows’ locomotion, and permitted an evaluation of its soundness in each cluster. Observations were made in a swine-breeding farm that followed standard swine production practices. In the farm, the sows were moved when farrowing from the gestation stalls to the farrowing crates, and were then returned to the service stalls. On these occasions, as recorded over the course of six months, each sow separately passed through a corridor connecting the two rooms, which is where the force platform was placed. The sows were not trained for this task. Signals were separately extracted from four load cells located under the platform, and were processed to obtain the center of pressure (CoP) and the vertical ground reaction force (F) of each sow as it walked on the platform (322 CoP tracks). The trajectory of each sow was derived from the generated CoP track. A gait cycle was considered complete when the CoP track oscillated (swayed) once in the plane of transversal of the sow’s trajectory. In each gait cycle, the following variables were calculated: mean velocity, normalized impulse balance per gait cycle, number of relevant peaks of F per gait cycle, and peak ratio obtained from the autocorrelation function of F. Using these variables, all CoP tracks were classified into three clusters (p<0.05). The relationships among the variables in each cluster allowed for distinction among the CoP tracks in terms of the soundness of locomotion. No significant differences in the measured variables were observed between the CoP tracks of primiparous and multiparous sows, whereas sows entering the farrowing crates were found to walk more slowly (p<0.05) and with less balance (p=0.063) than when leaving it. Considering intraclass correlation coefficient of the variables per CoP track as an indicator of locomotive soundness, the cluster of the fastest and most balanced CoP tracks yielded significantly more reliable impulse balance (p<0.05) responses than did the other clusters. More reliable impulse balance was also observed in CoP tracks made by multiparous sows than by primiparous ones, and by the sows leaving than those entering the farrowing crates (p<0.05).

      PubDate: 2017-09-07T14:23:09Z
  • A segmentation method for greenhouse vegetable foliar disease spots images
           using color information and region growing
    • Abstract: Publication date: November 2017
      Source:Computers and Electronics in Agriculture, Volume 142, Part A
      Author(s): Juncheng Ma, Keming Du, Lingxian Zhang, Feixiang Zheng, Jinxiang Chu, Zhongfu Sun
      This paper presents a novel image processing method using color information and region growing for segmenting greenhouse vegetable foliar disease spots images captured under real field conditions. Disease images captured under real field conditions are suffering from uneven illumination and complicated background, which is a big challenge to achieve robust disease spots segmentation. A disease spots segmentation method consisting of two pipelined procedures is proposed in this paper. Firstly a comprehensive color feature and its detection method are presented. The comprehensive color feature (CCF) consists of three color components, Excess Red Index (ExR), H component of HSV color space and b ∗ component of L ∗ a ∗ b ∗ color space, which implements powerful discrimination of disease spots and clutter background. Then an interactive region growing method based on the CCF map is used to achieve disease spots segmentation from clutter background. To evaluate the robustness and accuracy, the proposed segmentation method is assessed by cucumber downy mildew images. Results show that the proposed method can achieve accurate and robust segmentation under real field conditions.

      PubDate: 2017-09-07T14:23:09Z
  • Robust model predictive control of the automatic operation boats for
    • Abstract: Publication date: November 2017
      Source:Computers and Electronics in Agriculture, Volume 142, Part A
      Author(s): Jun Zhang, Tairen Sun, Dean Zhao, Jianqing Hong, Yueping Sun
      This paper proposes a robust model predictive control (RMPC) approach for the automatic operation boats to cast baits evenly along desired paths. The difficulties in the control design come from the control system model, which is nonlinear, underactuated, input saturated, and disturbed by time-varying signals. The RMPC overcomes these difficulties by the receding horizon optimization explicitly considering the input saturation and using the mixed H 2 / H ∞ cost function. To decrease computational complexity of the RMPC, a polyhedral model is constructed as the predictive model based on dynamics of the path-following error. The feasibility and effectiveness of the proposed path-following control is verified by theoretical analysis and illustrated by simulations and experiments.

      PubDate: 2017-09-07T14:23:09Z
  • Multiple camera fruit localization using a particle filter
    • Abstract: Publication date: November 2017
      Source:Computers and Electronics in Agriculture, Volume 142, Part A
      Author(s): S.S. Mehta, C. Ton, S. Asundi, T.F. Burks
      Apart from socioeconomic factors, success of robotics in agriculture lies in developing economically attractive solutions with efficiency comparable to that of the humans. Fruit localization is one of the building blocks in many robotic agricultural operations (e.g., yield mapping and robotic harvesting) that determines 3D Euclidean positions of the fruits using one or several sensors. It is crucial to guarantee the performance of the localization methods in the presence of fruit detection errors and unknown fruit motion (e.g., due to wind gust), so that the desired efficiency of the subsequent systems can be achieved. For instance, inaccurate localization may severely affect fruit picking efficiency in robotic harvesting. The presented estimation-based localization approach provides estimates of the fruit positions in the presence of fruit detection errors and unknown fruit motion, and it is based on a new sensing procedure that uses multiple ( ⩾ 2 ) inexpensive monocular cameras. A nonlinear estimator called particle filter is developed to estimate the unknown position of the fruits using image measurements obtained from multiple cameras. The particle filter is partitioned into clusters to independently localize individual fruits, while the behavior of the clusters is manipulated at global level to maintain a single filter structure. Since the accuracy of localization is affected by errors in fruit detection, the presented sensor model includes non-Gaussian fruit detection errors along with image noise. Fruit motion can significantly reduce harvesting efficiency due to errors in locating moving fruits. In contrast to existing methods, the dynamics of fruit motion are derived and included in the localization framework to obtain time-varying position estimates of the moving fruits. A detailed theoretical foundation is provided for the new estimation-based fruit localization approach, and it is validated through extensive Monte Carlo simulations. The performance of the estimator is evaluated by varying the design parameters, measurement noise, number of fruits, amount of overlap in clustered fruit scenarios, and fruit velocity. Correlation of these parameters with the performance of the estimator is derived, and guidelines are presented for selecting the design parameters and predicting performance bounds under given operating conditions.

