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ENGINEERING (1201 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: 17)
AAPG Bulletin     Hybrid Journal   (Followers: 7)
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: 247)
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: 8)
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: 26)
Advances in Fuel Cells     Full-text available via subscription   (Followers: 16)
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: 22)
Advances in Human Factors/Ergonomics     Full-text available via subscription   (Followers: 26)
Advances in Magnetic and Optical Resonance     Full-text available via subscription   (Followers: 9)
Advances in Natural Sciences: Nanoscience and Nanotechnology     Open Access   (Followers: 29)
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: 38)
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: 31)
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: 17)
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: 16)
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   (Followers: 1)
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)
Asia-Pacific Journal of Science and Technology     Open Access  
Asian Engineering Review     Open Access  
Asian Journal of Applied Science and Engineering     Open Access   (Followers: 1)
Asian Journal of Applied Sciences     Open Access   (Followers: 2)
Asian Journal of Biotechnology     Open Access   (Followers: 8)
Asian Journal of Control     Hybrid Journal  
Asian Journal of Current Engineering & Maths     Open Access  
Asian Journal of Technology Innovation     Hybrid Journal   (Followers: 8)
Assembly Automation     Hybrid Journal   (Followers: 2)
at - Automatisierungstechnik     Hybrid Journal   (Followers: 1)
ATZagenda     Hybrid Journal  
ATZextra worldwide     Hybrid Journal  
Australasian Physical & Engineering Sciences in Medicine     Hybrid Journal   (Followers: 1)
Australian Journal of Multi-Disciplinary Engineering     Full-text available via subscription   (Followers: 2)
Autonomous Mental Development, IEEE Transactions on     Hybrid Journal   (Followers: 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: 5)
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: 32)
Biomedical Engineering: Applications, Basis and Communications     Hybrid Journal   (Followers: 5)
Biomedical Microdevices     Hybrid Journal   (Followers: 9)
Biomedical Science and Engineering     Open Access   (Followers: 4)
Biomedizinische Technik - Biomedical Engineering     Hybrid Journal  
Biomicrofluidics     Open Access   (Followers: 4)
BioNanoMaterials     Hybrid Journal   (Followers: 2)
Biotechnology Progress     Hybrid Journal   (Followers: 39)
Boletin Cientifico Tecnico INIMET     Open Access  
Botswana Journal of Technology     Full-text available via subscription   (Followers: 1)
Boundary Value Problems     Open Access   (Followers: 1)
Brazilian Journal of Science and Technology     Open Access   (Followers: 2)
Broadcasting, IEEE Transactions on     Hybrid Journal   (Followers: 10)
Bulletin of Canadian Petroleum Geology     Full-text available via subscription   (Followers: 14)
Bulletin of Engineering Geology and the Environment     Hybrid Journal   (Followers: 10)
Bulletin of the Crimean Astrophysical Observatory     Hybrid Journal  
Cahiers, Droit, Sciences et Technologies     Open Access  
Calphad     Hybrid Journal  
Canadian Geotechnical Journal     Hybrid Journal   (Followers: 24)
Canadian Journal of Remote Sensing     Full-text available via subscription   (Followers: 43)
Case Studies in Engineering Failure Analysis     Open Access   (Followers: 8)
Case Studies in Thermal Engineering     Open Access   (Followers: 4)
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: 7)
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: 22)
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: 4)
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: 265)
Composites Part A : Applied Science and Manufacturing     Hybrid Journal   (Followers: 188)
Composites Part B : Engineering     Hybrid Journal   (Followers: 278)
Composites Science and Technology     Hybrid Journal   (Followers: 182)
Comptes Rendus Mécanique     Full-text available via subscription   (Followers: 2)
Computation     Open Access  
Computational Geosciences     Hybrid Journal   (Followers: 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: 7)
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: 7)
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   (Followers: 1)
CTheory     Open Access  

        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  [3048 journals]
  • The agreement between two next-generation laser methane detectors and
           respiration chamber facilities in recording methane concentrations in the
           spent air produced by dairy cows
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): Diana Sorg, Sarah Mühlbach, Frank Rosner, Björn Kuhla, Michael Derno, Susanne Meese, Angela Schwarm, Michael Kreuzer, Hermann Swalve
      In this study, the handheld laser methane detector (LMD) was discussed as a tool for estimating the methane emissions of individual dairy cows by measuring the profiles of the exhaled air. Data obtained with the most recent generation of the device were compared with those of indirect open-circuit respiration chambers, which are commonly used to quantify methane emissions from ruminants. Data from two LaserMethane Mini-Green LMD units (Tokyo Gas Engineering Solutions) exhibited high agreement with those from four respiration chambers, two at the AgroVet-Strickhof, Eschikon, Lindau (Switzerland) and two at the Leibniz Institute for Farm Animal Biology (FBN) Dummerstorf (Germany). The results were determined using Pearson and concordance correlations and the Bland–Altman method. An inverse regression analysis was used to predict the amount of methane in the chambers from the LMD data. The two LMD units also agreed well with each other in the same respiration chamber and under farm conditions. Both the LMDs and chambers were suitable for detecting differences in mean methane concentrations in the spent air produced by dairy cows during different cow activities in the chamber (p < 0.05). Therefore, the most recent LMD model can reliably quantify the dynamics of methane concentrations in the air produced by dairy cows, although the devices were originally designed to detect gas leaks with high methane concentrations in industrial applications. Further studies are needed to investigate the utility of the current LMD technology in measuring the methane profiles directly at the animal’s nostrils to quantify methane emissions from dairy cows and other ruminants.

