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  Subjects -> AGRICULTURE (Total: 793 journals)
    - AGRICULTURAL ECONOMICS (69 journals)
    - AGRICULTURE (560 journals)
    - CROP PRODUCTION AND SOIL (92 journals)
    - DAIRYING AND DAIRY PRODUCTS (27 journals)
    - POULTRY AND LIVESTOCK (45 journals)

AGRICULTURE (560 journals)                  1 2 3 | Last

Showing 1 - 200 of 263 Journals sorted alphabetically
Aceh International Journal of Science and Technology     Open Access   (Followers: 2)
Acta agriculturae Slovenica     Open Access   (Followers: 4)
Acta Agrobotanica     Open Access   (Followers: 5)
Acta Agronomica Hungarica     Full-text available via subscription   (Followers: 2)
Acta Agronomica Sinica     Full-text available via subscription   (Followers: 5)
Acta Biologica Sibirica     Open Access  
Acta Scientiarum. Animal Sciences     Open Access   (Followers: 3)
Acta Scientiarum. Technology     Open Access   (Followers: 3)
Acta Technologica Agriculturae     Open Access   (Followers: 1)
Acta Universitatis Sapientiae, Alimentaria     Open Access   (Followers: 1)
Advances in Agriculture     Open Access   (Followers: 7)
Advances in Agriculture & Botanics     Open Access   (Followers: 14)
Advances in Agronomy     Full-text available via subscription   (Followers: 15)
Advances in Horticultural Science     Open Access  
Advances in Life Science and Technology     Open Access   (Followers: 14)
Africa Research Bulletin: Political, Social and Cultural Series     Hybrid Journal   (Followers: 10)
African Journal of Agricultural Research     Open Access   (Followers: 3)
African Journal of Food Science     Open Access   (Followers: 5)
African Journal of Food, Agriculture, Nutrition and Development     Open Access   (Followers: 17)
African Journal of Range & Forage Science     Hybrid Journal   (Followers: 6)
Agra Europe     Full-text available via subscription   (Followers: 3)
Agribusiness : an International Journal     Hybrid Journal   (Followers: 3)
Agric     Open Access  
Agricultura     Open Access   (Followers: 1)
Agricultura Tecnica     Open Access   (Followers: 5)
Agricultura Tropica et Subtropica     Open Access   (Followers: 1)
Agricultura, Sociedad y Desarrollo     Open Access   (Followers: 1)
Agricultural Advances     Open Access   (Followers: 3)
Agricultural and Food Science     Open Access   (Followers: 18)
Agricultural Commodities     Full-text available via subscription  
Agricultural Economics     Hybrid Journal   (Followers: 45)
Agricultural History     Full-text available via subscription   (Followers: 166)
Agricultural History Review     Full-text available via subscription   (Followers: 10)
Agricultural Research     Hybrid Journal   (Followers: 3)
Agricultural Science     Open Access   (Followers: 2)
Agricultural Science     Full-text available via subscription   (Followers: 5)
Agricultural Sciences     Open Access   (Followers: 7)
Agricultural Systems     Hybrid Journal   (Followers: 31)
Agricultural Water Management     Hybrid Journal   (Followers: 41)
Agriculture     Open Access   (Followers: 8)
Agriculture & Food Security     Open Access   (Followers: 14)
Agriculture (Poľnohospodárstvo)     Open Access   (Followers: 2)
Agriculture and Agricultural Science Procedia     Open Access  
Agriculture and Biology Journal of North America     Open Access  
Agriculture and Food Sciences Research     Open Access   (Followers: 4)
Agriculture and Human Values     Hybrid Journal   (Followers: 13)
Agriculture, Ecosystems & Environment     Hybrid Journal   (Followers: 56)
Agriprobe     Open Access  
Agrivita : Journal of Agricultural Science     Open Access   (Followers: 2)
Agro-Science     Full-text available via subscription  
Agroalimentaria     Open Access  
Agrociencia     Open Access   (Followers: 1)
Agrociencia Uruguay     Open Access  
Agrokémia és Talajtan     Full-text available via subscription   (Followers: 2)
Agrokreatif Jurnal Ilmiah Pengabdian kepada Masyarakat     Open Access  
Agronomía Colombiana     Open Access  
Agronomía Costarricense     Open Access   (Followers: 1)
Agronomía Mesoamericana     Open Access  
Agronomie Africaine     Full-text available via subscription  
Agronomy     Open Access   (Followers: 11)
Agrosearch     Open Access   (Followers: 1)
Agrotekma : Jurnal Agroteknologi dan Ilmu Pertanian     Open Access  
Akademik Ziraat Dergisi     Open Access  
Alinteri Zirai Bilimler Dergisi : Alinteri Journal of Agricultural Sciences     Open Access  
Ambiência     Open Access  
Ambiente & Agua : An Interdisciplinary Journal of Applied Science     Open Access   (Followers: 1)
American Journal of Agricultural and Biological Sciences     Open Access   (Followers: 10)
American Journal of Botany     Full-text available via subscription   (Followers: 16)
American Journal of Economics and Sociology     Hybrid Journal   (Followers: 29)
American Journal of Potato Research     Hybrid Journal   (Followers: 2)
American Journal of Rural Development     Open Access   (Followers: 5)
Anais da Academia Brasileira de Ciências     Open Access   (Followers: 2)
Annales des Sciences Agronomiques     Full-text available via subscription  
Annals of Agricultural Sciences     Open Access   (Followers: 2)
Annals of Silvicultural Research     Open Access   (Followers: 1)
Annals Valahia University of Targoviste - Agriculture     Open Access  
Annual Review of Resource Economics     Full-text available via subscription   (Followers: 12)
APCBEE Procedia     Partially Free   (Followers: 1)
Applied Financial Economics Letters     Hybrid Journal   (Followers: 8)
Arboricultural Journal : The International Journal of Urban Forestry     Hybrid Journal   (Followers: 7)
Archivos de Zootecnia     Open Access   (Followers: 1)
ARO. The Scientific Journal of Koya University     Open Access  
Arquivos do Instituto Biológico     Open Access   (Followers: 1)
Arthropod-Plant Interactions     Hybrid Journal   (Followers: 2)
Asian Economic Papers     Hybrid Journal   (Followers: 7)
Asian Journal of Agricultural Research     Open Access   (Followers: 4)
Asian Journal of Medical and Biological Research     Open Access   (Followers: 2)
Asian Journal of Plant Sciences     Open Access   (Followers: 3)
Australian Cottongrower, The     Full-text available via subscription   (Followers: 1)
Australian Economic Papers     Hybrid Journal   (Followers: 31)
Australian Economic Review     Hybrid Journal   (Followers: 6)
Australian Forest Grower     Full-text available via subscription   (Followers: 4)
Australian Forestry     Full-text available via subscription   (Followers: 2)
Australian Grain     Full-text available via subscription   (Followers: 3)
Australian Holstein Journal     Full-text available via subscription   (Followers: 1)
Australian Journal of Agricultural and Resource Economics     Hybrid Journal   (Followers: 3)
Australian Journal of Agricultural Engineering     Open Access   (Followers: 1)
Australian Sugarcane     Full-text available via subscription  
Avances en Investigacion Agropecuaria     Open Access   (Followers: 1)
Bangladesh Agronomy Journal     Open Access   (Followers: 1)
Bangladesh Journal of Agricultural Research     Open Access   (Followers: 3)
Bangladesh Journal of Scientific Research     Open Access   (Followers: 2)
Bioagro     Open Access   (Followers: 1)
Biocatalysis and Agricultural Biotechnology     Hybrid Journal   (Followers: 4)
Biocontrol Science and Technology     Hybrid Journal   (Followers: 5)
Biodiversity     Hybrid Journal   (Followers: 27)
Biodiversity : Research and Conservation     Open Access   (Followers: 28)
Biological Agriculture & Horticulture : An International Journal for Sustainable Production Systems     Partially Free   (Followers: 11)
Biosystems Engineering     Hybrid Journal   (Followers: 10)
Biotecnología en el Sector Agropecuario y Agroindustrial     Open Access  
Biotemas     Open Access  
Boletín Semillas Ambientales     Open Access  
Bragantia     Open Access   (Followers: 2)
Brazilian Archives of Biology and Technology     Open Access   (Followers: 3)
British Poultry Science     Hybrid Journal   (Followers: 5)
Buletin Peternakan : Bulletin of Animal Science     Open Access   (Followers: 1)
Buletin Veteriner Udayana     Open Access   (Followers: 3)
Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca : Food Science and Technology     Open Access  
Bulletin of University of Agricultural Sciences and Veterinary Medicine Cluj-Napoca. Agriculture     Open Access  
Caderno de Ciências Agrárias     Open Access  
Cahiers Agricultures     Open Access  
California Agriculture     Open Access   (Followers: 2)
Cambridge Journal of Economics     Hybrid Journal   (Followers: 59)
Canadian Water Resources Journal     Hybrid Journal   (Followers: 22)
Capitalism Nature Socialism     Hybrid Journal   (Followers: 15)
Ceiba     Open Access  
Central European Forestry Journal     Open Access  
Cereal Chemistry     Full-text available via subscription   (Followers: 4)
CERNE     Open Access  
CESifo Economic Studies     Hybrid Journal   (Followers: 17)
Change and Adaptation in Socio-Ecological Systems     Open Access   (Followers: 1)
Chemical and Biological Technologies for Agriculture     Open Access  
Chilean Journal of Agricultural Research     Open Access   (Followers: 1)
Ciencia & Natura     Open Access   (Followers: 1)
Ciência e Agrotecnologia     Open Access  
Ciencia e investigación agraria     Open Access   (Followers: 1)
Ciência e Técnica Vitivinícola     Open Access  
Ciencia forestal en México     Open Access  
Ciência Rural     Open Access   (Followers: 2)
Ciencia y Agricultura     Open Access  
Ciencia, Tecnología y Salud     Open Access  
COCOS : The Journal of the Coconut Research Institute of Sri Lanka     Open Access   (Followers: 1)
Coffee Science     Open Access  
Cogent Food & Agriculture     Open Access   (Followers: 3)
Competition & Change     Hybrid Journal   (Followers: 10)
Computers and Electronics in Agriculture     Hybrid Journal   (Followers: 4)
Contributions to Tobacco Research     Open Access   (Followers: 2)
Corps et culture     Open Access   (Followers: 5)
Cuadernos de Desarrollo Rural     Open Access   (Followers: 1)
Cultivos Tropicales     Open Access   (Followers: 1)
Cultural Geographies     Hybrid Journal   (Followers: 18)
Cultural Sociology     Hybrid Journal   (Followers: 33)
Cultural Studies - Critical Methodologies     Hybrid Journal   (Followers: 16)
Cultural Studies of Science Education     Hybrid Journal   (Followers: 6)
Cultural Trends     Hybrid Journal   (Followers: 17)
Culture, Agriculture, Food and Environment     Hybrid Journal   (Followers: 14)
Culture, Agriculture, Food and Environment     Hybrid Journal   (Followers: 7)
Current Agricultural Science and Technology     Open Access  
Current Agriculture Research Journal     Open Access   (Followers: 1)
Current Life Sciences     Open Access   (Followers: 4)
Current Research in Dairy Sciences     Open Access   (Followers: 5)
Developments in Agricultural Economics     Full-text available via subscription   (Followers: 4)
Developments in Agricultural Engineering     Full-text available via subscription   (Followers: 2)
Diatom Research     Hybrid Journal   (Followers: 2)
Die Bodenkultur : Journal of Land Management, Food and Environment     Open Access  
Dossiers Agraris     Open Access  
Ecological Applications     Full-text available via subscription   (Followers: 155)
Economic Affairs     Hybrid Journal   (Followers: 6)
Economic and Industrial Democracy     Hybrid Journal   (Followers: 8)
Economic Bulletin     Hybrid Journal   (Followers: 4)
Economic Policy     Hybrid Journal   (Followers: 38)
Economic Record     Hybrid Journal   (Followers: 7)
Emirates Journal of Food and Agriculture     Open Access   (Followers: 1)
Empirical Economics     Hybrid Journal   (Followers: 15)
Encuentro     Open Access  
Engineering in Agriculture, Environment and Food     Hybrid Journal  
Ensaios e Ciência: Ciências Biológicas, Agrárias e da Saúde     Open Access  
Eppo Bulletin     Hybrid Journal   (Followers: 2)
Ethiopian Journal of Agricultural Sciences     Open Access  
Ethiopian Journal of Science and Technology     Open Access  
Ethology     Hybrid Journal   (Followers: 6)
EU agrarian Law     Open Access   (Followers: 4)
Euphytica     Hybrid Journal   (Followers: 7)
Eurochoices     Hybrid Journal   (Followers: 1)
European Agrophysical Journal     Open Access  
European Journal of Agronomy     Hybrid Journal   (Followers: 11)
European Journal of American Culture     Hybrid Journal   (Followers: 2)
European Journal of Health Economics     Hybrid Journal   (Followers: 21)
European Journal of Law and Economics     Hybrid Journal   (Followers: 64)
European Review of Agricultural Economics     Hybrid Journal   (Followers: 11)
EvoDevo     Open Access   (Followers: 3)
Extensão Rural     Open Access   (Followers: 1)
Farmer’s Weekly     Full-text available via subscription  
Farmlink Africa     Full-text available via subscription  
Fitosanidad     Open Access  
Florea : Jurnal Biologi dan Pembelajarannya     Open Access  
Folia Horticulturae     Open Access   (Followers: 4)
Food and Agricultural Immunology     Hybrid Journal   (Followers: 2)
Food and Energy Security     Open Access   (Followers: 5)
Food and Environment Safety     Open Access  