      PubDate: 2017-09-07T14:23:09Z
  • Crowdsourcing for agricultural applications: A review of uses and
           opportunities for a farmsourcing approach
    • Abstract: Publication date: November 2017
      Source:Computers and Electronics in Agriculture, Volume 142, Part A
      Author(s): Julien Minet, Yannick Curnel, Anne Gobin, Jean-Pierre Goffart, François Mélard, Bernard Tychon, Joost Wellens, Pierre Defourny
      Crowdsourcing, understood as outsourcing tasks or data collection by a large group of non-professionals, is increasingly used in scientific research and operational applications. In this paper, we reviewed crowdsourcing initiatives in agricultural science and farming activities and further discussed the particular characteristics of this approach in the field of agriculture. On-going crowdsourcing initiatives in agriculture were analysed and categorised according to their crowdsourcing component. We identified eight types of agricultural data and information that can be generated from crowdsourcing initiatives. Subsequently we described existing methods of quality control of the crowdsourced data. We analysed the profiles of potential contributors in crowdsourcing initiatives in agriculture, suggested ways for increasing farmers’ participation, and discussed the on-going initiatives in the light of their target beneficiaries. While crowdsourcing is reported to be an efficient way of collecting observations relevant to environmental monitoring and contributing to science in general, we pointed out that crowdsourcing applications in agriculture may be hampered by privacy issues and other barriers to participation. Close connections with the farming sector, including extension services and farm advisory companies, could leverage the potential of crowdsourcing for both agricultural research and farming applications. This paper coins the term of farmsourcing asa professional crowdsourcing strategy in farming activities and provides a source of recommendations and inspirations for future collaborative actions in agricultural crowdsourcing.

      PubDate: 2017-09-07T14:23:09Z
  • Determination of apple bruise resistance based on the surface pressure and
           contact area measurements under impact loads
    • Abstract: Publication date: November 2017
      Source:Computers and Electronics in Agriculture, Volume 142, Part A
      Author(s): Piotr Komarnicki, Roman Stopa, Łukasz Kuta, Daniel Szyjewicz
      The bruises as well as other affects in fruit quality cause lower selling prices and generate loss for fruit growers. For proper identification of damages in biological material, the impact load, contact pressure as well as force affecting on apple skin should be examined. In this paper, the authors present an experimental method in assessing of the bruise resistance as well as the bruise threshold for the 'Gala' apple cultivar consisting a relationship between the impact loads during free drop against four rigid surfaces. The authors measured the contact surface between tested fruit and fixed material to determine the bruise resistance. The Tekscan® measuring system was applied to determine the contact surface and the surface pressures at the moment of collision during impact test. Damaged tissue was photographed and subjected to the computer image analysis in order to determine the bruise volume. In this study, the bruise resistance index (BRI) as relationship between determined bruise volume and surface pressure at varying drop heights was presented. Due to the difficulty and time–consuming process of the bruise volume evaluation, the authors decided to replace commonly used method with functional relation of the contact surface. It allowed for assessing the alternative BRI c indicator which was based on the relationship between the surface pressure and the contact surface. Both the indicators as well as verifying linear regression analysis showed, that proposed BRI c power model with high precision describes power curves of the BRI indicator. From conducted analysis results that the BRI c indicator allows for the determination of the bruise resistance for the 'Gala' apple cultivar and precisely describes the BRI indicator. Based on the BRI c curves and changes in the bruise area–drop height relationship, a graphical method in assessment of the bruise resistance and the bruise threshold was proposed. The presented method can be used as effective tool in mechanical damage assessing.

      PubDate: 2017-09-07T14:23:09Z
  • Model updating for the classification of different varieties of maize
           seeds from different years by hyperspectral imaging coupled with a
           pre-labeling method
    • Abstract: Publication date: November 2017
      Source:Computers and Electronics in Agriculture, Volume 142, Part A
      Author(s): Dongsheng Guo, Qibing Zhu, Min Huang, Ya Guo, Jianwei Qin
      The use of hyperspectral imaging technology combined with chemometrics is an effective nondestructive method for sorting seed varieties. However, the performance of the method is susceptible to the influence of time and depends on the training set used in the modeling process. The accuracy of classification models maybe deteriorate when they are used to differentiate the same variety of seeds harvested in different years, due to new variances in the test set are introduced by changes in the cultivation conditions, soil environmental conditions and climatic changes from one year to another. To maintain the accuracy and robustness of model, a model-updating algorithm for differentiating maize seed varieties from different years based on hyperspectral imaging coupled with a pre-labeling method was proposed in this work. The pre-label of each unlabeled sample was obtained using the original classification models developed by the least squares support vector machine classifier. The representative unlabeled samples, which had reliable pre-labels, were selected for updating classification models based on Pearson correlation coefficients. After model updating, the average classification accuracies were improved by 8.9%, 35.8% and 9.6%, compared with those of non-updated models for three test sets, respectively. This shows the effectiveness of the proposed method for classifying maize seeds of different years.

      PubDate: 2017-09-02T10:48:33Z
  • Development of a single energy balance model for prediction of
           temperatures inside a naturally ventilated greenhouse with polypropylene
           soil mulch
    • Abstract: Publication date: November 2017
      Source:Computers and Electronics in Agriculture, Volume 142, Part A
      Author(s): Audberto Reyes-Rosas, Francisco D. Molina-Aiz, Diego L. Valera, Alejandro López, Sasirot Khamkure
      In this study, a semi-empirical dynamic model of energy balance was developed to predict temperatures (air, plants, greenhouse cover and soil) in a naturally ventilated greenhouse with a polypropylene mulch covering the soil in a Mediterranean climate. The model was validated using experimental data of 5 non-successive periods of 5days throughout the crop season in the province of Almería (Spain). During the evaluation period, the transmissivity of the cover ranged between 0.44 and 0.80 depending on whitening, and the leaf area index of the tomato crops growing inside the greenhouse varied from LAI =0.74 to 1.30 m2 m−2. The model mainly consists of a system of 6 non-linear differential equations of energy conservation at inside air, greenhouse plastic cover, polypropylene mulch and three layers of soil. We used multiple linear regressions to estimate the crop temperature in a simple way that allows a reduction in the number of parameters required as input. The main components of the energy balance in warm climate conditions are the solar radiation, the heat exchanged by natural ventilation and the heat stored in the soil. To improve the estimation of the heat exchanged by ventilation, different discharge coefficients were used for roof CdVR and side openings CdVS . Both coefficients changed throughout the time as a function of the height and opening angle of the windows and of the air velocity across the insect-proof screens. The model also used different wind effect coefficients Cw for Northeast or Southwest winds, to take into account the different obstacles (a neighbouring greenhouse at the south and a warehouse at the north). A linear regression of the wind direction angle θw was used as correction function for the volumetric ventilation flux G. The results showed that the accuracy of the model is affected mainly by errors in the cover transmissivity on cloudy days (when diffuse radiation prevails) and errors in the temperature of air exiting the greenhouse on windy days (when hot air stagnated near roof openings, that were closed by the climate controller to avoid wind damage). In general, the results of validation comparing calculated values with those measured on 25days (with relative root mean square errors below 10%), show sufficient accuracy for the model to be used to estimate air, crop, plastic cover, polypropylene mulch and soil temperatures inside the greenhouse, and as a design tool to optimise the ventilation system characteristics and control settings.
      Graphical abstract image