      PubDate: 2017-11-15T19:30:51Z
  • Improvement of feedlot operations through statistical learning and
           business analytics tools
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): Hector Flores, Cesar Meneses, J. Rene Villalobos, Octavio Sanchez
      A decision-support, modeling tool is developed that can project future cattle growth patterns in a feedlot based on a low dimensionality dataset available at the start of the feeding process. This work adapts the predictive performance of two well-known statistical machine modeling tools, gradient boosting and random forest regression, to predict future cattle growth. Time series analysis techniques are then used to create an ensemble method that further improves prediction accuracy from individual modeling outputs. Hierarchical clustering techniques are used to leverage projected growth patterns to increase group homogeneity when assigning cattle to different feeding pens. Finally, a profit maximization method is developed that estimates the optimal time each individual cattle should remain in the system under different revenue and cost estimates. The purpose of this work is to incentivize the implementation of modern statistical learning tools in cattle management operations, especially within low-to-mid scale operations that traditionally rely on the expertise of its workers and have limited cattle and process information. Access to ‘off-the-shelf’ statistical learning tools, requiring minimal user-interaction, not only enhances prediction accuracy but helps automate operational decisions. This results in higher process efficiencies and improved standardization practices, while also helping identify profit opportunities. Finally, integrating these components into a single operating framework allows the tool to adapt to changes in data characteristics, which is especially important within non-standardized processes. We show the application of this tool through a case study implementation on a mid-scale operation in the northwestern state of Sonora, Mexico. From our case-study results, it was found that the modeling tool can satisfactorily predict growth patterns based on a low-dimensional set. It also can also capture historic decision-making when segmenting cattle into homogenous groups during their feeding process. Furthermore, it can help identify profit opportunities when estimating optimal cattle system times under varying market conditions.

      PubDate: 2017-11-15T19:30:51Z
  • A novel low-cost smart leaf wetness sensor
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): Gemma Hornero, Jorge E. Gaitán-Pitre, Ernesto Serrano-Finetti, Oscar Casas, Ramon Pallas-Areny
      Foliar wetness plays an essential role in plant disease cycles and epidemic development yet no cost-effective leaf wetness sensors (LWSs) are available that could be deployed within large areas to better understand that role. Electronic LWSs comprise an artificial leaf and the electronic circuitry able to measure electrical impedance changes due to water film or drops on the leaf surface. We propose a simple, compact and low-cost electronic interface circuit (EIC) for artificial leaves based on capacitance changes. The circuit relies on the charge-transfer capacitive sensing method and it is implemented by a microcontroller unit (MCU), which offers computation and communication capabilities currently missing in commercial LWSs, This EIC can be used in custom and commercial artificial leaves hence suits studies that require a close emulation of particular plant leaves.

      PubDate: 2017-11-15T19:30:51Z
  • Discrimination among tea plants either with different invasive severities
           or different invasive times using MOS electronic nose combined with a new
           feature extraction method
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): Yubing Sun, Jun Wang, Shaoming Cheng
      Damage of tea plant causes a lot of loss in tea production, but there is not an appropriate method to detect tea plants with pest damage. In this work, electronic nose (E-nose) and Gas Chromatography-Mass Spectrometer (GC-MS), as an auxiliary technique, were employed to detect tea plants with pest damage in two aspects, including tea plants with different invasive severities and with different invasive times, for giving a comprehensive results. A new feature extraction method based on a piecewise function was proposed and its performance was compared with those of the other three commonly employed models-polynomial functions, exponential functions, and Gaussian functions. Feature selection based on principal component analysis (PCA) and multi-layered perceptron (MLP) were employed for further feature reduction and classification, respectively. The results showed that feature extraction based on piecewise function was the best. The combination of feature extraction based on piecewise function, feature selection based on PCA and MLP was the best method and good enough for the classification in tea plants damage area. The results proved that E-nose was able to detect tea plants either with different invasive severities or different invasive times.
      Graphical abstract image

      PubDate: 2017-11-15T19:30:51Z
  • Monthly pan-evaporation estimation in Indian central Himalayas using
           different heuristic approaches and climate based models
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): Anurag Malik, Anil Kumar, Ozgur Kisi
      Estimation of pan-evaporation has a vital importance in water resources planning and management especially in arid/semi-arid regions. In this study, four heuristic approaches, multi-layer perceptron neural network (MLPNN), co-active neuro-fuzzy inference system (CANFIS), radial basis neural network (RBNN) and self-organizing map neural network (SOMNN) were utilized to estimate monthly pan-evaporation (EPm) at two locations, Pantnagar and Ranichauri, in the foothills of Indian central Himalayas. The monthly climatic data, minimum and maximum air temperatures, relative humidity in the morning and afternoon, wind speed, sun-shine hours and pan-evaporation, were used for model calibration and validation. The combination of appropriate input variables for the applied models was decided using gamma test. The results obtained by MLPNN, CANFIS, RBNN and SOMNN models were compared with climate-based empirical models, such as Stephens-Stewart (SS) and Griffith’s (G), on the basis of root mean squared error, coefficient of efficiency and coefficient of correlation. The results indicated that the performance of CANFIS (RMSE = 0.627 mm, COE = 0.936, COC = 0.979) and MLPNN (RMSE = 0.214 mm, COE = 0.989, COC = 0.970) models with six input variables was superior than the others models in estimating monthly pan evaporation at Pantnagar and Ranichauri stations.

