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  Subjects -> AGRICULTURE (Total: 821 journals)
    - AGRICULTURAL ECONOMICS (74 journals)
    - AGRICULTURE (576 journals)
    - CROP PRODUCTION AND SOIL (95 journals)
    - POULTRY AND LIVESTOCK (48 journals)

AGRICULTURE (576 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: 8)
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: 11)
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: 19)
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: 4)
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: 19)
Agricultural Commodities     Full-text available via subscription  
Agricultural Economics     Hybrid Journal   (Followers: 45)
Agricultural History     Full-text available via subscription   (Followers: 180)
Agricultural History Review     Full-text available via subscription   (Followers: 10)
Agricultural Research     Hybrid Journal   (Followers: 4)
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: 43)
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   (Followers: 1)
Agriculture and Biology Journal of North America     Open Access  
Agriculture and Food Sciences Research     Open Access   (Followers: 7)
Agriculture and Human Values     Hybrid Journal   (Followers: 14)
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   (Followers: 1)
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: 2)
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: 30)
American Journal of Potato Research     Hybrid Journal   (Followers: 3)
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: 5)
Asian Journal of Medical and Biological Research     Open Access   (Followers: 3)
Asian Journal of Plant Sciences     Open Access   (Followers: 2)
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: 4)
Bangladesh Journal of Scientific Research     Open Access   (Followers: 1)
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: 29)
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: 61)
Canadian Water Resources Journal     Hybrid Journal   (Followers: 22)
Capitalism Nature Socialism     Hybrid Journal   (Followers: 16)
Ceiba     Open Access  
Central European Forestry Journal     Open Access  
Cereal Chemistry     Full-text available via subscription   (Followers: 5)
CERNE     Open Access  
CESifo Economic Studies     Hybrid Journal   (Followers: 17)
Change and Adaptation in Socio-Ecological Systems     Open Access   (Followers: 2)
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: 5)
Contributions to Tobacco Research     Open Access   (Followers: 3)
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: 34)
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: 15)
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: 3)
Die Bodenkultur : Journal of Land Management, Food and Environment     Open Access  
Dossiers Agraris     Open Access  
Ecological Applications     Full-text available via subscription   (Followers: 167)
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: 39)
Economic Record     Hybrid Journal   (Followers: 7)
Emirates Journal of Food and Agriculture     Open Access   (Followers: 1)
Empirical Economics     Hybrid Journal   (Followers: 14)
Encuentro     Open Access  
Engineering in Agriculture, Environment and Food     Hybrid Journal  
Ensaios e Ciência: Ciências Biológicas, Agrárias e da Saúde     Open Access  
Environment and Development Economics     Hybrid Journal   (Followers: 32)
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: 65)
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)

        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  [3177 journals]
  • Reducing field work time using fleet routing optimization
    • Authors: Hasan Seyyedhasani; Joseph S. Dvorak
      Pages: 1 - 10
      Abstract: Publication date: May 2018
      Source:Biosystems Engineering, Volume 169
      Author(s): Hasan Seyyedhasani, Joseph S. Dvorak
      Agricultural producers seek to complete their field work operations as quickly as possible. This is achievable through the simultaneous use of multiple vehicles for an operation. However, path allocation and scheduling then must be considered. Transforming the field work problem into a Vehicle Routing Problem (VRP) and using optimization procedures designed for this problem provides a method of allocating paths. In this work, the accuracy of a VRP representation of field work is confirmed and the ability of this optimization system to reduce field work times is verified. Experiments were conducted using three tractors during a rotary mowing operation. First, the traditional routes used by human drivers were recorded. Then, a VRP representation of this operation was created, and new routes generated by a Tabu Search optimization procedure. Finally, the field operation was repeated using the optimized routes. Using these routes, the time to complete the field work was reduced by 17.3% and the total operating time for all tractors was reduced by 11.5%. The predictions by the VRP representation for completion time and total time were both within 2% of the actual times recorded when the tractors followed the computer-generated routes in the field. These reductions illustrated the ability of the route optimization procedure to improve effective field efficiency.