      PubDate: 2017-09-02T10:48:33Z
  • The use of optical coherence tomography for the evaluation of textural
           changes of grapes exposed to pulsed electric field
    • Abstract: Publication date: November 2017
      Source:Computers and Electronics in Agriculture, Volume 142, Part A
      Author(s): Jarosław Gocławski, Joanna Sekulska-Nalewajko, Ewa Korzeniewska, Agnieszka Piekarska
      Application of pulsed electric fields (PEF) to food is a nonthermal technology of food processing. The short pulses of high intensity electric field can modify the internal structures of fruits and vegetables by affecting the cell permeability. In the presented study three popular grape wine cultivars – Johanniter, Hibernal and Marechal Foch were exposed to PEF at electric field strengths of 3.3kV/cm and 5kV/cm. The significant textural changes of near peel grape layers influenced by electric field were observed in optical coherence tomography (OCT) images, using infrared light of 1300nm. These changes were expressed by the variation of entropy, standard deviation or lacunarity features and evaluated in a dedicated software tool developed by the authors in Matlab environment. The OCT is a non-destructive technique in which no sample preparation is needed and grapes still remain intact (undamaged) during imaging. The OCT cross-sections revealed the progressive process of expanding zone with strong echo in sub-peel layers what may indicate cell permeabilization or even loosing parenchyma cells integrity. Also grape surface deformations under PEF were quantified. It has been shown that the values of considered textural features in near peel grape tissue were related to the intensity of electric field. Marechal Foch cultivar appeared to be more resistant to PEF than two other grape varieties.

      PubDate: 2017-09-02T10:48:33Z
  • A robust algorithm based on color features for grape cluster segmentation
    • Abstract: Publication date: November 2017
      Source:Computers and Electronics in Agriculture, Volume 142, Part A
      Author(s): Nasser Behroozi-Khazaei, Mohammad Reza Maleki
      Image processing has been widely used for automation purposes in modern agriculture. The algorithm development for the image segmentation is the most controversial and challenging issue in orchard environment which researchers encounter. This paper describes a robust algorithm based on artificial neural network (ANN) and genetic algorithm (GA) for segmenting grape clusters from leaves and background using color features near to harvest. GA was employed for optimizing of ANN structure and selecting supreme color features simultaneously. The results showed that GA specifies the 8 color features as supreme features and define 8–15-35–3 as the best structure of the ANN. The overall accuracy of the algorithm was 99.40%. The promising results in algorithm development described in this study lead to introduce it as a practical sensing tool in precision agriculture as well as those industrial facilities dealing with image analysis.

      PubDate: 2017-09-02T10:48:33Z
  • Value of dimensionality reduction for crop differentiation with
           multi-temporal imagery and machine learning
    • Abstract: Publication date: November 2017
      Source:Computers and Electronics in Agriculture, Volume 142, Part A
      Author(s): Jason Kane Gilbertson, Adriaan van Niekerk
      This study evaluates the use of automated and manual feature selection – prior to machine learning – for the differentiation of crops in a Mediterranean climate (Western Cape, South Africa). Five Landsat-8 images covering the different crop class phenological stages were acquired and used to generate a range of spectral and textural features within an object-based image analysis (OBIA) paradigm. The features were used as input to decision trees (DTs), k-nearest neighbour (k-NN), support vector machine (SVM), and random forest (RF) supervised classifiers. Testing was done by performing classifications (using all spatial variables) and then incrementally reducing the feature counts (based on importance allocated to features by filters), feature extraction, and manual (semantic) feature selection. Classification and regression trees (CART) and RF were used as methods to filter feature selection. Feature-extraction methods employed include principal components analysis (PCA) and Tasselled cap transformation (TCT). The classification results were analysed by comparing the overall accuracies and kappa coefficients of each scenario, while McNemar’s test was used to assess the statistical significance of differences in accuracies among classifiers. Feature selection was found to improve the overall accuracies of the DT, k-NN, and RF classifications, but reduced the accuracy of SVM. The results showed that SVM with feature extraction (PCA) on individual image dates produced the most accurate classification (96.2%). Semantic groupings of features for classification also revealed that using the image bands and indices is not sufficient for crop classification, and that additional features are needed. The accuracy differences of the classifiers were, however, not statistically significant, which suggests that, although dimensionality reduction can improve crop differentiation when multi-temporal Landsat-8 imagery is used, it had a marginal effect on the results. For operational crop-type classification in the study area (and similar regions), we conclude that the SVM algorithm can be applied to the full set of features generated.

      PubDate: 2017-09-02T10:48:33Z
  • Land consolidation of small-scale farms in preparation for a cane
    • Abstract: Publication date: November 2017
      Source:Computers and Electronics in Agriculture, Volume 142, Part A
      Author(s): Wanita Boonchom, Kullapapruk Piewthongngam, Pattarawit Polpinit, Pachara Chatavithee
      The cane cultivation areas of certain countries are primarily composed of small-scale farms. To adopt harvesting machinery efficiently, consolidating these small plots is essential. However, the decision to plough out cane ratoon to synchronize the cultivation process in consolidated land area is complicated because the plots have different cane ages and different ownerships. To address this problem, we develop a mathematical model and a heuristic method based on the greedy algorithm to create a consolidation and plough-out plan. The solution obtained using the heuristic method differs from the optimal solution less than 1.5% for small cases of 5, 10, and 15 cane plots and requires less computational time. The proposed heuristics, when used to solve large-sized problems, suggest a plan with benefits that are approximately 49.39% higher than those of the conventional unsynchronized method. This approach is likely to facilitate consolidation planning for sugar mills and cane growers, resulting in more efficient harvester utilization.

      PubDate: 2017-09-02T10:48:33Z
  • Exponentially smoothed Fujii index for online imaging of biospeckle
           spatial activity
    • Abstract: Publication date: November 2017
      Source:Computers and Electronics in Agriculture, Volume 142, Part A
      Author(s): P.M. Pieczywek, J. Cybulska, A. Zdunek, A. Kurenda
      This paper describes a simple and efficient approach for the real-time evaluation of biospeckle spatial activity using a live video stream. The proposed method combines the exponentially weighted averaging of time-series data with a calculation of the Fujii biospeckle activity index. The exponentially smoothed Fujii method (ESF) was compared with the conventional offline method. A comparison was carried out using the speckle data of apple fruit with a fungal infection. Using data from a model experiment it was shown that the proposed method is capable of producing consistent and reliable results which are comparable with results obtained using the conventional approach. The exponentially smoothed Fujii method (ESF) provided high contrast maps of biospeckle activity without any loss of resolution. Reasonable computational demands made the practical implementation of this method possible using a standard desktop computer.