      PubDate: 2017-11-15T19:30:51Z
  • Weed detection in soybean crops using ConvNets
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): Alessandro dos Santos Ferreira, Daniel Matte Freitas, Gercina Gonçalves da Silva, Hemerson Pistori, Marcelo Theophilo Folhes
      Weeds are undesirable plants that grow in agricultural crops, such as soybean crops, competing for elements such as sunlight and water, causing losses to crop yields. The objective of this work was to use Convolutional Neural Networks (ConvNets or CNNs) to perform weed detection in soybean crop images and classify these weeds among grass and broadleaf, aiming to apply the specific herbicide to weed detected. For this purpose, a soybean plantation was carried out in Campo Grande, Mato Grosso do Sul, Brazil, and the Phantom DJI 3 Professional drone was used to capture a large number of crop images. With these photographs, an image database was created containing over fifteen thousand images of the soil, soybean, broadleaf and grass weeds. The Convolutional Neural Networks used in this work represent a Deep Learning architecture that has achieved remarkable success in image recognition. For the training of Neural Network the CaffeNet architecture was used. Available in Caffe software, it consists of a replication of the well known AlexNet, network which won the ImageNet Large Scale Visual Recognition Challenge 2012 (ILSVRC2012). A software was also developed, Pynovisão, which through the use of the superpixel segmentation algorithm SLIC, was used to build a robust image dataset and classify images using the model trained by Caffe software. In order to compare the results of ConvNets, Support Vector Machines, AdaBoost and Random Forests were used in conjunction with a collection of shape, color and texture feature extraction techniques. As a result, this work achieved above 98% accuracy using ConvNets in the detection of broadleaf and grass weeds in relation to soil and soybean, with an accuracy average between all images above 99%.

      PubDate: 2017-11-15T19:30:51Z
  • Performance of stem denoising and stem modelling algorithms on single tree
           point clouds from terrestrial laser scanning
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): Tiago de Conto, Kenneth Olofsson, Eric Bastos Görgens, Luiz Carlos Estraviz Rodriguez, Gustavo Almeida
      The present study assessed the performance of three different methods of stem denoising and three different methods of stem modelling on terrestrial laser scanner (TLS) point clouds containing single trees – thus validating all tested methods, which were made available as an open source software package in the R language. The methods were adapted from common TLS stem detection techniques and rely on finding one main trunk in a point cloud by denoising the data to precisely extract only stem points, followed by a circle or cylinder fitting procedure on stem segments. The combination of the Hough transformation stem denoising method and the iteratively reweighted total least squares modelling method had best overall performance – achieving 2.15 cm of RMSE and 1.09 cm of bias when estimating diameters along the stems, detecting 80% of all stem segments measured on field surveys. All algorithms performed better on point clouds of boreal species, in comparison to tropical Eucalypt. The point clouds underwent reduction of point density, which increased processing speed on the stem denoising algorithms, with little effect on diameter estimation quality.

      PubDate: 2017-11-09T08:30:11Z
  • Using GIS and multicriteria decision aid to optimize the direction of
           trees cutting in the forest ecosystem: A case study
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): Wassim Jaziri
      Forest trees’ harvesting is a critical activity in the forest ecosystem management process. This activity is among the most destructive practices and causes ecological damage on the forest ecosystem. The traditional way of trees’ harvesting is not rational and should be optimized to reduce damage and to improve the profitability of forest resources. Besides, a cost-effective and ecologically-aware harvesting activity is likely to guarantee a sustainable management while ensuring a satisfactory financial benefit. We are interested in this paper in optimizing the direction of trees cutting to reduce ecological damage while taking into account spatial and financial constraints of loggers. We propose a multicriteria geo-optimization approach based on a combination of optimization techniques and GIS functionalities. The optimization method is based on the search for the minimum of a weighted sum of spatial, financial and ecological costs. In this work, we consider various criteria: (1) spatial criteria to consider adjacency restrictions; (2) financial criteria to preserve crop trees for future operations and to facilitate the transport of stems to the log yard; (3) ecological criteria to reduce the destruction of trees and soil degradation. Moreover, spatial data are extracted from Raster-based GIS and used to select trees to be accessed as well as to estimate the cost of cutting and transporting them. We present the results of experiments on real data from the Neotropical forest of French Guiana.

      PubDate: 2017-11-09T08:30:11Z
  • A Wireless Sensor Network (WSN) application for irrigation facilities
           management based on Information and Communication Technologies (ICTs)
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): Won-Ho Nam, Taegon Kim, Eun-Mi Hong, Jin-Yong Choi, Jin-Taek Kim
      Irrigation facilities that supply agricultural water are distributed at low density across areas with water demand and require efficient operation and maintenance. Traditional manual irrigation facilities management faces critical limitations, such as delays/losses resulting from data handling errors, and facility misidentification. Therefore, an information system for irrigation facilities management could be more efficient if it includes a wireless sensor network (WSN) that uses information and communication technologies (ICTs). We propose a wireless sensor network application for irrigation facilities management based on radio frequency identification (RFID) and quick response (QR) codes. The system was installed in a pilot site in the I-dong irrigation districts in Gyeonggi, South Korea, and was determined to be beneficial for the inspection of agricultural irrigation facilities in the irrigation districts. Real-time information downloading, collecting field data, and updating the condition of the irrigation facilities in terms of operational conditions and maintenance requirements can improve management. The operation results demonstrated the applicability of the ICTs and WSN to agricultural water management and that it provided good portability, recognition, and information gathering abilities in the field.