        1 2 3 | Last

Journal Cover Biosystems Engineering
  [SJR: 0.824]   [H-I: 77]   [10 followers]  Follow
    
   Hybrid Journal Hybrid journal (It can contain Open Access articles)
   ISSN (Print) 1537-5110 - ISSN (Online) 1537-5129
   Published by Elsevier Homepage  [3118 journals]
  • Establishing the conveying parameters required for the air-seeders
    • Authors: Andrii Yatskul; Jean-Pierre Lemiere
      Pages: 1 - 12
      Abstract: Publication date: February 2018
      Source:Biosystems Engineering, Volume 166
      Author(s): Andrii Yatskul, Jean-Pierre Lemiere
      Correct energy management offers the best possibility for reducing the costs in agricultural production. In present day farming energy and agronomic efficiencies are both important factors. As the working-width of implements has increased, air-seeding is the best solution for the sowing of cereal crops. One of the problematic areas is the design of the air-delivery system, particularly if the pneumatic conveying system is wrongly dimensioned. The flow of seeding material during conveying must be high and regular enough for a high-speed seeding. There are three parameters that ensure the conveying of seeding material in a pipe: air velocity, flow concentration and pipe diameter. It is demonstrated that the outlets of the divider heads are the most critical part of the conveying system. Outlet pipes relatively small diameters and must allow for the highest seeding rates without clogging. It was hypothesised that the air velocity in outlet pipe may be used as an input data for designed a completed conveying system. This paper determines I) a minimal air velocity and flow concentration per type of seeds relative to pipe diameter; II) establishes a method to measure the air velocity of the loaded flow, which could be used to optimise existing seeders from an energy point of view; III) describes a global design methodology for air-seeder conveying systems; IV) reports an comparative study of energy of the most commonly used outlet pipe diameters within the air-seeders; V) describes a method for calculating the energy consumption evaluation; VI) prescribes the optimum outlet pipe diameter deduced from our experimental results, necessary for the design of the following divider heads. Tests were carried out using for wheat and barley seeds, starter fertilisers and a wheat–fertiliser mixture, for three currently used pipe diameters (20, 25 and 30 mm).
      Graphical abstract image

      PubDate: 2017-12-13T06:38:28Z
      DOI: 10.1016/j.biosystemseng.2017.11.001
      Issue No: Vol. 166 (2017)
       
  • Application of thermal imaging of wheat crop canopy to estimate leaf area
           index under different moisture stress conditions
    • Authors: Koushik Banerjee; Prameela Krishnan; Nilimesh Mridha
      Pages: 13 - 27
      Abstract: Publication date: February 2018
      Source:Biosystems Engineering, Volume 166
      Author(s): Koushik Banerjee, Prameela Krishnan, Nilimesh Mridha
      Thermal imaging cameras determine the temperature of objects by non-contact measurements and give the temperature reading for each pixel of the image. They work on the same principle as spot pyrometers. Thermal imaging of the crop canopy can give the temperatures of both the crop canopy and the soil directly. However, a better distinction between the two classes, i.e., leaf and soil, can be made using image classification techniques. In the present study, thermal imaging was used to determine the canopy coverage of wheat; the leaf area index (LAI) was estimated under different moisture stress conditions. The thermal Images were analysed by five different supervised image classification techniques, maximum likelihood, Mahalanobis, minimum distance to mean, parallelepiped and support vector machine methods, using ENVI – image analysis software. The best estimation of LAI was by the support vector machine method, due to its higher overall classification accuracy and the Kappa coefficients. This was further supported by the statistical analysis based on the comparison between the digital image derived LAI and those measured using the plant canopy analyser. The LAI of wheat crop canopy estimated from the thermal images using the support vector machine method was meaningful with higher R2 value of 0.915 and lower values of root mean squared errors (RMSE) and mean based errors (MBE). The present study clearly showed that thermal image analysis can be applied as a non-destructive, rapid technique to characterise the temperature of crop canopy and then to estimate the LAI of wheat grown under moisture stress conditions.

      PubDate: 2017-12-13T06:38:28Z
      DOI: 10.1016/j.biosystemseng.2017.10.012
      Issue No: Vol. 166 (2017)
       
  • On-line crop/weed discrimination through the Mahalanobis distance from
           images in maize fields
    • Authors: Iván D. García-Santillán; Gonzalo Pajares
      Pages: 28 - 43
      Abstract: Publication date: February 2018
      Source:Biosystems Engineering, Volume 166
      Author(s): Iván D. García-Santillán, Gonzalo Pajares
      This study proposes a new automatic method for crop/weed discrimination in images captured in maize fields during the initial growth stages. The images were obtained under perspective projection with a camera installed on board at the front part of a tractor. Different approaches have addressed the problem based on crop row determination and then assuming that inter-row plants are weeds. Nevertheless, an important challenge is the identification of weeds intermixed within the crop rows. This issue is addressed on this paper by applying a minimum criterion distance based on the Mahalanobis distance derived from a Bayesian classification approach, this makes the main contribution. The identification of both intra- and inter-row weeds is useful for more accurate weed quantification for site-specific treatments. Image quality is affected by uncontrolled lighting conditions in outdoor agricultural environments. Also, different plant densities appear due to different growth stages affecting the crop/weed identification process. The proposed method was designed to deal with the above undesired situations, consisting of three phases: (i) segmentation, (ii) training and (iii) testing. The three phases are executed on-line for each image, where training is specific of each single image, requiring no prior training, as it is usual in common machine learning-based approaches, mainly supervised. This makes the second research contribution. The performance of the proposed approach was quantitatively compared against three existing strategies, achieving an accuracy of 91.8%, pixel-wise determined against ground-truth images manually built, with processing times ≤280 ms, which can be useful for real-time applications.

      PubDate: 2017-12-13T06:38:28Z
      DOI: 10.1016/j.biosystemseng.2017.11.003
      Issue No: Vol. 166 (2017)
       
  • The recognition of litchi clusters and the calculation of picking point in
           a nocturnal natural environment
    • Authors: Juntao Xiong; Rui Lin; Zhen Liu; Zhiliang He; Linyue Tang; Zhengang Yang; Xiangjun Zou
      Pages: 44 - 57
      Abstract: Publication date: February 2018
      Source:Biosystems Engineering, Volume 166
      Author(s): Juntao Xiong, Rui Lin, Zhen Liu, Zhiliang He, Linyue Tang, Zhengang Yang, Xiangjun Zou
      In a natural environment, the recognition of ripe litchi and calculation of the picking point are always difficult problems for a picking robot. In this study, a visual system for litchi image acquisition is built and a method of nocturnal litchi recognition and a calculation of picking point is proposed. For comparison, images of the same cluster of litchis are captured during the day in a natural environment and during the night using artificial illumination. By analysing colour features of the same litchi image in different colour models, the YIQ colour model is proved to be the model with the best practicability for nocturnal litchi recognition. In this proposed method, the background of the nocturnal image, instead of the litchi fruit and stem, is first removed using an improved fuzzy clustering method (FCM) combining this analysis approach with a one-dimensional random signal histogram. The fruit is then segmented from the stem base using the Otsu algorithm. The Harris corner was used for picking point detection. The change rates of the horizontal and vertical positions between corner points are analysed to identify the picking point. The experiments show that nocturnal litchi recognition accuracy is 93.75% with an average recognition time of 0.516 s. At different depth distances, the highest accuracy for the picking point calculation is 97.5%, while the lowest is 87.5%. The results show the accuracy and feasibility of this method for litchi recognition and picking point calculation during the night. This research provides technical support of visual localisation technology for litchi-picking robots.

      PubDate: 2017-12-13T06:38:28Z
      DOI: 10.1016/j.biosystemseng.2017.11.005
      Issue No: Vol. 166 (2017)
       
  • Development of a linear mixed model to predict the picking time in
           strawberry harvesting processes
    • Authors: Farangis Khosro Anjom; Stavros G. Vougioukas; David C. Slaughter
      Pages: 76 - 89
      Abstract: Publication date: February 2018
      Source:Biosystems Engineering, Volume 166
      Author(s): Farangis Khosro Anjom, Stavros G. Vougioukas, David C. Slaughter
      In manual fruit and vegetable harvesting, picking time statistics can be used to improve labour management and optimise the design and operation of harvest-aiding machines, such as conventional cross-row conveyors or recently proposed robotic transport carts. In this study, a dataset of 161 picking times from 18 workers was collected in commercial strawberry fields in Salinas, California, and a set of conditional linear mixed models (LMMs) was formulated to model the amount of time (“picking time”) required by a picker to fill an empty tray with harvested crop. The LMMs were based on different combinations of the following influencing factors: picker speed, time of day, plant spacing, and picking cart style. The significance of effects of these factors was investigated and the LMMs were compared with each other using cross-validation (CV) techniques. The LMMs were also evaluated using a new dataset collected during the next year's harvest season. The best predictive LMM was found to be a heterogeneous model with “picker speed”, “time of day”, and “picking cart” factors. The model had a prediction error of 134.9 s based on 10-fold CV, and 136.8 s based on leave-one-out CV (LOOCV). The selected model predicts a priori mean and standard deviation of picking times for any given combination of factor levels. For instance, if picker speed is ‘fast’, the time of day is ‘morning’, and the picking cart is ‘standard’, the marginal predicted picking time is 477.1 ± 42.4 s. The proposed methodology and model structures offer a practical tool for strawberry picking time modelling, which could also be applied to other manually harvested specialty crops such as raspberries, cherry tomatoes, and table grapes.