      PubDate: 2017-09-02T10:48:33Z
  • Development of a multi-robot tractor system for agriculture field work
    • Abstract: Publication date: November 2017
      Source:Computers and Electronics in Agriculture, Volume 142, Part A
      Author(s): Chi Zhang, Noboru Noguchi
      A multi-robot tractor system for conducting agriculture field work was developed in order to reduce total work time and to improve work efficiency. The robot tractors can form a spatial pattern, I-pattern, V-pattern or W-pattern, during the work process. The safety zones of each robot were defined as a circle and a rectangle. The robots can coordinate to turn to the next lands without collision or deadlock. The efficiency of the system depends on the number of robots, the spatial pattern, the setting distance between each robot, and the field length. Three simulations were carried out to determine the usefulness of the system. The simulation results showed that the efficiency range of three robots using the I-pattern is from 83.2% to 89.8% at a field length of 100m. The efficiency range of seven robots using the W-pattern is from 59.4% to 65.8% at a field length of 100m. However, the minimum efficiency of seven robots using the W-pattern is 84.9% at a field length of 500m. The efficiency would be higher than 85% if the field length was larger than 500m. Thus, the newly developed multi-robot tractor system is more effective in a large field.

      PubDate: 2017-09-02T10:48:33Z
  • Comparison of regression methods for spatial downscaling of soil organic
           carbon stocks maps
    • Abstract: Publication date: November 2017
      Source:Computers and Electronics in Agriculture, Volume 142, Part A
      Author(s): P. Roudier, B.P. Malone, C.B. Hedley, B. Minasny, A.B. McBratney
      This paper presents a refinement of the dissever algorithm, a framework for downscaling spatial information based on available environmental covariates proposed by Malone et al. (2012). While the original algorithm models the relationships between the target variable and the covariates using a general additive model (GAM), the modified procedure presented in this paper allows the user to choose between a wide range of regression methods. These developments have been implemented in an open-source package for the R statistical environment, and tested by downscaling soil organic carbon stocks (SOCS) maps available on two study sites in Australia and New Zealand using 4 different regression methods: linear model (LM), GAM, random forest (RF), and Cubist (CU). In this study, the spatial resolution of a set of reference maps were degraded to a coarser resolution, so to assess the performance of the different downscaling methods. On the Australian site, the 1-km SOCS coarse resolution map has been downscaled to a 90-m resolution. The best results were achieved using either CU or RF ( R 2 = 0.91 and 0.94 respectively). On the New Zealand site, the 250-m SOCS coarse resolution map has been downscaled to a 10-m resolution. The best results were achieved using GAM ( R 2 = 0.90 ). The results illustrate that the optimal regression methods for downscaling spatial information using dissever vary on a case-by-case basis. In particular, simpler approaches such as LM or GAM outperformed more complex approaches in cases where only a limited number of pixels are available to train the downscaling algorithm. This demonstrate the value of an implementation that facilitates testing of different regression strategies.

      PubDate: 2017-09-02T10:48:33Z
  • Inside Front Cover - Editorial Board Page
    • Abstract: Publication date: September 2017
      Source:Computers and Electronics in Agriculture, Volume 141

      PubDate: 2017-09-02T10:48:33Z
  • Automatic system for improving underwater image contrast and color through
           recursive adaptive histogram modification
    • Abstract: Publication date: September 2017
      Source:Computers and Electronics in Agriculture, Volume 141
      Author(s): Ahmad Shahrizan Abdul Ghani, Nor Ashidi Mat Isa
      Contrast and color are important attributes to extract and acquire much information from underwater images. However, normal underwater images contain bright foreground and dark background areas. Previous enhancement methods enhance the foreground areas but retain darkness and blue-green illumination of background areas. This study proposes a new method of enhancing underwater image, which is called recursive adaptive histogram modification (RAHIM), to modify image histograms column wisely in accordance with Rayleigh distribution. Modifying image saturation and brightness in the hue–saturation–value color model increases the natural impression of image color through the human visual system. Qualitative and quantitative evaluations prove the effectiveness of the proposed method. Comparison with state-of-the-art methods shows that the proposed method produces the highest average entropy, measure of enhancement (EME), and EME by entropy with the values of 7.618, 28.193, and 6.829, respectively.
      Graphical abstract image

      PubDate: 2017-09-02T10:48:33Z
  • A new approach for zoning irregularly-spaced, within-field data
    • Abstract: Publication date: September 2017
      Source:Computers and Electronics in Agriculture, Volume 141
      Author(s): Corentin Leroux, Hazaël Jones, Anthony Clenet, Bruno Tisseyre
      Management zones can be defined as homogeneous regions for which specific management decisions are to be considered. The delineation of these management units is important because it enables or at least facilitate growers and practitioners performing site specific management. The delineation of management zones has essentially been performed by (i) clustering techniques or (ii) segmentation algorithms arising from the image processing domain. However, the first approach does not take into account the spatial relationships in the data, and is prone to generate a large number of fragmented zones while he second methodology has only been dedicated to regularly-spaced, within-field data. This work proposes a new approach to generate contiguous management zones from irregularly-spaced within-field observations, e.g. within-field yield, soil conductivity, soil samples, which are a very important source of data in precision agriculture studies. A seeded region growing and merging algorithm has been specifically designed for these irregularly-spaced observations. More specifically, a Voronoi tessellation was implemented to define spatial relationships between neighbouring observations. Seeds were automatically placed at specific locations across the fields and management zones were first expanded from these seeds. The merging procedure aimed at generating more manageable and interpretable zones. The merging algorithm was defined in a way that made it possible to incorporate machinery and technical management constraints. Experiments demonstrated that the proposed methodology was able to generate relatively compact and contiguous management zones. Furthermore, machinery and technical constraints were shown to significantly influence the results of the delineation which proved the importance of accounting for these considerations.

      PubDate: 2017-09-02T10:48:33Z
  • Seed drill instrumentation for spatial coulter depth measurements
    • Abstract: Publication date: September 2017
      Source:Computers and Electronics in Agriculture, Volume 141
      Author(s): Søren Kirkegaard Nielsen, Lars Juhl Munkholm, Mathieu Lamandé, Michael Nørremark, Nick Skou-Nielsen, Gareth T.C. Edwards, Ole Green
      An even and correct depth placement of seeds is crucial for uniform crop germination and for obtaining the desired agricultural yield. On state-of-the-art seed drills, the coulter down pressure is set manually by static springs or heavy weights, which entails that the coulter’s seeding depth reacts to variations in soil resistance. The aim of the study was to develop and test an instrumentation concept installed on a low-cost, lightweight, three meter wide, single-disc seed drill, for on-the-go measurements of spatial depth distributions of individual coulters under real field conditions. A field experiment was carried out to measure individual coulter depths at three different operational speeds. The targeted seeding depth was −30mm but shallower mean coulter depths were obtained and the depth decreased slightly – although not significantly – with increasing speed, i.e. to −22.1, −20.9 and −19.0mm for 4, 8, and 12kmh−1, respectively. The coulter depths ranged between −60mm (below the surface) and even above surface at all speeds, but the variation tended to decrease with decreasing speed. However, soil resistance influenced coulter depth as indicated by a significant block effect. The mean coulter depth varied up to ±5mm between the blocks. In addition, significant depth variations between the individual coulters were found. The mean depths varied between −14.2 and −25.9mm for the eleven coulters. The mean shallowest coulter depth (−14.2mm) was measured for the coulter running in the wheel track of the tractor. The power spectral densities (distribution) of the coulter depth oscillation frequencies showed that the majority of oscillations occurred below 0.5Hz without any natural vibration frequency. The study concluded that the instrumentation concept was functional for on-the-go spatial coulter depth measurements.