      PubDate: 2017-11-09T08:30:11Z
  • Porcine automation: Robotic abdomen cutting trajectory planning using
           machine vision techniques based on global optimization algorithm
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): Yi Liu, Ming Cong, Huadong Zheng, Dong Liu
      The purpose of this paper is to provide details on implementation of accurate and intelligent automation solution for porcine abdomen cutting while a pig is hung up by rear legs. The system developed utilized a 6-DOF industrial manipulators, customized tools, 2D camera and PC. Eye-to-hand calibrations built coordinate transformation relations of units in Cartesian space. The porcine abdomen curve was identified and fitted into quintic spline curve from image. Under cavum peritonaei constrains, optimal sectional trajectory was planned based on genetic algorithm (GA) by comparing several kinds of optimization algorithms. The results of experimental replications show that the system was successful both in following the varied position carcass and cutting open abdominal cavity without haslet damage. The system can enhance the quality, hygienic standard and efficiency of the process.

      PubDate: 2017-11-09T08:30:11Z
  • Prediction of carbon dioxide concentration in weaned piglet buildings by
           wavelet neural network models
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): Roberto Besteiro, Tamara Arango, J. Antonio Ortega, M. Ramiro Rodríguez, M. Dolores Fernández, Ramón Velo
      Carbon dioxide concentration is a major factor in air quality, animal welfare and air exchange rates inside livestock buildings. CO2 concentration series show a dynamic, non-linear and non-stationary behavior. This type of process can be handled by Wavelet Neural Network (WNN) models, which have been developed in recent years. The purpose of this paper is to develop WNN models capable of predicting the dynamics of CO2 in weaner buildings. Outdoor temperatures, CO2 concentration and temperature in the animal zone and animal activity were recorded in a commercial piglet farm during two complete production cycles. Two WNN models were generated from the recorded data: an autoregressive model (AM) that used the CO2 series and outdoor temperatures for the prediction, and an explanatory model (EM) that used only exogenous variables, namely outdoor temperature, temperature in the animal zone and animal activity. Because CO2 is a highly autoregressive variable, the best results are obtained with the AM. The AM yield an RMSE of 26.330 ppm and a Pearson’s r of 0.995. The EM, with an RMSE of 154.361 ppm and a Pearson’s r of 0.895, reveal the importance of indoor and outdoor temperatures and, consequently, of ventilation rate, for CO2 concentration inside the building. In addition, our results show the effects of animal activity on CO2 concentration, which are delayed by 40–50 min. Based on these results, the CO2 concentrations in the animal zone of weaner buildings can be accurately predicted by WNN models. Therefore, WNN modeling could be widely used to predict and understand the dynamics of environmental variables inside livestock buildings.

      PubDate: 2017-11-09T08:30:11Z
  • Determination of mango ripening degree by electrical impedance
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): Acácio Figueiredo Neto, Nelson Cárdenas Olivier, Erlon Rabelo Cordeiro, Helinando Pequeno de Oliveira
      The kinetics of ripening of fruits, followed by subsequent degradation process represent important biochemical mechanisms with high aggregate value for agribusiness sector. In contrast to the destructive assays for identification of ripening degree of fruits, the development of non-invasive techniques introduces potential advantages concerning to the development of new methods for fruit production control. In this direction, the electrical impedance spectroscopy was used for the identification of the maturation degree of fruits based on variation of bulk resistance dependence with maturation of fruits, due to the variation in the liquid content in fruit fibers. The results revealed the strong correlation between mechanical assays and electrical parameters, characterizing an important advantage in terms of a non-invasive and non-destructive method for ripening identification degree.

      PubDate: 2017-11-09T08:30:11Z
  • An automatic and rapid system for grading palm bunch using a Kinect camera
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): Burawich Pamornnak, Somchai Limsiroratana, Thanate Khaorapapong, Mitchai Chongcheawchamnan, Arno Ruckelshausen
      In a trading market, price of oil palm (Elaeis guineensis) is negotiated depending some key parameters of the fresh fruit bunch (FFB). Inspectors have been hired by a buyer to grade FFB to accept or reject. The classification results made by human inspection are skeptical and not very reliable if workload is high. We have developed a system to grade FFB depending on its quality. Several palm features are extracted from RGB, near infrared, and depth images, captured with a Microsoft Kinect camera version 2.0 installed in a light-controlled environment on the conveyor line. Two main algorithms for classification have been developed. The first algorithm is called a volume integration scheme (SVIS), which measures the relative volume of palm bunch. The second developed algorithm classifies palm bunch into three grades (L-Grade, M-Grade and H-Grade) based on oil content from Soxhlet extraction. The system achieves 83% accuracy for grading palm bunch within 6 s per one sample, which shows the possibility of using the system in a trading market.

      PubDate: 2017-11-09T08:30:11Z
  • Synthesis and design of rice pot seedling transplanting mechanism based on
           labeled graph theory
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): Liang Sun, Xuan Chen, Chuanyu Wu, Guofeng Zhang, Yadan Xu
      The movement and trajectory of pot seedling transplanting (PST) are more complicated than those of rice carpet seedling transplanting (CST). When used to realise PST, the existing single-planetary-carrier two-stage-gear transmission configuration adopted in the gear-train transplanting mechanism has limitations, which are trajectory shape and transplanting posture design. To break through the structure constraint of the gear trains and obtain gear train mechanisms suitable for transplanting movement systematically, the type synthesis of gear train mechanism based on graph theory was carried out. On the basis of analysing the kinematic characteristics of gear train transplanting (GTT) mechanisms, the criterion of gear train structure was established, and the gear train topology graphs were screened. In combination with the designs of rice seedling transplanting and gear train structure, a particular gear train configuration was selected. The expected transplanting trajectory was obtained by fitting the curve to the data points. The reverse design, simulation analysis and testing based on the given trajectory were carried out. Finally, the feasibility of the synthesis scheme was verified.