      PubDate: 2017-12-13T06:38:28Z
      DOI: 10.1016/j.biosystemseng.2017.10.006
      Issue No: Vol. 166 (2017)
       
  • Identification of group-housed pigs based on Gabor and Local Binary
           Pattern features
    • Authors: Weijia Huang; Weixing Zhu; Changhua Ma; Yizheng Guo; Chen Chen
      Pages: 90 - 100
      Abstract: Publication date: February 2018
      Source:Biosystems Engineering, Volume 166
      Author(s): Weijia Huang, Weixing Zhu, Changhua Ma, Yizheng Guo, Chen Chen
      A novel method for the identification of group-housed pigs based on machine vision is proposed. It benefits to the automatic detection and analysis of the behaviour of pigs. Top-view videos of pigs were obtained and the images of individual pigs extracted. The Gabor features were extracted by convolving pig images with Gabor filters and the local structural features using the Local Binary Pattern (LBP) identification. Principle Component Analysis (PCA) was then used to reduce the feature dimension and the features were concatenated to form the feature vectors. In order to evaluate the performance of the proposed method, standing posture images of pigs were used to conduct the experiments in terms of Support Vector Machine (SVM) classification. The experimental results demonstrated that the combination of Gabor and LBP features produced better results. The average recognition rate achieved 91.86% by SVM with a linear kernel and the PCA parameter varied from 0.85 to 0.99.

      PubDate: 2017-12-13T06:38:28Z
      DOI: 10.1016/j.biosystemseng.2017.11.007
      Issue No: Vol. 166 (2017)
       
  • Hyperspectral measurements of yellow rust and fusarium head blight in
           cereal crops: Part 1: Laboratory study
    • Authors: Rebecca L. Whetton; Kirsty L. Hassall; Toby W. Waine; Abdul M. Mouazen
      Pages: 101 - 115
      Abstract: Publication date: February 2018
      Source:Biosystems Engineering, Volume 166
      Author(s): Rebecca L. Whetton, Kirsty L. Hassall, Toby W. Waine, Abdul M. Mouazen
      This paper assesses the potential use of a hyperspectral camera for measurement of yellow rust and fusarium head blight in wheat and barley canopy under laboratory conditions. Scanning of crop canopy in trays occurred between anthesis growth stage 60, and hard dough growth stage 87. Visual assessment was made at four levels, namely, at the head, at the flag leaves, at 2nd and 3rd leaves, and at the lower canopy. Partial least squares regression (PLSR) analyses were implemented separately on data captured at four growing stages to establish separate calibration models to predict the percentage coverage of yellow rust and fusarium head blight infection. Results showed that the standard deviation between 500 and 650 nm and the squared difference between 650 and 700 nm wavelengths were found to be significantly different between healthy and infected canopy particularly for yellow rust in both crops, whereas the effect of water-stress was generally found to be unimportant. The PLSR yellow rust models were of good prediction capability for 6 out of 8 growing stages, a very good prediction at early milk stage in wheat and a moderate prediction at the late milk development stage in barley. For fusarium, predictions were very good for seven growing stages and of good performance for anthesis growing stage in wheat, with best performing for the milk development stages. However, the root mean square error of predictions for yellow rust were almost half of those for fusarium, suggesting higher prediction accuracies for yellow rust measurement under laboratory conditions.

      PubDate: 2017-12-13T06:38:28Z
      DOI: 10.1016/j.biosystemseng.2017.11.008
      Issue No: Vol. 166 (2017)
       
  • Analysis of two visual odometry systems for use in an agricultural field
           environment
    • Authors: Stefan K. Ericson; Björn S. Åstrand
      Pages: 116 - 125
      Abstract: Publication date: February 2018
      Source:Biosystems Engineering, Volume 166
      Author(s): Stefan K. Ericson, Björn S. Åstrand
      This paper analyses two visual odometry systems for use in an agricultural field environment. The impact of various design parameters and camera setups are evaluated in a simulation environment. Four real field experiments were conducted using a mobile robot operating in an agricultural field. The robot was controlled to travel in a regular back-and-forth pattern with headland turns. The experimental runs were 1.8–3.1 km long and consisted of 32–63,000 frames. The results indicate that a camera angle of 75° gives the best results with the least error. An increased camera resolution only improves the result slightly. The algorithm must be able to reduce error accumulation by adapting the frame rate to minimise error. The results also illustrate the difficulties of estimating roll and pitch using a downward-facing camera. The best results for full 6-DOF position estimation were obtained on a 1.8-km run using 6680 frames captured from the forward-facing cameras. The translation error ( x , y , z ) is 3.76% and the rotational error (i.e., roll, pitch, and yaw) is 0.0482 deg m−1. The main contributions of this paper are an analysis of design option impacts on visual odometry results and a comparison of two state-of-the-art visual odometry algorithms, applied to agricultural field data.

      PubDate: 2017-12-27T13:47:02Z
      DOI: 10.1016/j.biosystemseng.2017.11.009
      Issue No: Vol. 166 (2017)
       
  • A methodology of orchard architecture design for an optimal harvesting
           robot
    • Authors: Victor Bloch; Amir Degani; Avital Bechar
      Pages: 126 - 137
      Abstract: Publication date: February 2018
      Source:Biosystems Engineering, Volume 166
      Author(s): Victor Bloch, Amir Degani, Avital Bechar
      To improve robot performance for agricultural tasks, and decrease its cost, the robot can be optimally designed for a specific task in a specific working environment. However, since the environment defines the robot optimal kinematics, the environment itself should also be optimised for optimal robot performance. The objective of this paper is to present and demonstrate a methodology for simultaneous optimal design of robot kinematic and the working environment. This methodology was demonstrated by an example on a tree orchard design for an apple harvesting robot. First, an optimal robot structure for apple picking task was found for a number of tree architectures (shaped by different training systems): Central Leader, Y-trellis and Tall Spindle. Results indicate that for minimising the average apple picking time, the Tall Spindle architecture is preferable for the robotic harvesting of both a single tree and a tree row. Further, the influence of the robot platform motion time on the chosen robot kinematics and the tree training system was analysed. Results show that for fast platforms, the Tall spindle architecture is advantageous. If the platform movement between positions near the trees is slow, the Central Leader architecture is favourable. Additionally, the tilt angle of the Y-trellis training system was analysed using simulated models created by the L-systems simulations. The optimal tilt angle was found to be nearly horizontal (85°), allowing the robot designer to choose the optimal combination of the robot kinematics, number of robot harvesting positions around the tree and the tree training system.

      PubDate: 2017-12-27T13:47:02Z
      DOI: 10.1016/j.biosystemseng.2017.11.006
      Issue No: Vol. 166 (2017)
       
  • Utilisation of visible/near-infrared hyperspectral images to classify
           aflatoxin B1 contaminated maize kernels
    • Authors: Daniel Kimuli; Wei Wang; Kurt C. Lawrence; Seung-Chul Yoon; Xinzhi Ni; Gerald W. Heitschmidt
      Pages: 150 - 160
      Abstract: Publication date: February 2018
      Source:Biosystems Engineering, Volume 166
      Author(s): Daniel Kimuli, Wei Wang, Kurt C. Lawrence, Seung-Chul Yoon, Xinzhi Ni, Gerald W. Heitschmidt
      A visible/near-infrared (VNIR) hyperspectral imaging (HSI) system (400–1000 nm) was used to assess the feasibility of detecting aflatoxin B1 (AFB1) on surfaces of 600 kernels of four maize varieties from different regions of the U.S.A. i.e. Georgia, Illinois, Indiana and Nebraska. For each variety, four AFB1 solutions (10, 20, 100 and 500 ppb) were artificially applied on kernel surfaces. Similarly, a control group was generated from 30 kernels of each variety treated with a solution of methanol. Principal component analysis (PCA) was used to reduce dimensionality of the HSI data followed by the application of factorial discriminant analysis (FDA) on the principal component variables. PCA results showed a pattern of separation between uncontaminated and contaminated kernels for all varieties except for Indiana and pooled samples. FDA showed the ability to predict AFB1 contamination of each variety with over 96% validation accuracy while prediction for AFB1 contamination group membership of pooled samples reached 98% accuracy in validation. Variation in the spectra of AFB1 contaminated kernels could have caused the variation in the predicted AFB1 contamination group membership. The PCA and FDA models where influenced by the chemical information from CH, NH and OH bonds of VNIR spectral regions. This study presents the potential of using VNIR hyperspectral imaging in direct AFB1 contamination classification studies of maize kernels of different varieties. The study further suggests that varietal differences of maize kernels may have no influence on AFB1 contamination classification.

      PubDate: 2017-12-27T13:47:02Z
      DOI: 10.1016/j.biosystemseng.2017.11.018
      Issue No: Vol. 166 (2017)
       
  • Nondestructive detection of zebra chip disease in potatoes using
           near-infrared spectroscopy
    • Authors: Pei-Shih Liang; Ronald P. Haff; Sui-Sheng T. Hua; Joseph E. Munyaneza; Tariq Mustafa; Siov Bouy L. Sarreal
      Pages: 161 - 169
      Abstract: Publication date: February 2018
      Source:Biosystems Engineering, Volume 166
      Author(s): Pei-Shih Liang, Ronald P. Haff, Sui-Sheng T. Hua, Joseph E. Munyaneza, Tariq Mustafa, Siov Bouy L. Sarreal
      Near-Infrared (NIR) spectroscopy (900–2600 nm) was evaluated as a rapid, non-destructive method for detection of zebra chip disease (ZC) in potatoes. Two models were tested; one that directly correlated spectra with ZC and one that measured sugar concentrations which in turn are known to be correlated with ZC. Applying stepwise regression in conjunction with canonical discriminant analysis to raw spectra, total classification accuracy of 98.35% was achieved in discriminating infected potatoes from control, with 2% false negative and 1% false positive error rates. The same analysis applied to 2nd derivative spectra yielded 97.25% accuracy with equal false negative and false positive error rates. Canonical discriminant analysis applied to sucrose, glucose, and fructose concentrations previously determined by high-performance liquid chromatography yielded 96.7% classification accuracy, with 4.3% false positive and 2.3% false negative rates. Accuracy did not significantly differ when fructose was excluded from the model. Partial least squares regression models built to predict sugar concentrations from the 2nd derivative NIR spectra resulted in R2 for actual vs. predicted concentrations of 0.7 and 0.72 respectively for sucrose and glucose, 0.63 for fructose, and 0.81 for total sugars. Given the relatively low R2 values in measuring sugar concentrations directly from the spectra it was concluded that classification accuracy is highest for models that directly correlate spectral features to ZC without considering sugar concentrations. Furthermore, this indicates that although NIR can detect infection, it may not be effective for evaluating severity of ZC in fresh potatoes.

      PubDate: 2017-12-27T13:47:02Z
      DOI: 10.1016/j.biosystemseng.2017.11.019
      Issue No: Vol. 166 (2017)
       
  • Hyperspectral imaging for the determination of potato slice moisture
           content and chromaticity during the convective hot air drying process
    • Authors: Waseem Amjad; Stuart O.J. Crichton; Anjum Munir; Oliver Hensel; Barbara Sturm
      Pages: 170 - 183
      Abstract: Publication date: February 2018
      Source:Biosystems Engineering, Volume 166
      Author(s): Waseem Amjad, Stuart O.J. Crichton, Anjum Munir, Oliver Hensel, Barbara Sturm
      Hyperspectral imaging (HSI) was utilised for the determination of moisture content of potato slices with three thicknesses (5 mm, 7 mm, 9 mm) at three drying temperatures (50 °C, 60 °C, 70 °C) during convective drying in a laboratory hot air dryer. The Page, thin-layer drying model was found better to explain the drying kinetics with a fitting accuracy of R2 (0.96–0.99) and lowest reduced Chi-square (0.00024–0.00090), Root mean square errors (RMSE) (0.014–0.026), and relative percentage error (1.5%–5.1%) under the used drying conditions. Spectral data were analysed using partial least squares regression (PLS) analysis, a multivariate calibration technique, alongside Monte Carlo Uninformative Variable Elimination (MCUVE-PLS) and competitive adaptive reweighted sampling (CARS-PLS). The feasibility of both moisture content and CIELAB prediction with a reduced wavelength set from the Visible near-infrared (VNIR) region (500–1000 nm) was investigated with these three models. The PLS model (R2 = 0.93–0.98, RMSE = 0.16–0.36 and the lowest number of optimal wavelengths = 6, for all drying conditions) was found suitable to implement for the moisture visualisation procedure. Potato chromaticity was also shown to be predictable during drying using a similar number of wavelengths, with PLS models for CIELAB a* performing well (R2 = 0.91–0.65, RMSE = 0.61–1.78). PLS Models for CIELAB b* more variably (R2 = 0.91–0.62, RMSE = 2.16–4.42) due to potato colour mainly varying along this axis. The current study showed that hyperspectral imaging was a useful tool for non-destructive measurement and visualisation of the moisture content and chromaticity during the drying process.