      PubDate: 2017-09-02T10:48:33Z
  • Maize and weed classification using color indices with support vector data
           description in outdoor fields
    • Abstract: Publication date: September 2017
      Source:Computers and Electronics in Agriculture, Volume 141
      Author(s): Yang Zheng, Qibing Zhu, Min Huang, Ya Guo, Jianwei Qin
      An automated method for maize and weed detection is very important to efficiently remove weeds and precisely calculate the quantity of maize. Color features were used in this study to investigate a simple maize-detection method using a color machine-vision system. Conventional image segmentation methods based on RGB values cannot separate maize from weeds because of the highly similar image RGB values of these plants. Thus, a post-processing algorithm was developed to distinguish maize from weeds after image preprocessing. Color indices were used to develop a classification model. The nine optimal features were selected by principal component analysis to reduce the effect of illumination. Finally, support vector data description was used as a classifier to differentiate maize from the mixes of different species of weeds. Pictures were taken by a commercial camera and used to verify the stability of the algorithm. Results show that the overall accuracy for three years is 90.19%, 92.36% and 93.87%, respectively. And the color indices used in this work were stable under various weather conditions and over time.

      PubDate: 2017-09-02T10:48:33Z
  • Wheat landraces identification through glumes image analysis
    • Abstract: Publication date: September 2017
      Source:Computers and Electronics in Agriculture, Volume 141
      Author(s): Oscar Grillo, Sebastiano Blangiforti, Gianfranco Venora
      A new practical method able to identify wheat local landraces was implemented. It is based on computerized image analysis techniques and statistical identification, for the first time on the basis of glumes size, shape, colour and texture. Ears of 52 different Sicilian wheat landraces were reaped for three consecutive years. Digital images of the glumes were acquired, processed and analysed, measuring 138 quantitative morpho-colorimetic variables. The data were statistically analysed applying a Linear Discriminant Analysis. All the statistical comparisons, distinguished for systematic rank, given perfect identification performances; while an overall percentage of correct identification of 89.7% was reached when all the landraces were compared all together. Finally, the identification system was tested with an unknown glume sample, later entirely identified as Vallelunga, one of the Sicilian landraces. This work represents the first attempt of wheat landraces identification based on glume phenotypic characters, applying image analysis techniques. Considering the growing interest in local old wheat landraces, strongly linked to the renewed appreciation in traditional and typical local products, the obtained results support the application of the image analysis system not only for grading purposes, but also to define the product traceability, in order to get a “market card” for wheat landraces.

      PubDate: 2017-09-02T10:48:33Z
  • Crop height monitoring with digital imagery from Unmanned Aerial System
    • Abstract: Publication date: September 2017
      Source:Computers and Electronics in Agriculture, Volume 141
      Author(s): Anjin Chang, Jinha Jung, Murilo M. Maeda, Juan Landivar
      Crop height is a very important attribute to assess overall crop condition, irrigation, and estimation of terminal yield. In this study, a novel method to monitor crop height of Sorghum (Sorghum bicolor) using an Unmanned Aerial System (UAS) is proposed. UAS data were acquired seven times over the growing season and each aerial acquisition included over 200 images with significant image overlap at an altitude of 50m above ground. Ortho-mosaic image and 3D point cloud were generated by applying the Structure from Motion (SfM) algorithm to the images. Ground control points (GCPs) were installed around the study area and they were surveyed using a real time kinematic (RTK) GPS unit for accurate geo-referencing of the geospatial data products. A Digital Terrain Model (DTM) and Digital Surface Model (DSM) were generated from the 3D point cloud data, and a Crop Height Model (CHM) was then created by subtracting DTM from DSM. Uniform crop grids along the center line of each variety were defined for further processing. The maximum CHM value within each individual grid was taken to represent crop height of the grid, and average of all grid heights over the whole area of each variety was calculated as crop height of individual variety. These measurements were compared with manual crop height measurements. Root Mean Square Error (RMSE) between field measurements and the proposed approach was 0.33m. In addition, the height estimates from both field measurement and the proposed approach could be used to derive a growth curve by fitting a sigmoidal curve. The residual RMSEs between the observed and predicted value of the curve established from UAS and field measurements were calculated as 0.05m and 0.1m, respectively. The growth curve results showed that the proposed approach indicated less RMSE and generated more reliable growth curves for monitoring sorghum height.

      PubDate: 2017-09-02T10:48:33Z
  • Investigating biospeckle laser analysis as a diagnostic method to assess
           sprouting damage in wheat seeds
    • Abstract: Publication date: September 2017
      Source:Computers and Electronics in Agriculture, Volume 141
      Author(s): Darren B. Sutton, Zamir K. Punja
      Sprouting Damage is a persistent quality control concern in the cereals industry, as sprouting damaged kernels (SDK) contain enzymes that have a detrimental effect on flour quality. Furthermore, the severity of sprouting damage is difficult to detect using standard visual grading methods. In this work, we present Biospeckle Laser Analysis (BLA) as a diagnostic tool to measure the germination progress and the simulated SDK severity of Canadian Western Red Spring wheat seeds. We first analysed dissected seeds and found that high frequency biospeckle activity in the germ correlated with germination progress. Following this, a novel whole seed grading protocol was developed using qualitative and quantitative data provided by the biospeckle measurement. Using our whole seed grading protocol, seeds subjected to simulated SDK treatments at two levels (10 and 20h pre-trial water exposure) could be differentiated from healthy seeds and from each respective treatment (p<0.05). Our results indicate that BLA has the ability to detect latent SDK and may have further applications such as characterizing the dormancy traits of wheat cultivars and studying the seed germination process.