      PubDate: 2017-11-09T08:30:11Z
  • Numerical study of mechanically ventilated broiler house equipped with
           evaporative pads
    • Abstract: Publication date: Available online 2 November 2017
      Source:Computers and Electronics in Agriculture
      Author(s): Dimitris Fidaros, Catherine Baxevanou, Thomas Bartzanas, Constantinos Kittas
      One of the important factors to improve the broiler production is the provision of an optimum indoor environment (air quality, temperature, humidity, air velocity, gases and PMs concentration) with lower possible cost. The internal microclimate can be controlled either passively by selecting appropriate construction geometry and materials or actively by the ventilation systems and the electromechanical (E/M) equipment. In the case of broiler chamber the conditions that constitute optimum internal microclimate vary with respect the birds’ age. In the present work, the ventilation, inside a modern and fully automated broiler chamber equipped with fans and evaporative pads located in Central Greece, is simulated using Computational Fluid Dynamics (CFD) techniques. The transport phenomena inside the broiler house are described with Reynolds Averaged Navier Stokes (RANS) equations solved with the Finite Volume Method (FVM). The flow is assumed 3D, steady state and turbulent. The fans of the broiler chamber abduct air from the interior, forming inside negative pressure distribution, and are modeled as exhaust fans. The air enters the broiler house through evaporative pads which are simulated as porous media and as heat sinks, concurrently. The heat sink term is yielded analytically according to the external climatic conditions and the evaporative pads specifications. The litter and the animals are considered also as porous materials and sources of heat. The birds’ thermal properties and their heat emissions are computed according to their age, the measured birds’ volume, and height and meat composition. The developed CFD model is validated against measurements of temperature (16 points) and air velocity (6 points). According to the simulation results, it is drawn that the vertical temperature gradient should be taken into account when the operational sensors for the cooling devices are positioned inside the chamber since there is a deviation higher than 2 °C between the air content above and among the birds. Also various combinations of the available five fans, operating in two possible modes of the examined poultry chamber are studied in order to assess their effect to the internal microclimate. The operation of two or three central fans are proven to be the optimum choice in terms of temperature, ventilation and air velocity. The operation of only one fan fails to preserve the required temperature, while the operation of more than three fans does not improve the ventilation rates.

      PubDate: 2017-11-09T08:30:11Z
  • Visual analytics and remote sensing imagery to support community-based
           research for precision agriculture in emerging areas
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): Mark P. Wachowiak, Daniel F. Walters, John M. Kovacs, Renata Wachowiak-Smolíková, April L. James
      Agriculture in northern Ontario, Canada, has not yet reached the level of development of the southern regions of the province. In spite of the increasing desirability of the former region for agricultural expansion, northern agricultural producers – as well as other producers in “emerging” areas – have less access to information and decision support services relative to more established agricultural regions. At the same time, geographic information systems (GIS) are now being integrated into precision agriculture to assess field variability, to ensure optimal use of information, to maximize output, and to increase efficiency. To address this trend, a community-based research initiative based on an interactive web-based information visualization and GIS decision support system has been deployed with the aim of providing northern Ontario producers with access to the data they need to make the best possible decisions concerning their crops. This system employs citizen science and community-based participatory research to build a mutually beneficial partnership between agricultural producers, researchers, and other community stakeholders.

      PubDate: 2017-11-02T07:22:16Z
  • Modelling the interaction of a deep tillage tool with heterogeneous soil
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): Zhiwei Zeng, Ying Chen, Xirui Zhang
      Numerical simulation of soil-tool interactions is a cost-effective method to assess and gain insight into the tool performance as affected by working parameters. In this study, a discrete element model (DEM) was developed to simulate a deep tillage tool and its interaction with soil to address the stratified soil layers in agricultural fields using the Particle Flow Code in Three Dimensions (PFC3D). Sensitivity analysis was performed to determine the most critical set of model parameters which affected the soil dynamic behaviors. With test data, this set of model parameters was calibrated using a systematic scheme that featured response surface optimization and inverse solution technique to minimize the discrepancy between simulations and measurements. The model was validated and then utilized to investigate the effects and the optimal value of the working depth. The results showed that the particle stiffness and bond stiffness were the most influential model parameters and the calibrated values were 6.43×1 Nm−1 and 2.62×1 Pam−1, respectively. The calibrated model was capable of predicting soil cutting resistance and soil disturbance characteristics with relative errors ranging from 2.63 to 10.2%. Simulation results indicated that the when the deep tillage tool works at either 5 or 20mm deeper than the hardpan layer, the best performance for breaking the hardpan could be achieved. The proposed model can facilitate the investigation of interactions between soil engaging tools and heterogeneous soil.