      PubDate: 2017-12-27T13:47:02Z
      DOI: 10.1016/j.biosystemseng.2017.12.001
      Issue No: Vol. 166 (2017)
       
  • PocketPlant3D: Analysing canopy structure using a smartphone
    • Authors: Roberto Confalonieri; Livia Paleari; Marco Foi; Ermes Movedi; Fosco M. Vesely; William Thoelke; Cristina Agape; Giulia Borlini; Irene Ferri; Federico Massara; Roberto Motta; Riccardo A. Ravasi; Sofia Tartarini; Camilla Zoppolato; Luca M. Baia; Andrea Brumana; Davide Colombo; Antonio Curatolo; Valerio Fauda; Denise Gaia; Andrea Gerosa; Antonio Ghilardi; Enrico Grassi; Andrea Magarini; Francesco Novelli; Fatima B. Perez Garcia; Andrea Rota Graziosi; Michele Salvan; Tommaso Tadiello; Laura Rossini
      Pages: 1 - 12
      Abstract: Publication date: December 2017
      Source:Biosystems Engineering, Volume 164
      Author(s): Roberto Confalonieri, Livia Paleari, Marco Foi, Ermes Movedi, Fosco M. Vesely, William Thoelke, Cristina Agape, Giulia Borlini, Irene Ferri, Federico Massara, Roberto Motta, Riccardo A. Ravasi, Sofia Tartarini, Camilla Zoppolato, Luca M. Baia, Andrea Brumana, Davide Colombo, Antonio Curatolo, Valerio Fauda, Denise Gaia, Andrea Gerosa, Antonio Ghilardi, Enrico Grassi, Andrea Magarini, Francesco Novelli, Fatima B. Perez Garcia, Andrea Rota Graziosi, Michele Salvan, Tommaso Tadiello, Laura Rossini
      Leaf angle and curvature are considered important by breeders for increasing plant productivity. We developed a smartphone app (PocketPlant3D) that makes use of the device accelerometer and magnetometer to measure leaf insertion angle and the leaf angles from the insertion to the tip, in turn used to reconstruct the 3D distribution of the angles of photosynthetic tissues. Starting from these angles, PocketPlant3D derives the mean leaf tilt angle (MTA) and the parameters of the Campbell and β leaf angle distributions (LAD), as well as a new leaf curvature indicator. The app was compared with other methods for quantifying precision in measuring leaf insertion angle on maize and sweetcorn (4320 leaf insertion angle measurements) via a ring test. Both precision metrics (repeatability and reproducibility) were similar for the different methods, with the exception of the digital inclinometer, which was the less precise. Concerning the analysis of canopy structure (a total of more than 72,000 angles were measured), PocketPlant3D allowed two genotypes to be discriminated for MTA and, especially, for the parameters of the two LADs and for the curvature indicator, whereas the two genotypes presented similar leaf insertion angles. The completeness of the information collected and the time effectiveness make PocketPlant3D a useful tool for phenotyping activities and ecophysiological studies.

      PubDate: 2017-12-13T06:38:28Z
      DOI: 10.1016/j.biosystemseng.2017.09.014
      Issue No: Vol. 164 (2017)
       
  • Effects of ventilator configuration on the flow pattern of a
           naturally-ventilated three-span Mediterranean greenhouse
    • Authors: Karlos Espinoza; Alejandro López; Diego L. Valera; Francisco D. Molina-Aiz; José A. Torres; Araceli Peña
      Pages: 13 - 30
      Abstract: Publication date: December 2017
      Source:Biosystems Engineering, Volume 164
      Author(s): Karlos Espinoza, Alejandro López, Diego L. Valera, Francisco D. Molina-Aiz, José A. Torres, Araceli Peña
      Natural ventilation used in agricultural greenhouses is important to control greenhouse microclimate. The effect of the ventilator configuration on the flow pattern of a three-span Mediterranean greenhouse with an obstacle to airflow (a neighbouring greenhouse) was investigated. Two different ventilator configurations, two or three half-arch roof vents with two roll-up side vents, were evaluated using sonic anemometry. It was observed that the flow pattern through the greenhouse depends of the ventilation surfaces distribution and the obstruction to the ventilation system. Moreover, the magnitude and distribution of ventilation surface affected the overall ventilation rate and the ventilation rate at plant level. The ventilator configuration with two roof and two side vents improved air movement at the plant level, although the overall volumetric flow rate was lower than that with three roof and two side vents.
      Graphical abstract image

      PubDate: 2017-12-13T06:38:28Z
      DOI: 10.1016/j.biosystemseng.2017.10.001
      Issue No: Vol. 164 (2017)
       
  • Internet of Things in agriculture, recent advances and future challenges
    • Authors: Antonis Tzounis; Nikolaos Katsoulas; Thomas Bartzanas; Constantinos Kittas
      Pages: 31 - 48
      Abstract: Publication date: December 2017
      Source:Biosystems Engineering, Volume 164
      Author(s): Antonis Tzounis, Nikolaos Katsoulas, Thomas Bartzanas, Constantinos Kittas
      The increasing demand for food, both in terms of quantity and quality, has raised the need for intensification and industrialisation of the agricultural sector. The “Internet of Things” (IoT) is a highly promising family of technologies which is capable of offering many solutions towards the modernisation of agriculture. Scientific groups and research institutions, as well as the industry, are in a race trying to deliver more and more IoT products to the agricultural business stakeholders, and, eventually, lay the foundations to have a clear role when IoT becomes a mainstream technology. At the same time Cloud Computing, which is already very popular, and Fog Computing provide sufficient resources and solutions to sustain, store and analyse the huge amounts of data generated by IoT devices. The management and analysis of IoT data (“Big Data”) can be used to automate processes, predict situations and improve many activities, even in real-time. Moreover, the concept of interoperability among heterogeneous devices inspired the creation of the appropriate tools, with which new applications and services can be created and give an added value to the data flows produced at the edge of the network. The agricultural sector was highly affected by Wireless Sensor Network (WSN) technologies and is expected to be equally benefited by the IoT. In this article, a survey of recent IoT technologies, their current penetration in the agricultural sector, their potential value for future farmers and the challenges that IoT faces towards its propagation is presented.

      PubDate: 2017-12-13T06:38:28Z
      DOI: 10.1016/j.biosystemseng.2017.09.007
      Issue No: Vol. 164 (2017)
       
  • Close range hyperspectral imaging of plants: A review
    • Authors: Puneet Mishra; Mohd Shahrimie Mohd Asaari; Ana Herrero-Langreo; Santosh Lohumi; Belén Diezma; Paul Scheunders
      Pages: 49 - 67
      Abstract: Publication date: December 2017
      Source:Biosystems Engineering, Volume 164
      Author(s): Puneet Mishra, Mohd Shahrimie Mohd Asaari, Ana Herrero-Langreo, Santosh Lohumi, Belén Diezma, Paul Scheunders
      The increasing need to develop a rapid understanding of plant functional dynamics has led to the employment of sensor technology for non-destructive assessment of plants. Hyperspectral Imaging (HSI) being an integration of two modalities, imaging and point spectroscopy, is nowadays emerging as a potential tool for rapid, non-destructive and automated close range assessment of plants functional dynamics both in terms of structure and physiology. Firstly, this paper presents an overview of some basic concepts of close range HSI on plants, concerning the plant–light interaction, instrumental setup, and spectral data analysis. Furthermore, the work reviews recent advances of HSI for plant related studies under controlled experimental conditions as well as in natural agricultural settings. Applications are discussed on foliar content estimation, variety identification, growth monitoring, stress and disease-related studies, phenotyping and adoption of HSI in high-throughput phenotyping platforms (HTPPs). Close range HSI is a challenging task and suffers from technical complexities related to external factors (e.g. illumination effects) as well as plant-related factors (e.g. complex plant geometry). The paper finally discusses some of the technical challenges related to the implementation of HSI in the close range assessment of plant traits.

      PubDate: 2017-11-02T15:44:29Z
      DOI: 10.1016/j.biosystemseng.2017.09.009
      Issue No: Vol. 164 (2017)
       
  • Modelled performance of energy saving air treatment devices to mitigate
           heat stress for confined livestock buildings in Central Europe
    • Authors: Ronja Vitt; Lutz Weber; Werner Zollitsch; Stefan J. Hörtenhuber; Johannes Baumgartner; Knut Niebuhr; Martin Piringer; Ivonne Anders; Konrad Andre; Isabel Hennig-Pauka; Martin Schönhart; Günther Schauberger
      Pages: 85 - 97
      Abstract: Publication date: December 2017
      Source:Biosystems Engineering, Volume 164
      Author(s): Ronja Vitt, Lutz Weber, Werner Zollitsch, Stefan J. Hörtenhuber, Johannes Baumgartner, Knut Niebuhr, Martin Piringer, Ivonne Anders, Konrad Andre, Isabel Hennig-Pauka, Martin Schönhart, Günther Schauberger
      Intensive pig and poultry production are predominantly performed in confined livestock buildings which are equipped with mechanical ventilation systems. The frequency of heat stress will increase due to climate change. Heat stress events are accompanied by performance depressions (e.g. daily weight gain, egg production, mortality, feed conversion rate). Consequently, appropriate air treatment devices can become necessary to optimise the indoor climate of confined livestock buildings because of a high inlet air temperature. In this study, we analysed the effects of three energy saving air treatment devices: (1) earth-air heat exchanger, (2) direct evaporative cooling by cooling pads, and (3) indirect evaporative cooling systems which combine evaporative cooling (e.g. by cooling pads) with a subsequent heat recovery system. All systems are compared to a reference ventilation system without air treatment, which is today's typical housing system. The results show that the earth-air heat exchanger (1) is the most efficient air treatment device. It eliminates heat stress and can also be used during wintertime to increase the inlet air temperature. The two adiabatic cooling systems (2) and (3) can reduce heat stress by about 90%. Cooling pads can lead to a high relative humidity of the inlet air between 75% and 100%, which can cause problems inside the livestock buildings, e.g. increasing the moisture content of the bedding material. The indirect cooling device can avoid this disadvantage at the expense of a reduced temperature reduction of the inlet air temperature and higher investment costs.
      Graphical abstract image

      PubDate: 2017-11-08T15:21:06Z
      DOI: 10.1016/j.biosystemseng.2017.09.013
      Issue No: Vol. 164 (2017)
       
  • Dynamic distribution of thermal effects of an oscillating frost protective
           fan in a tea field
    • Authors: Kensuke Kimura; Daisuke Yasutake; Kentaro Nakazono; Masaharu Kitano
      Pages: 98 - 109
      Abstract: Publication date: December 2017
      Source:Biosystems Engineering, Volume 164
      Author(s): Kensuke Kimura, Daisuke Yasutake, Kentaro Nakazono, Masaharu Kitano
      Wind machines are one of the most effective and prevalent methods of frost protection for crops. However, their physical and physiological effectiveness should be re-examined because climate change from global warming can reduce the cold tolerance of crops, leading to higher risk of frost damage. Here, we experimentally elucidate thermal effects of a wind machine by spatiotemporal analysis of leaf heat balance, with the aid of a new method facilitating continuous and multipoint evaluation of leaf boundary layer conductance (G A). G A regulates convective heat exchange between leaves and ambient air. This analysis was performed in a tea field with a frost protective fan (oscillating wind machine), thereby visualising the spatiotemporal distribution of thermal effects from the oscillating fan. Those effects showed a dynamically varying distribution in space and time, as shown in the supplementary animation, under strong influences of both changes in G A and air-to-leaf temperature difference. The change in G A was synchronised with airflow from the oscillating fan onto leaves. However, the change in air-to-leaf temperature difference was complicated, with delay in transient thermal responses of leaves and ambient air. These spatiotemporal characteristics of thermal effects varied substantially with field location, owing to different intervals and durations of airflow from the oscillating fan. Consequently, remarkable non-uniformities in thermal effects appeared across the field, increasing potential risk of frost damage. These results indicate that the effectiveness of the fan still has room for improvement by considering the spatiotemporal characteristics of thermal effects reported herein, toward more reliable frost protection under global warming.