      PubDate: 2017-09-02T10:48:33Z
  • The smartphone as an economical and reliable tool for monitoring the
           browning process in sparkling wine
    • Abstract: Publication date: September 2017
      Source:Computers and Electronics in Agriculture, Volume 141
      Author(s): Juan Luis Pérez-Bernal, Mercedes Villar-Navarro, M. Lourdes Morales, Cristina Ubeda, Raquel M. Callejón
      A fast, reliable colorimetric method based on colour measurements obtained from digital images of sparkling wine to study wine browning is proposed. Digital images were obtained, using a smartphone camera and a diffuse light source as the measurement device and, in order to isolate external influences, a suitable blackbox. Images in Red Green and Blue (RGB) colour space were splitted into the three basic channels (R, G, and B) and their values were used to monitor the browning process. Four sparkling Cava wines were monitored during an accelerated browning process. Results showed that while the Red and Green channels remained almost constant, the browning process affected primarily the Blue channel, decay being time-dependent. The Blue channel decay (%Bt ) percentage over time is proposed as a new quality marker. This value had a high correlation with absorbance at 420nm and 5-hydroxymethyl-2-furfural contents. These latter are the most usual markers of wine browning and the results obtained show that %Bt is a good browning descriptor. The advantages of the proposed methodology are single-step multiple samples analysis, affordable instrumentation and the fact that sample preparation is not required.
      Graphical abstract image

      PubDate: 2017-09-02T10:48:33Z
  • Tango in forests – An initial experience of the use of the new Google
           technology in connection with forest inventory tasks
    • Abstract: Publication date: September 2017
      Source:Computers and Electronics in Agriculture, Volume 141
      Author(s): Julián Tomaštík, Šimon Saloň, Daniel Tunák, František Chudý, Miroslav Kardoš
      This study focuses on the evaluation of the accuracy and feasibility of the use of the new Google Tango technology for outdoor measurements in forest inventory tasks. The technology uses RGB-D and inertial sensors and visual Simultaneous Localization and Mapping (SLAM) and combines them with compact mobile devices. Three circular test plots, established using forest inventory methodology, were used for the testing. Tree position references were measured using a total station; reference diameters at breast height (DBH) were acquired using callipers. Close-range photogrammetry and Field-Map measurements were conducted for comparison. Root mean square errors (RMSE) of the DBHs acquired using the Tango device were up to two centimetres. The positional accuracy was highly dependent on scanning methods. Two patterns of scanning were designed for the testing – “Spiral” and “Sun”. RMSE of positions were over one metre for the Spiral pattern and 0.20m for the Sun pattern. These results are comparable with some earlier reported results of other technologies, which provide 3D point clouds (photogrammetry, laser scanning). Field experiences related to the use of the hardware and software are also reported. With the further development of hardware and dedicated software, the Google Tango platform could provide a feasible, sufficiently accurate, and cost-effective solution for various measurements in forests where point clouds are applicable.
      Graphical abstract image

      PubDate: 2017-08-02T16:38:07Z
  • Development of a machine vision system for determination of mechanical
           properties of onions
    • Abstract: Publication date: September 2017
      Source:Computers and Electronics in Agriculture, Volume 141
      Author(s): Ayoub Jafari Malekabadi, Mehdi Khojastehpour, Bagher Emadi, Mahmood Reza Golzarian
      In this research, a machine vision system was developed to measure the contact area and dimensions of agricultural samples in mechanical properties testing. Two probes were made of aluminum and round glass panes. The Universal Testing Machine was equipped with these probes. One camera was positioned inside the probe to monitor the contact area between probe and samples. Other camera was located outside of probe to measure dimension of samples during the test. Onions were selected as study products and those mechanical properties were measured using this machine vision system. Also, to verify the results of this system, the mechanical properties were calculated using conventional methods. The effects of onion cultivars (red and yellow), loading direction (polar and equatorial) and loading speed (15 and 25mm/min) on the size of contact area, the stress and strain, elasticity modulus and Poisson’s ratio were examined. The results showed that there was no any statistically significant differences between conventional method and our presented method at 99% confidence level. Therefore, a machine vision system can be replaced with conventional method. It was possible to assimilate the shape of contact area to a near perfect circle. The effect of loading direction on Poisson’s ratio and the loading speed on stress and elasticity modulus were significant. Red onions under polar loading with speed 15mm/min had maximum Poisson’s ratio, stress and modulus of elasticity. The stress was obtained as 0.281±0.044MPa. The values of elasticity modulus were obtained as 2.56±1.4 and 2.77±1.8MPa for yellow and red onions, respectively. The Poisson’s ratio along x and y axis were obtained as 0.393±0.05 and 0.375±0.07, respectively. Polar loading samples were easy to deform laterally compared to equatorial loading samples. The contact area, stress and Poisson’s ratio increased with increasing deformation while the modulus of elasticity decreased.

      PubDate: 2017-08-02T16:38:07Z
  • Optimization of the harvest planning in the olive oil production: A case
           study in Chile
    • Abstract: Publication date: September 2017
      Source:Computers and Electronics in Agriculture, Volume 141
      Author(s): Celso Herrera-Cáceres, Francisco Pérez-Galarce, Eduardo Álvarez-Miranda, Alfredo Candia-Véjar
      In this work, a mathematical programming model for aiding the decision-making process of olive harvest planning is proposed. The model aims at finding a harvest schedule of different land units that maximizes the total amount of the oil extracted in the mill. Such a harvest plan must ensure quality standards, respect technological limitations, coordinate operations between the field and the mill, and satisfy a budget associated with the harvest operations. Moreover, the presented approach considers the effect of climatological phenomena (rain and frost) during the harvest season, which results in a reduction of olive crops. The model was tested on a real problem of a company located in the central zone of Chile. The experiments with the model show that it is able to obtain better solutions than those obtained by the traditional operation planning when it is tested with real datasets from the company. The optimization model is flexible, allowing the management of several parameters like the project budget and the risks generated by the climate. Thus, it can provide alternative harvest plans in a short time by simulating different climatological scenarios. From a managerial point of view, some lessons about the advantages and difficulties of the model were learned from its use in the company.

      PubDate: 2017-08-02T16:38:07Z
  • Analysis of annoying shocks transferred from tractor seat using vibration
           signals and statistical methods
    • Abstract: Publication date: September 2017
      Source:Computers and Electronics in Agriculture, Volume 141
      Author(s): Ahmad Taghizadeh-Alisaraei
      In many developing countries, agricultural tractors are employed for different field operations and transportation along rural roads where the operator is exposed to high levels of harmful vibrations from the tractor seat. In case of the vibrations transferred to the operator’s body, the vibration signal form and the number and type of the shocks have been neglected in related literature. This article is focused on modern approaches to analyze tractor and car seats vibrations which have been ignored in many standards guidelines. For this purpose, examining a case study, kurtosis and skewness approaches were used to evaluate the vibration signals generated by a tractor seat. Further, an innovative model was developed to evaluate the effect of the vibrations, in terms of vibration intensity and signal form, on the operator. Average percentage of vibration transfer from the engine to feet platform was estimated at 31%. It could be concluded that, ground roughness generates vibrations within the frequency range of 0–30Hz, while the engine causes vibrations at some frequency ranging within 50–200Hz. Kurtosis could better present the difference between the two signals. The results indicated that, when considered independently, the values of RMS, VDV and kurtosis cannot well represent differences in vibration signals parametrically. However, the novel approach, when used as a benchmark for comparison, showed the differences between the signals at high accuracy.