      PubDate: 2017-10-26T07:10:19Z
  • Automated system for real time tree canopy contact with canopy shakers
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): Rafael R. Sola-Guirado, David Ceular-Ortiz, Jesús A. Gil-Ribes
      Most crops destined for industrial transformation employ mechanical harvesters that have been developed satisfactorily by adapting the tree to the machine features. Not all fruit trees can be adapted, and this is the case in traditional olive orchards, where canopies are very irregular and a complex harvesting system is necessary to adapt to different tree geometries and sizes. Lateral canopy shakers have arisen as an alternative system, the use of which is spreading as they allow continuous integral harvesting of several crops such as citrus fruit or, more recently, the olive. Contact between the shaker and the canopy is a key harvesting factor that must be studied. Manually positioning several shaker heads at different heights to follow the tree contour during continuous harvesting is a tedious task for an operator and may decrease potential efficiency. However, automation of shaker contact with the canopy may increase harvester efficiency. Two automatic systems composed of several electronic devices were developed and incorporated into a harvester with several shaker heads. The first system controlled canopy contact based on measuring the distance between the shaker and the tree contour. The second system measured the variation of the shaker mechanism’s hydraulic pressure in order to adjust the position of each head relative to the canopy. Both systems were compared to manual control by studying removal efficiency, harvesting efficiency, debris production and percentage of shaking time within the control intervals. Results determined the suitability of automatic harvesting systems with an increase of 5.9% in removal efficiency based on the criterion of tree resistance to shaking, with no significant differences in tree damage and an increase in field capacity ha h-1 person-1. Laser LED may be a valid technology for measuring the distance to canopy in real time and gave satisfactory results but a decrease of 7.9% in removal efficiency compared to manual sighting. The bottom, middle and top of the tree present different patterns in the harvesting process, and as resistance mode adjusts control intervals to the different patterns, it may provide a closer fit to follow than distance mode. Further improvements are required to enhance harvesting efficiency by connecting automation between the removal system and the catch frame.

      PubDate: 2017-10-26T07:10:19Z
  • Inside Front Cover - Editorial Board Page
    • Abstract: Publication date: November 2017
      Source:Computers and Electronics in Agriculture, Volume 142, Part A

      PubDate: 2017-10-26T07:10:19Z
  • A low cost sunlight analyser and data logger measuring radiation
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): M.J. Oates, A. Ruiz-Canales, M. Ferrández-Villena, A. Fernández López
      Growing conditions in the early stages of crop development can be critical to eventual yield. This is true for a wide variety of crops such as lettuce, maize and rice. These conditions include not only soil quality, moisture and temperature, but also the quality and duration of available sunlight. A simple measure of ‘brightness’ is however not a good indication of the true ‘quality’ of the sunlight available. Research has shown that the presence or absence of specific wavelengths of light (particularly infra-red (IR), red and blue) can significantly affect photosynthesis and hence crop growth. Further, over exposure of plant tissue to high levels of ultra violet radiation can prove damaging. IR radiation is known to be scattered by weak levels of cloud and haze, and is significantly absorbed by moderate cloud conditions, resulting in lower levels reaching the ground. Ultra violet radiation is capable of penetrating even moderate levels of cloud. The total amount of quality sunlight received by an immature plant can affect its later yield, determining whether a crop is worth harvesting, or influence the later use of fertilizers or the real time control of supplementary, wavelength specific illumination. This paper discusses results from a low cost, real time, stand-alone LED based sunlight analyser and data logger capable of making both quantitative and qualitative measures of specific wavelength bands, and distinguishing sunlight conditions ranging from direct sun, through light haze, moderate cloud and even moonlight. The unit costs less than 10 Euros and can give in excess of 4months unattended monitoring and logging using 3 alkaline AA batteries, storing to an internal 32Mbit Flash EEPROM and transmitting via a 2.4GHz RF link.

      PubDate: 2017-10-18T15:55:52Z
  • Mapping skips in sugarcane fields using object-based analysis of unmanned
           aerial vehicle (UAV) images
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): Carlos Henrique Wachholz de Souza, Rubens Augusto Camargo Lamparelli, Jansle Vieira Rocha, Paulo Sergio Graziano Magalhães
      The use of unmanned aerial vehicles (UAVs) as remote sensing platforms has tremendous potential for describing detailed site-specific features of crops, especially in early post-emergence, which was not possible previously with satellite images. This article describes an object-based image analysis (OBIA) procedure for UAV images, designed to map and extract information about skips in sugarcane planting rows. The procedure consists of three consecutive phases: (1) identification of sugarcane planting rows, (2) identification of the existent sugarcane within the crop rows, and (3) skip extraction and creation of field-extent crop maps. Results based on experimental fields achieved skip rates of between 2.29% and 10.66%, indicating a planting operation with excellent and good quality, respectively. The relationship of estimated versus observed skip length had a coefficient of determination of 0.97, which was confirmed by the value of the enhanced Wilmott concordance coefficient of 0.92, indicating good agreement. The OBIA procedure allowed a high level of automation and adaptability, and it provided useful information for decision making, agricultural monitoring, and the reduction of operational costs.

      PubDate: 2017-10-18T15:55:52Z
  • A visual navigation algorithm for paddy field weeding robot based on image
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): Qin Zhang, M.E. Shaojie Chen, Bin Li
      Navigation system and its navigation algorithm are the crucial parts for intelligent paddy field weeding robot. The environments of paddy fields are complicated in South China. The colors of weed, duckweed and cyanobacteria, which grow in paddy fields, are very similar with rice seedlings. Moreover, the rice seedlings present various morphological features during the growth progress. Therefore, how to extract the guidance lines for navigation system and weeding robot presents various challenges. In order to deal with the above mentioned problems, a navigation method for weeding robot based on SUSAN (smallest univalue segment assimilating nucleus) corner and improved sequential clustering algorithm is proposed in this paper. Firstly, gray feature in paddy field image is extracted by using the adaptive graying algorithm. Secondly, the SUSAN corners are extracted as characteristic points. Thirdly, the seedling navigation line is detected by applying the improved sequential clustering algorithm and Hough Transform. Finally, the position error and angle error are calculated, and a fuzzy controller is designed to control the robot. Experimental results show desirable performances of the proposed method. The proposed segmentation method is effective in complicated environment.