      PubDate: 2017-11-08T15:21:06Z
      DOI: 10.1016/j.biosystemseng.2017.09.010
      Issue No: Vol. 164 (2017)
       
  • Optimised forage harvester routes as solutions to a traveling salesman
           problem with clusters and time windows
    • Authors: Ana Cerdeira-Pena; Luisa Carpente; Carlos Amiama
      Pages: 110 - 123
      Abstract: Publication date: December 2017
      Source:Biosystems Engineering, Volume 164
      Author(s): Ana Cerdeira-Pena, Luisa Carpente, Carlos Amiama
      In this work we study a variant of the traveling salesman problem with additional constraints of clusters, time windows, and processing times at the cities. We apply this model to a real-world problem related to an agricultural cooperative that needs to optimise the routes of several harvesters, and where getting the exact solution has been proved to be hopeless for large instances (see Carpente et al. (2010)). We introduce, implement and test two different heuristic algorithms based on tabu search and simulated annealing philosophy, respectively. An exhaustive experimental evaluation over several sets of real data shows that the simulated annealing approach exhibits a solid performance even on the most complex instances, while the tabu search based approach worsens with complexity. 1 Moreover, the optimised schedules corresponded to important economic savings. For this reason the cooperative has already successfully adopted the proposed heuristic in its regular planning activities.

      PubDate: 2017-11-08T15:21:06Z
      DOI: 10.1016/j.biosystemseng.2017.10.002
      Issue No: Vol. 164 (2017)
       
  • Class-based physical properties of air-classified sunflower seeds and
           kernels
    • Authors: Simon Munder; Dimitrios Argyropoulos; Joachim Müller
      Pages: 124 - 134
      Abstract: Publication date: December 2017
      Source:Biosystems Engineering, Volume 164
      Author(s): Simon Munder, Dimitrios Argyropoulos, Joachim Müller
      High oleic sunflower seeds, with higher oil content and greater oleic acid content than other sunflower seeds, are gaining in economic importance. However, there are still major gaps in knowledge concerning their post-harvest handling. This study was carried out, to establish a simply implemented method of classifying the seeds via air-separation, which allows rapid and precise assignment of high oleic sunflower seeds into different quality classes. Physical properties were evaluated and compared to earlier studies. A low hull to kernel ratio of 0.26 ± 0.04 was found, with seed masses ranging from 0.024 to 0.108 g. A high positive correlation between seed mass and kernel mass (r = 0.993) was observed but negligible correlations between seed length, width, thickness and mass were found. Seed mass analysis from the air-separation classes indicated highly precise (F 1 score = 0.966) allocation into mass based classes, with below 5% false positives. Thus, a significant difference between hull to kernel ratio, bulk density, porosity, sphericity, the angle of repose, and rupture force in vertical and transversal orientation was found for the different classes. Rupture force in the horizontal orientation, true density and static friction did not reveal any class differences. Threshold values for air velocity where therefore established for air-separation that can classify seeds of different qualities and optimise postharvest seed handling operations.

      PubDate: 2017-11-08T15:21:06Z
      DOI: 10.1016/j.biosystemseng.2017.10.005
      Issue No: Vol. 164 (2017)
       
  • Cost analysis of parcel fragmentation in agriculture: The case of
           traditional olive cultivation
    • Authors: Manuel Perujo Villanueva; Sergio Colombo
      Pages: 135 - 146
      Abstract: Publication date: December 2017
      Source:Biosystems Engineering, Volume 164
      Author(s): Manuel Perujo Villanueva, Sergio Colombo
      Land fragmentation and dispersion hampers the optimisation of agricultural management. In this study, a methodology is proposed to quantify the extra costs to farmers caused by the spatial dispersion among agricultural parcels in their farms. The methodology proposed measures the length of the trips between parcels in the same farm, adds the inoperative periods due to the travel and then estimates the additional costs for fuel and labour. The study focuses on traditional olive cultivation in the province of Jaen, Spain, as a major crop with a highly fragmented and scattered structure. Thus, the profitability of this crop is seriously reduced so lower production costs become necessary to prevent crop abandonment. The results indicate that the dispersion of the olive parcels has a significant impact on production costs, with cases in which the expense of travel between parcels represents more than a quarter of the overall farming costs. The significant impact of the spatial dispersion of the parcels on production costs evidences the need for policies either to enlarge farm size or to encourage shared cultivation to offset the spatial scattering of olive parcels.

      PubDate: 2017-11-08T15:21:06Z
      DOI: 10.1016/j.biosystemseng.2017.10.003
      Issue No: Vol. 164 (2017)
       
  • Quantitative evaluation of pork marbling score along Longissimus thoracis
           using NIR images of rib end
    • Authors: Hui Huang; Li Liu; Michael O. Ngadi
      Pages: 147 - 156
      Abstract: Publication date: December 2017
      Source:Biosystems Engineering, Volume 164
      Author(s): Hui Huang, Li Liu, Michael O. Ngadi
      This study investigated the potential of using the image of the rib end to assess pork marbling scores along Longissimus thoracis. Previous studies have shown the success of hyperspectral imaging in marbling score assessment of individual pork chops. In this research, hyperspectral imaging methods were employed to study the possibility of pork marbling assessment along longissimus thoracis using the NIR image of the rib end. Pattern analysis algorithms were used to extract efficient features from the rib end of the pork loin. Multiple linear regression (MLR) and leave-one-out cross validation were applied to build prediction models using the extracted features. Experiments showed that the prediction models performed well with correlation coefficients of calibration (Rc) ≥ 0.94, correlation coefficients of cross validation (Rcv) ≥ 0.88, correlation coefficients of prediction (Rp) ≥ 0.89. The promising results indicated the potential of using NIR images of rib ends for the assessment of marbling score along longissimus thoracis.

      PubDate: 2017-11-08T15:21:06Z
      DOI: 10.1016/j.biosystemseng.2017.10.004
      Issue No: Vol. 164 (2017)
       
  • Early detection of freezing damage in sweet lemons using Vis/SWNIR
           spectroscopy
    • Authors: Shahram Moomkesh; Seyed Ahmad Mireei; Morteza Sadeghi; Majid Nazeri
      Pages: 157 - 170
      Abstract: Publication date: December 2017
      Source:Biosystems Engineering, Volume 164
      Author(s): Shahram Moomkesh, Seyed Ahmad Mireei, Morteza Sadeghi, Majid Nazeri
      Three modes of measurement, reflectance, half-transmittance, and full-transmittance were examined to detect the freeze-damaged sweet lemons within the range 400–1100 nm. Soft independent modelling of class analogy (SIMCA), principal components analysis combined with artificial neural networks (PCA-ANN), and support vector machines (SVM) methods were conducted on the whole spectral information to detect freezing damage in the sweet lemons subjected to different laboratory simulated freeze conditions. Among the measurement modes, it was found that the half-transmittance outperformed the reflectance and full-transmittance by using all classifiers, such that the corresponding classification accuracy was 100% by using the PCA-ANN algorithm. The discrimination power plot of the SIMCA analysis obtained from the half-transmittance mode was then used to attain the effective features. To compare the performance of the features obtained from SIMCA analysis, a sensitivity analysis was carried out to extract the new informative wavelengths. For each feature selection procedure, twelve wavelength variables in the vicinity of four main wavelengths were found to be superior; then they were used to build new classification models. The test set validation results of ANN and SVM techniques revealed that SIMCA-based features led to better classification accuracies in comparison with the features obtained from sensitivity analysis. Among the ANN and SVM classifiers, the best performance was obtained by the ANN with the total accuracy of 96.3%, by using SIMCA-based features. The findings of this study can be useful for developing an online sweet lemon sorting system to detect the freezing damage.

      PubDate: 2017-11-15T19:22:21Z
      DOI: 10.1016/j.biosystemseng.2017.10.009
      Issue No: Vol. 164 (2017)
       
  • Semi-automated, machine vision based maize kernel counting on the ear
    • Authors: Tony E. Grift; Wei Zhao; Md Abdul Momin; Yu Zhang; Martin O. Bohn
      Pages: 171 - 180
      Abstract: Publication date: December 2017
      Source:Biosystems Engineering, Volume 164
      Author(s): Tony E. Grift, Wei Zhao, Md Abdul Momin, Yu Zhang, Martin O. Bohn
      In synchrony with the overall trend toward automation in plant phenotyping, two semi-automatic machine vision methods were devised targeted at counting maize kernels directly on de-husked ears. The ears represented row morphologies ranging from straight to curved, they featured missing kernels, underdeveloped kernels, and broken kernels, but displayed no disease or mould. The first method mimicked a common manual field method of estimating the total ear kernel count, based on counting the number of rows and multiplication by the number of kernels per row. Similarly, in this paper, the operator manually counted the number of rows, and also manually counted the number of kernels in a row image within an (operator determined) quasi-cylindrical mid-section of the ear. The total ear kernel count was then estimated by multiplying the number of rows by the number of kernels per row, yielding full ear extrapolation by multiplication by the ratio between the total ear length and the length of the quasi-cylindrical mid-section. This full ear image based approach achieved a kernel counting error ranging from −7.67% (under-count) to +8.60% (multi-count) among 23 maize ears. The second method only observed a fixed quasi-cylindrical mid-section of the ear. Image frames were acquired of each individual row of kernels located in the quasi-cylindrical mid-section, yielding kernel maps. Among 12 maize ears, the kernel missing error ranged from 0 to 4.24% and the multi-count error ranged from 0 to 1.92%. In total, 41 existing kernels were missed and 25 kernels were multi-counted among 2713 kernels counted.
      Graphical abstract image

      PubDate: 2017-11-15T19:22:21Z
      DOI: 10.1016/j.biosystemseng.2017.10.010
      Issue No: Vol. 164 (2017)
       
  • Contamination detection in fresh natural rubber latex by a dry rubber
           content measurement system using microwave reflectometer
    • Authors: Sahapong Somwong; Phairote Wounchoum; Mitchai Chongcheawchamnan
      Pages: 181 - 188
      Abstract: Publication date: December 2017
      Source:Biosystems Engineering, Volume 164
      Author(s): Sahapong Somwong, Phairote Wounchoum, Mitchai Chongcheawchamnan
      This paper proposes a classifier for contamination detection in fresh latex. It relates to a dry rubber content (DRC) measurement system implemented with a 1 GHz microwave reflectometer (MWR), which behaves anomalously when fresh latex field samples are contaminated with cassava flour or CaCO3 by 10% by mass or more. The relative permittivity ( ε r ) of non-contaminated and contaminated samples were investigated and an algorithm for detecting contaminants is proposed. The performance of this algorithm was tested experimentally and practically significant contamination levels were diagnosed with high accuracy.