      PubDate: 2018-02-25T22:05:58Z
      DOI: 10.1016/j.biosystemseng.2018.01.006
      Issue No: Vol. 169 (2018)
  • Heat and mass transfer properties of longan shrinking from a spherical to
           an irregular shape during drying
    • Authors: Krit Apinyavisit; Adisak Nathakaranakule; Gauri S. Mittal; Somchart Soponronnarit
      Pages: 11 - 21
      Abstract: Publication date: May 2018
      Source:Biosystems Engineering, Volume 169
      Author(s): Krit Apinyavisit, Adisak Nathakaranakule, Gauri S. Mittal, Somchart Soponronnarit
      The heat and mass transfer longan shrinking from a regular spherical shape to an irregular shape during combined microwave with hot air drying (MHD) was investigated. The heat and mass transfer coefficients equations for a spherical shape were modified using modelling and experimental data. A moisture diffusivity equation as a function of temperature and moisture content was also developed. A microwave based heat absorption equation was developed without using conventional microwave properties. The root mean square of errors (RMSE) between predicted and experimental data were 0.070–0.093 d.b. and 1.41–4.81 °C for average moisture content and longan temperature, respectively. Sensitivity analysis of the estimated parameters for the nonlinear equations indicated that global optimum values were determined.

      PubDate: 2018-02-25T22:05:58Z
      DOI: 10.1016/j.biosystemseng.2018.01.007
      Issue No: Vol. 169 (2018)
  • Filtering methods to improve the accuracy of indoor positioning data for
           dairy cows
    • Authors: Matti Pastell; Lilli Frondelius; Mikko Järvinen; Juha Backman
      Pages: 22 - 31
      Abstract: Publication date: May 2018
      Source:Biosystems Engineering, Volume 169
      Author(s): Matti Pastell, Lilli Frondelius, Mikko Järvinen, Juha Backman
      Several indoor positioning systems for livestock buildings have been developed to be used as tools in automated animal welfare monitoring. In many environments the measurements from positioning systems still contain unwanted noise and the quality of the measurement data can be enhanced using filters. The aim of this study was to develop an efficient filter for positioning data measured from dairy cows with UWB-based indoor positioning system in a free stall barn. A heuristic jump filter combined with median filter and extended Kalman filter was developed and tested. The performance of the filters were compared against reference data collected from Insentec roughage intake feeders and scan sampling of animal presence in a specific lying stall with over 1500 reference observations from both methods. The quality of the positioning data was significantly improved using filtering. The 9th order median filter provided best estimates for cow position when the cows were not moving with median 100% of measurements located in correct stall and 84% in correct feeding trough when compared to the reference observations and measurements. The extended Kalman filter also improved the positioning accuracy significantly when compared to raw data and provides better of estimates of the trajectory of moving cows.

      PubDate: 2018-02-25T22:05:58Z
      DOI: 10.1016/j.biosystemseng.2018.01.008
      Issue No: Vol. 169 (2018)
  • In-situ open path FTIR measurements of the vertical profile of spray drift
           from air-assisted sprayers
    • Authors: Oz Kira; Yael Dubowski; Raphael Linker
      Pages: 32 - 41
      Abstract: Publication date: May 2018
      Source:Biosystems Engineering, Volume 169
      Author(s): Oz Kira, Yael Dubowski, Raphael Linker
      Estimating pesticide spray drift, which is a part of total drift loss, is complex as airborne pesticide concentrations are low and depend on multiple factors. The aim was to measure and compare vertical profiles of spray drift generated by different sprayers using Open Path Fourier-Transform-Infra-Red (OP-FTIR) spectrometer. Field tests included three types of commercial agricultural sprayers. The OP-FTIR was placed at the edge of an apple orchard with the line of sight parallel to tree rows. The OP-FTIR and its reflector were mounted on platform lifts to allow measurements at 4 heights: 3 (canopy height), 4, 5, and 6 m above ground. The sprayers sprayed water within the three tree rows closest to the OP-FTIR as well as outside each tree row in order to estimate the spray drift as function of distance with and without tree interference. The results of the experiments showed that, under the meteorological conditions prevailing, there were substantial differences between the sprayers in terms of spray drift of droplets with diameter > 5 μm. Additionally, the results showed that spray drift can be reduced substantially (by up to 50%) by using a tree-line barrier or a buffer zone.