      PubDate: 2017-10-18T15:55:52Z
  • Fast non-rigid image feature matching for agricultural UAV via
           probabilistic inference with regularization techniques
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): Zhenghong Yu, Huabing Zhou, Cuina Li
      Image feature matching is one of the most important fundamental technologies for agricultural unmanned aerial vehicle (UAV). There has been some attempt on this task, but the performance and efficiency are still not satisfactory due to the complexity of UAV images, especially undergo non-rigid transformation. In this paper, we propose a probabilistic method to address these problems. We start by creating a set of putative correspondences based on the feature similarity and then focus on removing outliers from the putative set and estimating the transformation as well. This is formulated as a maximum likelihood estimation of a Bayesian model with latent variables indicating whether matches in the putative set are inliers or outliers. We enforce three effective regularization techniques on the correspondence in a reproducing kernel Hilbert space simultaneously, which helps to find an optimal solution. The problem is finally solved by using the expectation–maximization algorithm, and the closed-form solution of the transformation is derived in the maximization step. Moreover, a fast implementation based on sparse approximation is given which reduces the method computation complexity to quadratic without performance sacrifice. Extensive experiments on real farmland images demonstrate accurate and robust results of the proposed method which outperforms current state-of-the-art methods, particularly in case of severe outliers.

      PubDate: 2017-10-18T15:55:52Z
  • Simulation of water distribution under surface dripper using artificial
           neural networks
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): M.N. Elnesr, A.A. Alazba
      Predicting the wetting pattern of a dripper helps in the proper design of the drip irrigation system. An artificial neural network predictor model was developed based on the data from the well-tested model HYDRUS 2D/3D. The simulation data grid from HYDRUS was converted to simpler 3-variables vectors of wetting ellipses. The output vectors contain the radii in x and z directions and the center’s location in the z direction. The simulations were performed for several textural classes, infiltration times, emitter’s discharges, hydraulic models, and other features. After training the neural network, the testing dataset showed a correlation of 0.93–0.99, and the tested patterns showed high similarity to the HYDRUS outputs. Additionally, the paper provided solutions for the problem of simulating larger flow emitters where the flux exceeds the soil’s hydraulic conductivity and the problem of converting HYDRUS outputs to easy-to-use vectors of three parameters representing specific moisture content at a particular time. This work tried a set of 51 input variables’ permutations suggesting the best set of top results. The best trained neural network is freely available for the benefit of researchers and for future development. The sensitivity analysis of the input variables showed that the wetting pattern is mostly affected by time of infiltration, emitter discharge, and the saturated hydraulic conductivity. Future developments of the model are promising by increasing the training data extremes and possibly by adding more features like emitter’s depth for the subsurface drippers.

      PubDate: 2017-10-18T15:55:52Z
  • Grading of ripening stages of red banana using dielectric properties
           changes and image processing approach
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): Asutosh Mohapatra, S. Shanmugasundaram, R. Malmathanraj
      In this research work, the dielectric properties of red banana fruit are studied at different ripening temperatures for developing a rapid and non-destructive assessment method to measure the ripening stages of red banana. A 5 volt sine wave AC power supply and a rectangular parallel plate capacitor circuit are used to measure the difference in dielectric properties caused by the introduction of a red banana in between the plates. The values of properties like capacitance and relative permittivity are increased continuously whereas impedance and admittance are decreased gradually with increase in ripening stages of red banana. In image processing approach, Noise Reductant Local Binary Pattern (NRLBP), Local Binary Pattern (LBP), Completed Local Binary Pattern (CLBP) based techniques are used for red banana’s ripening grade determination. The processing stages involved are enhancement, Binary Pattern generation and classification. The variant Binary patterns are tested on noisy as well as noiseless condition and the results are compared. A novel enhancement technique for banana ripening grade determination is proposed based on segmentation using Tsallis entropy. Also novel idea on the automation of q parameter involved in Tsallis Entropy is implemented. The threshold parameter of the Noise Reductant Local Binary Pattern (NRLBP) varied and its effect on classification rate is studied. A new modification is proposed and implemented on NRLBP to accommodate uniform background and areas with the image. Classification is done using Chi-Square distance/nearest neighbor and Fuzzy C means (FCM) clustering. The results are compared and superiority of FCM method for banana ripening grade determination is noted.

      PubDate: 2017-10-18T15:55:52Z
  • Development and assessment of a tractor driving simulator with immersive
           virtual reality for training to avoid occupational hazards
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): D. Ojados Gonzalez, B. Martin-Gorriz, I. Ibarra Berrocal, A. Macian Morales, G. Adolfo Salcedo, B. Miguel Hernandez
      Tractor overturns are the leading cause of fatalities in the agricultural sector. When drivers misuse the foldable roll over protective structure (ROPS) in tractors, it becomes highly inefficient as a rollover protection system. To solve this problem, the purpose of the present paper is to detail the development and assessment of a tractor driving simulator with immersive virtual reality for training to minimize this risk. In the agricultural sector, tractor driving simulators make it possible to train drivers in risk situations that are not feasible in the real field due to the high risk of roll over. The simulator includes a motion platform for this particular application. The findings of this study suggest that participants with safety knowledge make fewer errors in deploying the ROPS. To reduce the consequences of tractor accidents in the agricultural sector, the promotion of training courses is essential to avoid the misuse of the ROPS. On the contrary, the perception of risk and safety increased after the tractor driving simulator experience for all of the participants but increased significantly more so for non-frequent users of tractors. All of the groups of participants reported that the use of the tractor driving simulator was a positive experience because it can help them to drive more safely, and they feel that they need more training programmes in occupational safety.