      PubDate: 2017-08-02T16:38:07Z
  • Detection and discrimination of pests and diseases in winter wheat based
           on spectral indices and kernel discriminant analysis
    • Abstract: Publication date: September 2017
      Source:Computers and Electronics in Agriculture, Volume 141
      Author(s): Yue Shi, Wenjiang Huang, Juhua Luo, Linsheng Huang, Xianfeng Zhou
      Kernel discriminant analysis (KDA) can be used as a feasible strategy for identifying plant stresses, especially for detection of pests and diseases, considering the highly nonlinear distribution of hyperspectral absorption features that respond to biophysical variations in plants caused by foliar lesions. However, traditional computation of the kernel projection features produced by hyperspectral data is always affected by redundant information among the numerous wavelengths, subsequently leading to dimension disaster. In order to alleviate this problem, the aim of this study is to propose a spectral vegetation indices-based kernel discriminant approach (SVIKDA) for the detection and classification of yellow rust, aphid, and powdery mildew in winter wheat at the leaf and canopy level. Leaf and canopy level hyperspectral reflectance datasets were measured with a total of 314 and 187 samples, respectively. Fourteen Spectral Vegetation Indices (SVIs) related to foliar biophysical variations were employed as the input sample space; then, by using correlation analysis and independent t-tests, redundant information among SVIs was removed. Subsequently, a Gaussian kernel function was utilized to cast discriminant analysis into a nonlinear framework. Finally, using 5-fold cross validation, performance and transferability of this approach were evaluated. Our results revealed that the SVIKDA outperformed conventional linear discriminant approach on detection and classification among healthy wheat leaves and leaves infected with yellow rust, aphids, and powdery mildew. At the leaf level, the classification returned the overall accuracies (OA) of 82.9%, 89.2%, 87.9% for three occurrence levels, i.e. slight, moderate, and severe (Kappa>0.85). Depending on the types and severities of infestations, the classification accuracy was between 76% and 95%; At the canopy level, the multiple classifications between healthy leaves and leaves with damages from the three different infestations still achieved an accuracy greater than 87% (Kappa=0.84). In addition, this approach was also successfully applied in disease index (DI) estimation for a specific infestation at the leaf level, and optimal DI estimation returned high coefficients of determination (R2 >0.7). Furthermore, compared with the commonly used automatic classification algorithm, the SVIKDA achieved an accurate classification without losing the pathological basis of input variables. The results suggest that this method has reliable transferability and great robustness in detecting and discriminating pests and diseases for guiding precision plant protection.

      PubDate: 2017-08-02T16:38:07Z
  • A computer model for particle-like simulation in broiler houses
    • Abstract: Publication date: September 2017
      Source:Computers and Electronics in Agriculture, Volume 141
      Author(s): B.H. Cervelin, D. Conti, M.A. Diniz-Ehrhardt, J.M. Martínez
      The behavior of chickens in broiler houses is affected by many conditions that involve, from the dimensions and design of the house, to the real-time control of temperature and humidity. A lot of experience is available which provides useful recommendations leading to reasonable efficiency of broiler houses. Trying different alternatives in practice is, or course, very expensive. For this reason, computer simulation becomes an extremely useful tool. Presently, standard computers make it possible to build and run simulation models in which each broiler is considered as a single “particle” whose behavior is subject to the interaction with other broilers and the environment. The objective of this paper is to introduce, discuss, and analyze a new computational model, that, to the best of our knowledge, is the first of this type. The model considers the displacements of the chickens as being analogous to the motion of physico-chemical particles (atoms or molecules), relying on Langevin dynamics and taking temperature, air speed, humidity, house dimensions, chicken population, availability of eaters, and water drinking systems into consideration. The parameters of our model were tunned both using extreme and standard assumptions on the behavior of chickens in a broiler house.

      PubDate: 2017-07-23T08:10:54Z
  • Development of a visual monitoring system for water balance estimation of
           horticultural crops using low cost cameras
    • Abstract: Publication date: September 2017
      Source:Computers and Electronics in Agriculture, Volume 141
      Author(s): J.M. González-Esquiva, M.J. Oates, G. García-Mateos, B. Moros-Valle, J.M. Molina-Martínez, A. Ruiz-Canales
      The use of low cost cameras has been extended in all fields of technology in general, and agricultural applications in particular. Images provide useful information on the growing state of horticultural crops, which allow an accurate estimation of water balance and, hence, precise irrigation scheduling. In all of these cases, the temporary images of a crop can provide the percentage of green cover (PCG). This data is calibrated with the irrigation water amount that the crop needs for growing. Therefore, the use of visual monitoring systems in agriculture may reduce water consumption and increase productivity. In this paper, a novel system is presented using low cost cameras and a client-server architecture. It is composed of a set of inexpensive camera modules which communicate with a cloud computing server. Camera modules have been developed using open standard Arduino components; they are able to work independently, with their own connectivity, storage and power supply. On the other hand, the server is responsible for configuring these modules, performing computer vision algorithms and water balance estimation, storing all data in a secure database, and interacting with the user interface using the web. The final result is a complete and inexpensive system that allows continuous monitoring of the state of the crops, providing the user with valuable information about water balance for irrigation management. The proposed method achieves a high accuracy in the estimation of PGC, with an average error below 5%, requiring less than 2s of processing time per image in the server. This is transformed into an error in the computation of the crop coefficient below 1%. Technical details on the hardware and software components of the system are presented. Finally, advantages and weaknesses of the proposed solution are discussed, drawing new lines for future research.

      PubDate: 2017-07-23T08:10:54Z
  • Multi-modal sensor system for plant water stress assessment
    • Abstract: Publication date: September 2017
      Source:Computers and Electronics in Agriculture, Volume 141
      Author(s): James Y. Kim, David M. Glenn
      Plant stress critically affects plant growth and causes significant loss of productivity and quality. When the plant is under water stress, it impedes photosynthesis and transpiration, resulting in changes in leaf color and temperature. Leaf discoloration in photosynthesis can be assessed by measurements of leaf reflectance changes and leaf temperature changes in transpiration can be identified by thermography. To address these physiological properties, a multi-modal sensing system was developed and evaluated for a rapid, automated scouting of plant stress status in an apple orchard with irrigated and dry trees. The multimodal sensor system was installed on a mobile vehicle and includes an IR thermometer array, a thermal imager, a multispectral camera, and two sets of NDVI sensors with a digital camera and an ultrasonic range finder. Soil water status was continuously monitored using soil moisture sensors that were installed at the 15-cm depth for both irrigated and dry trees. A low-cost solution of canopy temperature sensing was developed using an IR thermometer array and validated to substitute the thermal imager with the advantage of rapid 2-D thermal mapping at up to 10Hz. An NDVI sensing system was developed to enhance filtering process of the background noise signals by supplemental assistance from a digital camera and a range finder. NDVI responses and 2-D thermal maps were created while driving and recorded weekly for 7weeks during the growing season. The experimental results identified significant difference of canopy temperature (2.6°C) and NDVI (0.235) between the irrigated and dry trees and supported further development of low-cost real-time system for decision support of plant stress detection and management.