      PubDate: 2018-02-25T22:05:58Z
      DOI: 10.1016/j.biosystemseng.2018.01.010
      Issue No: Vol. 169 (2018)
  • Test methods for characterising the water distribution from irrigation
           sprinklers: Design, evaluation and uncertainty analysis of an automated
    • Authors: Ezequiel Saretta; Antonio P. de Camargo; Tarlei A. Botrel; José A. Frizzone; Richard Koech; Bruno Molle
      Pages: 42 - 56
      Abstract: Publication date: May 2018
      Source:Biosystems Engineering, Volume 169
      Author(s): Ezequiel Saretta, Antonio P. de Camargo, Tarlei A. Botrel, José A. Frizzone, Richard Koech, Bruno Molle
      An automated system for indoor testing of irrigation sprinklers was developed and evaluated. The system was designed to test single sprinklers with jet lengths up to 18 m. The tests involve the use of 36 collectors (catch-cans) spaced at 0.5 m intervals along the jet radius. A single pressure transducer coupled to a manifold equipped with solenoid valves was employed to sequentially scan the water level in each collector. Radial application rates were determined based on water level measurements. Results obtained using the automated system were compared with those obtained using manual operation using mass measurements. Uncertainty analysis of the manual method was compared with the automated system. The automated system was found to be as reliable as the manually operated system for testing sprinklers. Although minor differences in the application rates measured by the two methods were detected, they did not cause appreciable differences in the distribution uniformity indicators used. The results presented will provide useful baseline for uncertainty analysis in irrigation sprinkler testing.

      PubDate: 2018-02-25T22:05:58Z
      DOI: 10.1016/j.biosystemseng.2018.01.011
      Issue No: Vol. 169 (2018)
  • Modelling the influence of crop density and weather conditions on field
           drying characteristics of switchgrass and maize stover using random forest
    • Authors: Amit Khanchi; Stuart Birrell; Robert B. Mitchell
      Pages: 71 - 84
      Abstract: Publication date: May 2018
      Source:Biosystems Engineering, Volume 169
      Author(s): Amit Khanchi, Stuart Birrell, Robert B. Mitchell
      Field drying trials were conducted using both field baskets as well as grab sampling techniques to study drying behaviour of switchgrass and maize (corn) stover (CS). Environmental conditions such as hourly solar radiation, vapour pressure deficit (VPD), average wind speed, rainfall amount, harvesting method, and field operations such as swath density were used as variables for model development. A powerful classification-based algorithm, which uses a collection of decision trees called random forest (RF) was utilised to predict moisture content (MC) of switchgrass and CS on wet basis. RF predicted the MC of switchgrass and CS with a coefficient of determination of 0.77 and 0.79, respectively. Rainfall, hours after harvest, average change in solar radiation in past 12 h, average solar radiation in past 12 h, and swath density were found to be the important variables affecting the MC of CS. Drying CS in low density (LD) and medium density (MD) swaths facilitated quick drying even in moderate drying conditions. Rainfall events ranging from 1.5 to 7.5 mm were experienced during the switchgrass drying period which delayed crop drying by one day to several days depending on the weather conditions after rainfall. Several rewetting events were also observed due to dew at night which increased the MC in LD switchgrass and CS by 5–15%. The models developed in the current study will help in decision-making of switchgrass and CS collection after harvest, based on forecast weather conditions in lower Midwestern states.

      PubDate: 2018-02-25T22:05:58Z
      DOI: 10.1016/j.biosystemseng.2018.02.002
      Issue No: Vol. 169 (2018)
  • Orchard manoeuvring strategy for a robotic bin-handling machine
    • Authors: Yunxiang Ye; Long He; Zhaodong Wang; Dylan Jones; Geoffrey A. Hollinger; Matthew E. Taylor; Qin Zhang
      Pages: 85 - 103
      Abstract: Publication date: May 2018
      Source:Biosystems Engineering, Volume 169
      Author(s): Yunxiang Ye, Long He, Zhaodong Wang, Dylan Jones, Geoffrey A. Hollinger, Matthew E. Taylor, Qin Zhang
      Unlike a car-like vehicle manoeuvring its way in an open field, a four-wheel-independent-steered robotic machine placed in an orchard must operate in a very confined working space between tree rows. Because the machine is subject to the unique constraints of the worksite space and operation limits, multiple steering modes are often required to effectively accomplish the desired bin-handling manoeuvers. In this study, we created a multi-mode manoeuvring strategy selection method to generate strategies that can guide the robotic platform to accomplish bin handling tasks, such as correcting pose error between tree rows, entering a tree lane from the headland, and loading a bin between tree rows, effectively. The method determines the manoeuvring strategies based on the situation among four steering modes: 1) Ackermann steering, 2) coordinated four wheel steering, 3) crab steering, and 4) spinning. The study first evaluated applicable strategies and selected the best of these strategies for different bin handling scenarios. Then, the selected strategies were implemented to drive a four-wheel-independent-steering (4WIS) system to complete the tasks in a commercial orchard in order to validate the method. Obtained results showed that the system could navigate the platform on desired trajectories to complete bin-handling tasks with a root mean square errors less than 0.06 m.