      PubDate: 2017-11-15T19:22:21Z
      DOI: 10.1016/j.biosystemseng.2017.10.013
      Issue No: Vol. 164 (2017)
       
  • Improving the effectiveness of heat treatment for insect pest control in
           flour mills by thermal simulations
    • Authors: Simona M.C. Porto; Francesca Valenti; Salvatore Bella; Agatino Russo; Giovanni Cascone; Claudia Arcidiacono
      Pages: 189 - 199
      Abstract: Publication date: December 2017
      Source:Biosystems Engineering, Volume 164
      Author(s): Simona M.C. Porto, Francesca Valenti, Salvatore Bella, Agatino Russo, Giovanni Cascone, Claudia Arcidiacono
      The aim of this study was the development of a method to improve the effectiveness of heat treatment (HT) for insect pest control in flour mills by thermal analyses and temperature trend models. Specific attention was paid to surface temperatures of thermal bridges (TBs), which represent HT weakness points because they provide refuge to insects and increase expenditure of electric power due to heat flux loss. Air and TB surface temperatures were monitored in a flour mill during execution of an HT. A first thermal analysis showed that values of indoor air temperatures near TBs were always lower than the computed temperature level that would also guarantee insect mortality on TB surfaces. Since the length of the steady-state air temperature profile was lower than that lethal to all insect vital stages, time series forecasting models based on trend analyses were used to simulate the suitable HT duration. The results highlighted that HT should be increased by 9 h to achieve an air temperature above 45 °C. Finally, to obtain TB surface temperatures lethal to insects, simulations were performed of building interventions capable of reducing sensible heat flux loss through the TBs by using insulating materials. The method described in this paper could help operators define a more suitable HT length and support flour mill owners in decision-making when building interventions to improve heat capacity of the mill envelope should be considered to reduce the power consumption of HT.

      PubDate: 2017-12-13T06:38:28Z
      DOI: 10.1016/j.biosystemseng.2017.10.015
      Issue No: Vol. 164 (2017)
       
  • Including a one-year grass ley increases soil organic carbon and decreases
           greenhouse gas emissions from cereal-dominated rotations – A Swedish
           farm case study
    • Authors: Thomas Prade; Thomas Kätterer; Lovisa Björnsson
      Pages: 200 - 212
      Abstract: Publication date: December 2017
      Source:Biosystems Engineering, Volume 164
      Author(s): Thomas Prade, Thomas Kätterer, Lovisa Björnsson
      Increased soil organic carbon (SOC) content has been shown to increase soil fertility and carbon sequestration, but SOC changes are frequently neglected in life cycle assessment (LCA) studies of crop production. This study used a novel LCA application using simulated SOC changes to examine the greenhouse gas (GHG) impact of a combined food and energy crop production from a crop rotation perspective. On a case pig farm, introduction of one year of grass ley into a cereal-dominated crop rotation was simulated. The grass and pig manure were used for biogas production and the digestion residues were used as fertiliser on the farm. This crop rotation shift increased the SOC stocks by an estimated 27 and 49% after 50 years and at steady state, respectively. The estimated corresponding net wheat yield increase due to higher SOC was 8–16% and 16–32%, respectively, indicating that initial loss of low-yield oat production can be partly counterbalanced. Net SOC increase (corresponding to 2 t CO2-eq ha−1 a−1) was the single most important variable affecting the GHG balance. When biogas replaced fossil fuels, GHG emissions of the combined energy-food crop rotation were approx. 3 t CO2-eq ha−1 a−1 lower than for the current food crop rotation. Sensitivity analyses led to variation of only 2–9% in the GHG balance. This study indicates that integrated food and energy crop production can improve SOC content and decrease GHG emissions from cropping systems. It also demonstrates the importance of including SOC changes in crop production-related LCA studies.
      Graphical abstract image

      PubDate: 2017-12-13T06:38:28Z
      DOI: 10.1016/j.biosystemseng.2017.10.016
      Issue No: Vol. 164 (2017)
       
  • High-throughput platform for automated sorting and selection of single
           seeds based on time-domain nuclear magnetic resonance (TD-NMR) measurement
           of oil content
    • Authors: Albrecht E. Melchinger; Simon Munder; Franz J. Mauch; Vilson Mirdita; Juliane Böhm; Joachim Müller
      Pages: 213 - 220
      Abstract: Publication date: December 2017
      Source:Biosystems Engineering, Volume 164
      Author(s): Albrecht E. Melchinger, Simon Munder, Franz J. Mauch, Vilson Mirdita, Juliane Böhm, Joachim Müller
      Sorting and selection of individual seeds by their oil content (OC) or oil mass from larger quantities of seeds is an important step for many applications in the breeding of oil seed crops. Time-domain nuclear magnetic resonance (NMR) has proved to be a very precise method for non-destructive OC measurement of seeds; however, benchtop NMR devices are not automated for high throughput. Our objectives were to construct a high-throughput platform for (i) singling seeds from bulks, (ii) measurement of their mass, (iii) measurement of their oil mass with NMR, and (iv) either sorting the measured seeds into fractions on the basis of their OC or placement of seeds individually in matrix trays for subsequent selection based on the recorded seed data. Modules for each of these tasks, some newly developed, were linked in a novel approach by transporting single seeds between modules using a combination of pneumatic and mechanical elements, as well as software for control of the platforms' parts and for remote control. Our platform enables fully automated measurement of up to 600 seeds h−1. Maize seeds were used to demonstrate the applicability of our platform for measurements of OC and seed mass. For both traits, repeatability and accuracy were extraordinarily high. The platform proved robust and stable in long series of measurements and represents a break-through for OC determination in the breeding of maize and oil seed crops.

      PubDate: 2017-12-13T06:38:28Z
      DOI: 10.1016/j.biosystemseng.2017.10.011
      Issue No: Vol. 164 (2017)
       
  • Wind-driven natural ventilation of greenhouses with vegetation
    • Authors: Chia-Ren Chu; Ting-Wei Lan; Ren-Kai Tasi; Tso-Ren Wu; Chih-Kai Yang
      Pages: 221 - 234
      Abstract: Publication date: December 2017
      Source:Biosystems Engineering, Volume 164
      Author(s): Chia-Ren Chu, Ting-Wei Lan, Ren-Kai Tasi, Tso-Ren Wu, Chih-Kai Yang
      A large eddy simulation (LES) model was used to examine the wind-driven cross ventilation of gable-roof greenhouses containing vegetation. The obstruction of air flow by vegetation was described by a porous drag model in the numerical model, and the simulation results were validated using wind tunnel experiments. The numerical model was then utilised to inspect the influences of vegetation and greenhouse length (in the wind direction) on the ventilation rate. The results revealed that the diminishing effects of the vegetation, insect screen and internal friction on the ventilation rate can all be quantified by a physical-based resistance model. The driving force (the difference between windward and leeward pressures) of long, multi-span greenhouses was found to be less than that of a short, single-span greenhouse leading to a lower ventilation rate. The resistance factor of the vegetation and the insect screen depends on their porosity, while the resistance factor of the internal friction increased as the greenhouse length increased. In addition, the internal friction of multi-span greenhouses should be considered when the length of the greenhouse was greater than six times the greenhouse height.

      PubDate: 2017-12-13T06:38:28Z
      DOI: 10.1016/j.biosystemseng.2017.10.008
      Issue No: Vol. 164 (2017)
       
  • Potential of turbulence interference in rough rice bin drying and storage
           systems fitted with cabling technology
    • Authors: Gbenga A. Olatunde; Griffiths G. Atungulu
      Pages: 1 - 14
      Abstract: Publication date: November 2017
      Source:Biosystems Engineering, Volume 163
      Author(s): Gbenga A. Olatunde, Griffiths G. Atungulu
      Modern on-farm in-bin drying and storage systems are equipped with networks of sensors to monitor air and grain conditions throughout the bins. However, the performance of sensors may be impaired by turbulent flow of the air through the grain mass. Computational fluid dynamic (CFD) simulations allow a more detailed view of fluid flow around the sensors to be obtained. The objectives of this work are to investigate the potential of turbulence generation around the sensors and the potential of turbulence to impair sensor performance. A finite volume method with porous media formulation was employed to simulate the turbulent airflow. The turbulence intensities predicted from different turbulence models (K−ɛ model, k−ω model and the Reynolds stress model (RSM)) were investigated and the result of airflow rates of 1.1 m3 min−1 [air] t−1 [rice] is presented. The results revealed that the turbulence models predicted a low to medium turbulence with intensities ranging between 1% and 2%. Sensors closer to the plenum experienced about 100% increase in turbulence intensity compared to the sensors closer to the outlet. Comparing the turbulence models used, RSM model require the least computational duration with average of 5 h simulation time compared with K–ɛ model and k –ω model requiring 9–42 h and 40 h, respectively. It can be concluded that sensors closer to the plenum should be built to cope with medium scale turbulent flow.

      PubDate: 2017-09-08T09:15:21Z
      DOI: 10.1016/j.biosystemseng.2017.08.010
      Issue No: Vol. 163 (2017)
       
  • Investigating air leakage and wind pressure coefficients of single-span
           plastic greenhouses using computational fluid dynamics
    • Authors: Takeshi Kuroyanagi
      Pages: 15 - 27
      Abstract: Publication date: November 2017
      Source:Biosystems Engineering, Volume 163
      Author(s): Takeshi Kuroyanagi
      Air leakage from greenhouses not only influences heating load and the carbon dioxide supply, but also affects wind loads on the greenhouse structure. Quantitative evaluation of the greenhouse air leakage is essential to estimate variable costs and achieve reasonable designs for greenhouses with adequate strength. In this study, greenhouse leakage rate was estimated through a combination of CFD simulation of the external pressure coefficients of the greenhouse cladding and modelling of airflow through leakage paths on the greenhouse walls. The simulation results of the leakage rate were validated by the experimental results obtained from two greenhouses with the same structure but different orientation. The correlation coefficients between the simulated and measured values ranged from 0.82 to 0.99, and the RMSE of the simulated leakage rate ranged from 0.014 to 0.052. The simulation results indicated that a strong transverse wind created lower leakage rate and internal pressure coefficient. These findings and methodology will be helpful for designing light-weight greenhouses in windy regions.

      PubDate: 2017-09-08T09:15:21Z
      DOI: 10.1016/j.biosystemseng.2017.08.004
      Issue No: Vol. 163 (2017)
       
  • The impact of transient heat transfer on tissue culture cell distribution
    • Authors: Jeffrey D. Brown; Heather E. Dillon; Dorcas V. Kaweesa; Arden M. Harada
      Pages: 28 - 36
      Abstract: Publication date: November 2017
      Source:Biosystems Engineering, Volume 163
      Author(s): Jeffrey D. Brown, Heather E. Dillon, Dorcas V. Kaweesa, Arden M. Harada
      It is common practice to subculture adherent eukaryotic cells in multi-well plates at room temperature before 37 °C incubation for growth. Under these conditions, cell distribution was non-uniform with a higher density of cells near the edges of the wells. Non-uniform cell density can alter cell behaviour in numerous biological assays and can result in variability when using automated plate readers on intact cells. This study investigated the possibility that the non-uniform cell distribution was caused by temperature gradients in the growth medium and well walls. Cell density analyses revealed significantly greater cell densities near well walls. Temperature distribution was documented using infrared imaging and temperature-sensitive LCD films, and a transient heat transfer mathematical model was developed to characterise the system and compared to the cell density results. The model predicts that an initial less-than-1 °C temperature gradient is present in the well shortly after initiating 37 °C incubation, leading to preferential cell adhesion to the warmer edges of wells in the first ∼30 min of incubation. Techniques to remediate non-uniform cell distribution were evaluated, and a simple method proved effective to promote uniform cell densities across wells of 24-well plates.
      Graphical abstract image

      PubDate: 2017-09-08T09:15:21Z
      DOI: 10.1016/j.biosystemseng.2017.08.009
      Issue No: Vol. 163 (2017)
       
  • 3D Computer-vision system for automatically estimating heifer height and
           body mass
    • Authors: Oron Nir; Yisrael Parmet; Daniel Werner; Gaby Adin; Ilan Halachmi
      Abstract: Publication date: Available online 18 December 2017
      Source:Biosystems Engineering
      Author(s): Oron Nir, Yisrael Parmet, Daniel Werner, Gaby Adin, Ilan Halachmi
      Animal dimensions play a vital role in providing data in support of management decisions regarding livestock. Nevertheless, dairy heifers are still measured manually, a time consuming and stressful task for both the farmer and the animal. This research suggests an approach that utilises a fully automated system to measure a heifer's body. The methodology involves a single low-cost Microsoft Kinect V2 Time-of-Flight 3D sensor, computer vision, machine learning, and object recognition using ellipse fitting with quantile regression as part of the feature extraction phase. The camera was installed at the Volcani Center dairy farm, on the ceiling above a free-walk path between the feeding zone and lying area. Video data of 107 Israeli Holstein heifers were recorded and validated against “gold references” (human-observed body mass, hip height and withers height). The tested system improved the normalised Root Mean Squared Error of estimates over the state of the art models by 70.4%, 69.8% and 42.6% for withers height, hip height, and body mass respectively. The models were also validated on a different dairy farm and yielded similar results. The methodology, may be adapted and applied to other elliptically shaped animal bodies, such as sheep, pigs, horses, and buffalo.