      PubDate: 2017-10-18T15:55:52Z
  • 3D numerical simulations as optimization tool for the design of novel EMAP
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): Anastasios Giannoulis, Antonios Mistriotis, Demetrios Briassoulis
      An innovative biodegradable bio-based packaging system has been designed to achieve Equilibrium Modified Atmosphere Packaging (EMAP) of high value fresh horticultural produce through optimised barrier properties obtained by laser micro-perforations combined with selectively permeable membrane technologies. Polylactic Acid (PLA) film was used as packaging material replacing conventional biaxial oriented polypropylene (BOPP) films. The tested commodities were fresh cherry tomatoes and peaches. The micro-perforated PLA EMAP packages appear to perform very well for both commodities due to their higher water vapour permeability compared to conventional materials. 3D numerical simulations were employed as a tool to design and analyse the performance of the novel EMAP system. The numerical simulations results were found to be in good agreement with the experimental data, with the average values differences being within the standard deviations (<5%). The numerical results were also found to agree with the average values predicted by an analytical model. The computational analysis provided detailed 3D mapping of the gas mixture concentrations in the EMAP headspace. 3D mapping not only confirmed the experimental average data but also identified dysfunctional packaging details and explained fruit skin deformations and regions of water vapour condensation observed in conventional BOPP EMAP.

      PubDate: 2017-10-18T15:55:52Z
  • Developing a low-cost 3D plant morphological traits characterization
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): Ji Li, Lie Tang
      A low-cost three-dimensional (3D) plant reconstruction and morphological traits characterization system was developed. Corn plant seedlings were used as research objects for development and validation of the 3D reconstruction and point cloud data analysis algorithms. In this application, precise alignment of multiple 3D views generated by a 3D time-of-flight (ToF) sensor is critical to the 3D reconstruction of a plant. Previous research indicated that there is strong need for high-throughput, high-accuracy, and low-cost 3D plant reconstruction and trait characterization phenotyping systems. This research produced a 3D reconstruction system for indoor plant phenotyping by innovatively integrating a low-cost 2D camera, a low-cost 3D ToF camera, and a chessboard pattern beacon array to track the position and attitude of the 3D ToF sensor and, thus, accomplished precise 3D point cloud registration over multiple views. Specifically, algorithms for beacon target detection, camera pose tracking, and spatial relationship calibration between 2D and 3D cameras were developed for such a low-cost but high-performance 3D reconstruction solution. A plant analysis algorithm in a 3D space was developed to extract the morphological trait parameters of the plants by analyzing their 3D point cloud data. The phenotypical data obtained by this novel and low-cost 3D reconstruction based phenotyping system were validated by the experimental data generated by instrument and manual measurement. The results demonstrated that the developed phenotyping system has achieved promising measurement accuracy, fast processing speed while offering a low hardware cost, lending itself to a practical means of acquiring detailed 3D morphological traits for automated indoor plant phenotyping.

      PubDate: 2017-10-11T15:19:23Z
  • Reliability of different color spaces to estimate nitrogen SPAD values in
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): José F. Reyes, Christian Correa, Javier Zúñiga
      A method to estimate the leaf nitrogen concentration based on image processing techniques was developed. The images came from maize leaves with known levels of nitrogen, measured with a SPAD meter. In order to establish a correlation between the N concentration and the SPAD measurement, an average value in SPAD units and a representative color value were assigned to every leaf. In fact, the representative color values were computed by creating a weighting average of three representative leaf color groups. Accordingly, a prototype of every group and the pixel's area of each group were used to compute the weighting average. Thereby, these groups were created according to their degree of similarity using the k-means clustering technique, which provides a prototype of every group. In specific, the clustering was performed over images in the L∗a∗b∗ color space. In addition, correlation models to predict SPAD values, using a single color channel and two color channels, from the RGB, HSV and L∗a∗b∗ color spaces were tested. The analysis shows that it is possible to correlate SPAD values and color data with the concentration of N, furthermore, shows that when single color channel correlations were performed, the channels G, b∗ and V provide the better modeling accuracy. Moreover, it was likewise shown that better correlations were obtained when a combination of two color channels, from the same or different color spaces, were applied in the correlation. In particular, the most accurate prediction models were obtained held the pairs of color channels S-V and G-b∗.

      PubDate: 2017-10-11T15:19:23Z
  • A review on the practice of big data analysis in agriculture
    • Abstract: Publication date: December 2017
      Source:Computers and Electronics in Agriculture, Volume 143
      Author(s): Andreas Kamilaris, Andreas Kartakoullis, Francesc X. Prenafeta-Boldú
      To tackle the increasing challenges of agricultural production, the complex agricultural ecosystems need to be better understood. This can happen by means of modern digital technologies that monitor continuously the physical environment, producing large quantities of data in an unprecedented pace. The analysis of this (big) data would enable farmers and companies to extract value from it, improving their productivity. Although big data analysis is leading to advances in various industries, it has not yet been widely applied in agriculture. The objective of this paper is to perform a review on current studies and research works in agriculture which employ the recent practice of big data analysis, in order to solve various relevant problems. Thirty-four different studies are presented, examining the problem they address, the proposed solution, tools, algorithms and data used, nature and dimensions of big data employed, scale of use as well as overall impact. Concluding, our review highlights the large opportunities of big data analysis in agriculture towards smarter farming, showing that the availability of hardware and software, techniques and methods for big data analysis, as well as the increasing openness of big data sources, shall encourage more academic research, public sector initiatives and business ventures in the agricultural sector. This practice is still at an early development stage and many barriers need to be overcome.

      PubDate: 2017-10-11T15:19:23Z
  • 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
  • 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
  • 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
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