      PubDate: 2017-07-23T08:10:54Z
  • Agricultural plastic waste spatial estimation by Landsat 8 satellite
    • Abstract: Publication date: September 2017
      Source:Computers and Electronics in Agriculture, Volume 141
      Author(s): Antonio Lanorte, Fortunato De Santis, Gabriele Nolè, Ileana Blanco, Rosa Viviana Loisi, Evelia Schettini, Giuliano Vox
      The use of plastic materials in agriculture involves several benefits but it results in huge quantities of agricultural plastic waste to be disposed of. Input and output data on the use of plastics in agriculture are often difficult to obtain and poor waste management schemes have been developed. The present research aims to estimate and map agricultural plastic waste by using satellite images. Waste was evaluated by means of the indexes relating waste production to crop type and plastic application as defined by the land use map realized by classifying the Landsat 8 image. The image classification was carried out using Support Vector Machines (SVMs), and the accuracy assessment showed that the overall accuracy was 94.54% and the kappa coefficient equal to 0.934. Data on the plastic waste obtained by the satellite land use map were compared with the data obtained by using the institutional land use map; a difference of 1.74% was identified on the overall quantity of waste.
      Graphical abstract image

      PubDate: 2017-07-23T08:10:54Z
  • Integration of computer vision and electronic nose as non-destructive
           systems for saffron adulteration detection
    • Abstract: Publication date: September 2017
      Source:Computers and Electronics in Agriculture, Volume 141
      Author(s): Sajad Kiani, Saeid Minaei, Mahdi Ghasemi-Varnamkhasti
      This work deals with the development and evaluation of an integrated system based on computer vision system (CVS) and electronic nose (e-nose) for saffron adulteration detection. Ten saffron samples adulterated with two common illegal constituents, namely, Artificially Colored Safflower (ACS) and Artificially Colored Yellow Styles of Saffron (ACYSS) at levels ranging from 10 to 50% (w/w) were characterized in this work. First, the developed CVS and e-nose system were integrated to form a unit system. This set up was utilized to extract color and aroma characteristic variables of each sample. The extracted variables were processed using Principal Component Analysis (PCA), Hierarchical Cluster Analysis (HCA), and Support Vectors Machines (SVMs) to demonstrate the discrimination capability of the developed system. Two multilayer artificial neural network (ANN-MLP) models were also employed for saffron color and aroma strength prediction based on ISO standards. PCA and HCA results of the color and aroma datasets revealed that the adulterated samples have different color and aroma strength compared to authentic saffron and they can clearly be distinguished. SVMs classifier showed good agreement with the PCA results and reached 89% and 100% success rate in the recognition of the different saffron samples based on their color and aroma datasets, respectively. Results of the two ANN-MLP models proved that the developed system is capable of differentiating the authentic and adulterated saffron samples based on their color and aroma strength ( R Color analysis 2 ⩾ 0.95 and R Aroma analysis 2 ⩾ 0.97 ).

      PubDate: 2017-07-23T08:10:54Z
  • Compensation of temperature effects for in-situ soil moisture measurement
           by DPHP sensors
    • Abstract: Publication date: September 2017
      Source:Computers and Electronics in Agriculture, Volume 141
      Author(s): Vinay S. Palaparthy, Devendra N. Singh, Maryam Shojaei Baghini
      Dual probe heat pulse (DPHP) sensors are economical solutions for soil moisture measurements. However, in agriculture fields the temperature significantly changes from time to time during 24h, which affects response of the soil moisture sensor. This paper, analyzes and models the error produced in the response of the DPHP sensors due to variation of the soil temperature. For this purpose, first effect of the soil temperature on the response of the sensor is studied using eight different soil samples. Accordingly, the existing soil moisture model, used for DPHP devices, is modified and used for the temperature compensation. A low power DPHP sensor comprising one heater probe and one temperature sensor probe, placed 0.003m apart, is fabricated. A low power, automated system, dissipating average power of 30mW, is also developed for the field measurements to validate the proposed model. The developed system is deployed in the field and soil moisture is measured for 38h at every 1h interval. Field measurements indicates that volumetric moisture content measured without temperature compensation leads to error of about 3% and with temperature compensation the error is reduced to 0.5%.

      PubDate: 2017-07-23T08:10:54Z
  • A framework for crack detection of fresh poultry eggs at visible radiation
    • Abstract: Publication date: September 2017
      Source:Computers and Electronics in Agriculture, Volume 141
      Author(s): M.H. Abdullah, S. Nashat, S.A. Anwar, M.Z. Abdullah
      Visible radiation technology is increasingly being used for inspection of crack in the eggshell. However, the complexity of the eggshell together with the heterogeneity of the egg internal structures, resulted in very textured image especially when view under candling light. Hence, the inspection process is technically very challenging. In an attempt to improve the inspection performance, a refined anisotropic diffusion filter together with the double thresholding algorithm are used to morphologically segment crack pixels from the background. In this case a novel technique based on Radon transform is developed for feature extraction while the classification is established via a multiclass Support Vector Machine (SVM). Experimental results indicate that the proposed framework performs well on eggs from same or different poultry houses with sensitivity and specificity, averaging at 89.2 % and 94.6 % respectively. The ROC analysis produced 100 % correct classification of intact or unbroken eggs. However, the performance decreased slightly when inspecting different types of cracked eggs with false positive ranging from 3 to 11 % due to high degree of similarity between groups.

      PubDate: 2017-07-23T08:10:54Z
  • Fault diagnosis method for water quality monitoring and control equipment
           in aquaculture based on multiple SVM combined with D-S evidence theory
    • Abstract: Publication date: September 2017
      Source:Computers and Electronics in Agriculture, Volume 141
      Author(s): Hao Yang, Shahbaz Gul Hassan, Liang Wang, Daoliang Li
      A water quality monitoring and control (WQMC) system is an important tool designed to maintain good water quality in aquaculture. A variety of events could cause WQMC equipment to malfunction. Such a problem would in turn result in the generation of unreliable monitoring information and reduced water quality control. The high-dimensional dataset generated by the WQMC equipment makes fault identification difficult. To solve the problem of fault identification, this paper reduced the effects of autocorrelation on the variables and determined feature space based on dynamic principal component analysis (DPCA). Based on cross-correlation analysis, 24 support vector machine (SVM) classifiers were developed for the multi-SVM model. A vote procedure was proposed to identify fault types and conflicts. To solve the conflicts and reduce incorrect diagnosis results, an amendment based on D-S theory determined which fault contributed more to causing the corresponding symptoms. Experimental results showed that the single-pass accuracy of the multi-SVM model in random sample tests varied from 90% to 94% and the combined method could effectively solve the conflicts. Furthermore, the fault identification accuracy could be improved by 3–5%. Incorrect fault diagnosis results remained, and the successful amendment ratio required further improvement. However, the proposed method was helpful for the maintenance and management of WQMC equipment.

      PubDate: 2017-07-23T08:10:54Z
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