      PubDate: 2017-12-27T13:47:02Z
      DOI: 10.1016/j.biosystemseng.2017.11.014
       
  • Classification of vitamin A deficiency levels by ocular changes in
           Japanese black cattle
    • Authors: Shuqing Han; Naoshi Kondo; Yuichi Ogawa; Tetsuhito Suzuki; Moriyuki Fukushima; Namiko Kohama; Tateshi Fujiura; Jianhua Zhang; Fantao Kong; Jianzhai Wu
      Abstract: Publication date: Available online 15 December 2017
      Source:Biosystems Engineering
      Author(s): Shuqing Han, Naoshi Kondo, Yuichi Ogawa, Tetsuhito Suzuki, Moriyuki Fukushima, Namiko Kohama, Tateshi Fujiura, Jianhua Zhang, Fantao Kong, Jianzhai Wu
      To help Japanese black cattle farmers diagnose vitamin A deficiency (VAD) levels in cattle, eye images of 40 cattle were recorded monthly during their vitamin A manipulated period using a 2-CCD camera. Ocular features extracted from images, including pupil colour, pupillary light reflex and light reflection, were investigated. Multivariate classification methods (SIMCA and PLS-DA) were used to classify cattle into mild, moderate and severe VAD groups. Five variables (r, I, CA, IPR, I_RFL) were used for classification. The mild and severe VAD groups could be classified with over 85% correct classification rate. However, the moderate VAD group could not be discriminated adequately. A VAD index was developed and proven to be effective in representing VAD status. The results showed the potential for ocular changes to be utilised as an aid to farm management.

      PubDate: 2017-12-27T13:47:02Z
      DOI: 10.1016/j.biosystemseng.2017.11.011
       
  • Prediction of calving in dairy cows using a tail-mounted tri-axial
           accelerometer: A pilot study
    • Authors: Stefanie Krieger; Georg Sattlecker; Florian Kickinger; Wolfgang Auer; Marc Drillich; Michael Iwersen
      Abstract: Publication date: Available online 14 December 2017
      Source:Biosystems Engineering
      Author(s): Stefanie Krieger, Georg Sattlecker, Florian Kickinger, Wolfgang Auer, Marc Drillich, Michael Iwersen
      Dystocia and stillborn calves are recurring problems for dairy farms, leading to high costs. By implementing a real-time monitoring system, problems could be reduced by supporting farmers to detect the onset of parturition and to intervene in case of dystocia. The hypothesis of this study was that the onset of parturition is detectable through analyses of the movement pattern of the dam's tail, recorded by a tri-axial accelerometer that was fixed on the upper part of the cow's tail. Cows (n = 5, approx. 1 week before calving) were housed in single straw-bedded boxes. Animal behaviour was video-recorded (24 h/d) and evaluated by encoding the events ‘frequency and duration of tail raising 5 h pre-partum’, ‘rupture of the amniotic sac’ and ‘expulsion of the calf’. In parallel, the accelerometer data collected from two days before calving to parturition were analysed. We developed an algorithm to detect the tail raising and created a decision function based on the frequency and duration of the tail raising. Exceeding the threshold led to a birth alarm. In each of the evaluated calvings, the alarm was triggered a short time before the expulsion of the calves, at 33, 32, 121, 6 and 71 min for cows 1 to 5, respectively. These preliminary results indicate that an accelerometer to detect tail movements may be useful to predict parturition. Further research is required to refine the algorithm and the decision function, to analyse predictability of dystocia and to develop a real-time alarm system under field conditions.

      PubDate: 2017-12-27T13:47:02Z
      DOI: 10.1016/j.biosystemseng.2017.11.010
       
  • Paraconsistent logic used for estimating the gait score of broiler
           chickens
    • Authors: Irenilza de A. Nääs; Luiz Carlos M. Lozano; Saman Abdanan Mehdizadeh; Rodrigo G. Garcia; Jair M. Abe
      Abstract: Publication date: Available online 8 December 2017
      Source:Biosystems Engineering
      Author(s): Irenilza de A. Nääs, Luiz Carlos M. Lozano, Saman Abdanan Mehdizadeh, Rodrigo G. Garcia, Jair M. Abe
      Visually estimating the gait score in a flock is not a precise task. The evaluation is done based on scores from 0 (sound bird) to 5 (lame bird). The extremes are easily identifiable; however, the intermediate scores are not evident. This study aimed to develop an algorithm and software to estimate broiler gait score. Video images were recorded from broilers walking on a special platform. The walkway was covered with bedding substrate. A blue panel provided a background to contrast with the birds. Selected broilers from the different gait scores were placed to walk on the runway, and a video was recorded. An algorithm was developed to analyse the video streams. The centroid of the chicken body was detected, through which the broiler's speed, and acceleration from gait score were calculated. The velocity and acceleration data were analysed using paraconsistent logic, and an algorithm was developed. The software was able to predict the intermediate values of broiler gait score with a low degree of uncertainty, given the broiler velocity and acceleration. For the estimation of GS 1, we obtained 50% accuracy. For GS 2 the estimation was 70% precise, and for GS 3, the results were 100% accurate. During the auditing of the flock welfare process, the intermediate results of broiler gait score are visually difficult to identify. Using the developed software it might be possible to detect lameness in broilers under commercial rearing since the velocity of displacement can be easily measured and used as input data by the growers.

      PubDate: 2017-12-13T06:38:28Z
      DOI: 10.1016/j.biosystemseng.2017.11.012
       
  • Sensing and control of crop water status
    • Authors: Werner B. Herppich; Manuela Zude-Sasse
      Abstract: Publication date: Available online 7 December 2017
      Source:Biosystems Engineering
      Author(s): Werner B. Herppich, Manuela Zude-Sasse


      PubDate: 2017-12-13T06:38:28Z
      DOI: 10.1016/j.biosystemseng.2017.11.013
       
  • Sugar beet and volunteer potato classification using Bag-of-Visual-Words
           model, Scale-Invariant Feature Transform, or Speeded Up Robust Feature
           descriptors and crop row information
    • Authors: Hyun K. Suh; Jan Willem Hofstee; Joris IJsselmuiden; Eldert J. van Henten
      Abstract: Publication date: Available online 7 December 2017
      Source:Biosystems Engineering
      Author(s): Hyun K. Suh, Jan Willem Hofstee, Joris IJsselmuiden, Eldert J. van Henten
      One of the most important steps in vision-based weed detection systems is the classification of weeds growing amongst crops. In the EU SmartBot project it was required to effectively control more than 95% of volunteer potatoes and ensure less than 5% of damage of sugar beet. Classification features such as colour, shape and texture have been used individually or in combination for classification studies but they have proved unable to reach the required classification accuracy under natural and varying daylight conditions. A classification algorithm was developed using a Bag-of-Visual-Words (BoVW) model based on Scale-Invariant Feature Transform (SIFT) or Speeded Up Robust Feature (SURF) features with crop row information in the form of the Out-of-Row Regional Index (ORRI). The highest classification accuracy (96.5% with zero false-negatives) was obtained using SIFT and ORRI with Support Vector Machine (SVM) which is considerably better than previously reported research although its 7% false-positives deviated from the requirements. The average classification time of 0.10–0.11 s met the real-time requirements. The SIFT descriptor showed better classification accuracy than the SURF, but classification time did not vary significantly. Adding location information (ORRI) significantly improved overall classification accuracy. SVM showed better classification performance than random forest and neural network. The proposed approach proved its potential under varying natural light conditions, but implementing a practical system, including vegetation segmentation and weed removal may potentially reduce the overall performance and more research is needed.

      PubDate: 2017-12-13T06:38:28Z
      DOI: 10.1016/j.biosystemseng.2017.11.015
       
  • A novel dielectric tensiometer enabling precision PID-based irrigation
           control of polytunnel-grown strawberries in coir
    • Authors: Martin S. Goodchild; Malcolm D. Jenkins; William R. Whalley; Chris W. Watts
      Abstract: Publication date: Available online 6 December 2017
      Source:Biosystems Engineering
      Author(s): Martin S. Goodchild, Malcolm D. Jenkins, William R. Whalley, Chris W. Watts
      The benefits of closed-loop irrigation control have been demonstrated in grower trials which show the potential for improved crop yields and resource usage. Managing water use, by controlling irrigation in response to soil or substrate moisture changes, to meet crop water demands is a popular approach but requires substrate specific moisture sensor calibrations and knowledge of the moisture levels that result in water deficit or over-watering. The use of water tension sensors removes the need for substrate specific calibration and enables a more direct relationship with hydraulic conductivity. In this paper, we present a novel dielectric tensiometer that has been designed specifically for use in soil-free substrates such as coir, peat and Rockwool with a water tension measurement range of −0.7 kPa to −2.5 kPa. This new sensor design has also been integrated with a precision PID-based (drip) irrigation controller in a small-scale coir substrate strawberry growing trial: 32 strawberry plants in 4 coir growbags under a polytunnel. The data illustrates that excellent regulation of water tension in coir can be achieved which delivers robust and precise irrigation control - matching water delivery to the demands of the plants. During a 30-day growing period vapour pressure deficit (VPD) and daily water use data was collected and the irrigation controller set to maintain coir water tension at the following levels: −0.90 kPa, −0.95 kPa and −1 kPa for at least 7 consecutive days at each level. For each set-point the coir water tension was maintained by the irrigation controller to within ±0.05 kPa. Meanwhile the polytunnel VPD varied diurnally from 0 to a maximum of 5 kPa over the trial period. Furthermore, the combination of the dielectric tensiometer and the method of PID-based irrigation control resulted in a linear relationship between daily average VPD and daily water use over 10 days during the cropping period.

      PubDate: 2017-12-13T06:38:28Z
      DOI: 10.1016/j.biosystemseng.2017.10.018
       
  • Hyperspectral machine vision as a tool for water stress severity
           assessment in soilless tomato crop
    • Authors: A. Elvanidi; N. Katsoulas; K.P. Ferentinos; T. Bartzanas; C. Kittas
      Abstract: Publication date: Available online 22 November 2017
      Source:Biosystems Engineering
      Author(s): A. Elvanidi, N. Katsoulas, K.P. Ferentinos, T. Bartzanas, C. Kittas
      Early detection of water deficit stress is essential for efficient crop management. In this study, hyperspectral machine vision was used as a non-contact technique for detecting changes in spectral reflectance of a soilless tomato crop grown under varying irrigation regimes. Four different irrigation treatments were imposed in tomato plants grown in slabs filled with perlite. The plants were grown in a growth chamber under controlled temperature and light conditions, and crop reflectance measurements were made using a hyperspectral camera to measure the radiation reflected by the crop from 400 nm to 1000 nm. The results showed that crop reflectance increased with increasing water deficit, and the detected reflectance increase was significant during the first day of irrigation was withheld. Based on the reflectance measurements, several crop indices were calculated and correlated with substrate volumetric water content and tomato leaf chlorophyll content. The results showed that when the modified red simple ratio tndex (mrSRI) and the modified red normalized vegetation index (mrNDVI) values increased by more than 2.5% and 23% respectively, the substrate volumetric water content decreased by more than 3%. In addition, when the Transformed Chlorophyll Absorption Reflectance Index (TCARI) value increased by about 16%, the leaf chlorophyll content decreased by about 3%. These results of the present study are promising for the development of a non-contact method for estimating plant water status in tomato crops grown under controlled environment.

      PubDate: 2017-12-13T06:38:28Z
      DOI: 10.1016/j.biosystemseng.2017.11.002
       
  • Evaluation and analysis of deep percolation losses of drip irrigated
           citrus crops under non-saline and saline conditions in a semi-arid area
    • Authors: Houda Nassah; Salah Er-Raki; Said Khabba; Younes Fakir; Fatima Raibi; Olivier Merlin; Bernard Mougenot
      Abstract: Publication date: Available online 17 November 2017
      Source:Biosystems Engineering
      Author(s): Houda Nassah, Salah Er-Raki, Said Khabba, Younes Fakir, Fatima Raibi, Olivier Merlin, Bernard Mougenot
      In arid and semi-arid regions, irrigation management is important to avoid water loss by soil evaporation and deep percolation (DP). In this context, estimating the irrigation water demand has been investigated by many studies in the Haouz plain. However, DP losses beneath irrigated areas in the plain have not been quantified. To fill the gap, this study evaluated DP over two drip-irrigated citrus orchards (Agafay and Saada) using both water balance and direct fluxmeter measurement methods, and explored the simple FAO-56 approach to optimise irrigation in order to both avoid crop water stress and reduce DP losses in case of non-saline and saline soils. The experimental measurements determined different terms of the water balance by using an Eddy-Covariance system, fluxmeter, soil moisture sensors and a meteorological station. Using the water balance equation and fluxmeter measurements, results showed that about 37% and 45% of supplied water was lost by DP in Saada and Agafay sites, respectively. The main cause of DP losses was the mismatch between irrigation and the real crop water requirement. For Agafay site, it was found that increased over-irrigation had the effect of reducing soil salinity by leaching salts. The applied FAO-56 model suggested an optimal irrigation scheduling by taking into account both rainfall and soil salinity. The recommended irrigations could save about 39% of supplied water in non-saline soil at Saada and from 30% to 47% in saline soil at Agafay.

      PubDate: 2017-12-13T06:38:28Z
      DOI: 10.1016/j.biosystemseng.2017.10.017
       
  • A software tool for the automatic and real-time analysis of cow velocity
           data in free-stall barns: The case study of oestrus detection from
           Ultra-Wide-Band data
    • Authors: Claudia Arcidiacono; Simona M.C. Porto; Massimo Mancino; Giovanni Cascone
      Abstract: Publication date: Available online 16 November 2017
      Source:Biosystems Engineering
      Author(s): Claudia Arcidiacono, Simona M.C. Porto, Massimo Mancino, Giovanni Cascone
      The increase in the design and utilisation of real-time location systems has produced a huge amount of data to be handled in real time. As a consequence, challenges still exist in improving the analysis process of data streams by designing new tools. In this context, a software tool for automatic and real-time analysis of cow velocity data acquired by an ultra-wide band real-time location system (UWB RTLS) in a free-stall barn was designed and developed. A functionality implemented in this software determined the instant velocity of each cow over time, which was represented through an interactive graph (CowVelocityGraph). Feasibility of the software tools for the visualisation and analysis of UWB data was assessed. A use case of this software tool was carried out to verify its suitability to acquire useful information related to the occurrence of cow's oestrus, which is the case study of this research. The results showed that a pattern, related to the behaviour of the cow analysed, could be identified in CowVelocityGraph when the state of oestrus occurred, allowing for visualisation and analysis of UWB data. The software developed in this study provides the user with the ability to work in real time by acquiring the RTLS data updated at short time intervals, greatly exploiting the UWB RTLS potentialities. Further tests need to be repeated in different farming conditions, on a significant number of cows. On a broader perspective, this study addressed the lack of analysis tools for data streams acquired in livestock houses.

      PubDate: 2017-12-13T06:38:28Z
      DOI: 10.1016/j.biosystemseng.2017.10.007
       
  • Precision fish farming: A new framework to improve production in
           aquaculture
    • Authors: Martin Føre; Kevin Frank; Tomas Norton; Eirik Svendsen; Jo Arve Alfredsen; Tim Dempster; Harkaitz Eguiraun; Win Watson; Annette Stahl; Leif Magne Sunde; Christian Schellewald; Kristoffer R. Skøien; Morten O. Alver; Daniel Berckmans
      Abstract: Publication date: Available online 14 November 2017
      Source:Biosystems Engineering
      Author(s): Martin Føre, Kevin Frank, Tomas Norton, Eirik Svendsen, Jo Arve Alfredsen, Tim Dempster, Harkaitz Eguiraun, Win Watson, Annette Stahl, Leif Magne Sunde, Christian Schellewald, Kristoffer R. Skøien, Morten O. Alver, Daniel Berckmans
      Aquaculture production of finfish has seen rapid growth in production volume and economic yield over the last decades, and is today a key provider of seafood. As the scale of production increases, so does the likelihood that the industry will face emerging biological, economic and social challenges that may influence the ability to maintain ethically sound, productive and environmentally friendly production of fish. It is therefore important that the industry aspires to monitor and control the effects of these challenges to avoid also upscaling potential problems when upscaling production. We introduce the Precision Fish Farming (PFF) concept whose aim is to apply control-engineering principles to fish production, thereby improving the farmer's ability to monitor, control and document biological processes in fish farms. By adapting several core principles from Precision Livestock Farming (PLF), and accounting for the boundary conditions and possibilities that are particular to farming operations in the aquatic environment, PFF will contribute to moving commercial aquaculture from the traditional experience-based to a knowledge-based production regime. This can only be achieved through increased use of emerging technologies and automated systems. We have also reviewed existing technological solutions that could represent important components in future PFF applications. To illustrate the potential of such applications, we have defined four case studies aimed at solving specific challenges related to biomass monitoring, control of feed delivery, parasite monitoring and management of crowding operations.

      PubDate: 2017-11-15T19:22:21Z
      DOI: 10.1016/j.biosystemseng.2017.10.014
       
  • Evaluation of wind pressure acting on multi-span greenhouses using CFD
           technique, Part 1: Development of the CFD model
    • Authors: Rack-woo Kim; In-bok Lee; Kyeong-seok Kwon
      Abstract: Publication date: Available online 10 November 2017
      Source:Biosystems Engineering
      Author(s): Rack-woo Kim, In-bok Lee, Kyeong-seok Kwon
      The CFD-computed and wind tunnel (WT)-measured wind pressure coefficients (C p) were compared for development of CFD model. First, the y + values were considered to identify the optimum conditions of the first cell height from the adjacent wall. The CFD-computed C p values closely corresponded to the measured C p values when the first cell height was 1.5 × 10−4 m. The computational domain test and the grid independence test were also conducted to determine the optimum domain size and mesh size. As a result of the computational domain test, the length of the upstream portion was fixed at 3H (H = ridge height), and the length of the downstream, side and upper portions were determined to be 15H, 5H and 5H, respectively. The mesh size was designed to be 1.0 × 10−2 m based on the grid independence test. Using the given design criteria, an appropriate turbulence model was selected, and the Shear stress transport (SST) k–ω model was eventually chosen as the turbulence model. Finally, the computed and measured C p values were compared using statistical indices, demonstrating that the CFD-designed model could accurately compute the C p values.

      PubDate: 2017-11-15T19:22:21Z
      DOI: 10.1016/j.biosystemseng.2017.09.008
       
  • Multifractal and joint multifractal analysis of general soil properties
           and altitude along a transect
    • Authors: Glécio M. Siqueira; Ênio F.F. Silva; Eva Vidal-Vázquez; Antonio Paz-González
      Abstract: Publication date: Available online 2 November 2017
      Source:Biosystems Engineering
      Author(s): Glécio M. Siqueira, Ênio F.F. Silva, Eva Vidal-Vázquez, Antonio Paz-González
      Multifractal characterisation of soil spatial variability has the potential for providing a better understanding of the distribution patterns of data values, and may contribute to improved resource management. We examined the scaling heterogeneity and multiple scale relationships of soil general properties and topography using multifractal and joint multifractal techniques. Soil samples were collected down to 0.20 m depth and altitude was recorded at equal intervals of 3 m along a 396 m transect in an Orthic Podzol at Pernambuco, Brazil. Soil properties studied were: textural fractions, pH, organic carbon (OC), exchangeable cations, exchangeable acidity (H + Al), sum of bases (SB), cation exchange capacity (CEC) and percent base saturation (V). The spatial distribution of altitude and soil general properties, characterised through generalised dimension, Dq, and singularity spectra, f(α)–α, showed a well-defined multifractal structure. Notwithstanding, these variables displayed several degrees of scaling heterogeneity, which was lowest for pH, sand and clay contents and highest for exchangeable cations and silt content. Joint multifractals showed that correlations between pairs of variables may or may not be stronger at the observation scale than across a range of spatial scales. Hence, soil OC and pH showed higher relationships to CEC, (H + Al), SB and V at the observation than at multiple spatial scales, while local topography effects on pH and CEC were greater at multiple scales. Multifractal and joint multifractal analysis provided new insights to characterise the spatial patterns and the relationships between soil properties at multiple scales, and to evaluate the effect of topography on soil heterogeneity.

      PubDate: 2017-11-08T15:21:06Z
      DOI: 10.1016/j.biosystemseng.2017.08.024
       
  • Evaluation of wind pressure acting on multi-span greenhouses using CFD
           technique, part 2: Application of the CFD model
    • Authors: Rack-woo Kim; Se-woon Hong; In-bok Lee; Kyeong-seok Kwon
      Abstract: Publication date: Available online 23 October 2017
      Source:Biosystems Engineering
      Author(s): Rack-woo Kim, Se-woon Hong, In-bok Lee, Kyeong-seok Kwon
      Revision of greenhouse design standards is required to ensure the structural safety of greenhouses in strong wind environments. The wind pressure coefficients (Cp) of various greenhouses must be evaluated to revise the newly modified greenhouse design standards. In this study, the Cp values of multi-span greenhouses that are typical in South Korea, e.g., wide-span, Venlo, and 1-2W type greenhouses, were estimated using the developed computational fluid dynamics (CFD) model from the first paper. In particular, the CFD-computed Cp values were analysed with consideration of the wind directions, number of spans, and greenhouse design factors, such as the roof slope and roof curvature radius. Based on the analysed results, the CFD-computed Cp values of multi-span greenhouses were suggested for use in structural and cladding designs based on the defined sections of the greenhouse surface, various wind directions, number of spans and design factors. The Cp values for structural design were analysed for wind blowing in the direction perpendicular to the ridge (0°) and in the direction of the ridge (90°). In addition, the maximum Cp values were examined for cladding design with consideration of every wind direction.

      PubDate: 2017-11-02T15:44:29Z
      DOI: 10.1016/j.biosystemseng.2017.09.011
       
 
 
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