      PubDate: 2018-02-25T22:05:58Z
      DOI: 10.1016/j.biosystemseng.2017.12.005
      Issue No: Vol. 169 (2018)
  • Use of full-scale hybrid horizontal tubular photobioreactors to process
           agricultural runoff
    • Authors: María Jesús García-Galán; Raquel Gutiérrez; Enrica Uggetti; Víctor Matamoros; Joan García; Ivet Ferrer
      Pages: 138 - 149
      Abstract: Publication date: February 2018
      Source:Biosystems Engineering, Volume 166
      Author(s): María Jesús García-Galán, Raquel Gutiérrez, Enrica Uggetti, Víctor Matamoros, Joan García, Ivet Ferrer
      Diffuse pollution in rural areas due to agricultural runoff is a widespread and difficult problem to address due to the large areas affected. Drainage channels receive polluted water, but its introduction into conventional treatment network is often unfeasible. Within this context, microalgae-based treatment systems could be used as alternative treatment plants. A new design of semi-closed (hybrid) tubular horizontal photobioreactor (HTH-PBR) with low energy requirements has been evaluated for microalgae cultivation at full-scale (8.5 m3), using agricultural runoff as feedstock. This novel system was tested in batch and continuous mode over 4 and 135 d. Considering a full-scale application in an agricultural context, a batch test was carried out to evaluate the performance of the system. An increase of 22% in the biomass concentration in 4 d was registered, and all nutrients were consumed during the first two days. In the continuous experiment carried out over winter (December–April), productivity was between 2 and 14 g g [TSS] m−3 d, but values up to 76.4 g [TSS] m−3 d were reached at the end of the study in spring, despite the low nutrients concentration in the feedstock. Elimination of emerging contaminants was also evaluated, obtaining the highest removals for the synthetic musk fragrances tonalide and galaxolide (73% and 68%), and the anti-inflammatory drug diclofenac (61%).
      Graphical abstract image

      PubDate: 2018-02-05T00:24:27Z
      DOI: 10.1016/j.biosystemseng.2017.11.016
      Issue No: Vol. 166 (2018)
  • Numerical simulation of spray drift and deposition from a crop spraying
           aircraft using a CFD approach
    • Authors: Bin Zhang; Qing Tang; Li-ping Chen; Rui-rui Zhang; Min Xu
      Pages: 184 - 199
      Abstract: Publication date: February 2018
      Source:Biosystems Engineering, Volume 166
      Author(s): Bin Zhang, Qing Tang, Li-ping Chen, Rui-rui Zhang, Min Xu
      Spray drift from aerial application is a great concern and there have been many efforts to predict it. Here computational fluid dynamics (CFD) techniques are used to predict the velocity field and the subsequent trajectories of spray droplets in the wake of a Thrush 510G aircraft. The fluid phase is modelled using the Reynolds-averaged Navier–Stokes (RANS) equations within an Eulerian frame, whilst the dispersed phase is modelled using a stochastic tracking model in the Lagrangian frame. The wake of aircraft is represented with a pair of wing-tip vortices and droplets are released in ten locations representing atomisers mounted below the wing. Firstly, a case without the presence of crosswind was simulated and deposition analysis was performed. A case with the presence of crosswind was then simulated to predict spray drift and deposition. The locations droplets at different times, as well as evaporation rate, were compared with AGDISP (agricultural dispersal model) predictions. The physics of vortices evolution, droplets motion and evaporation were analysed to explain the drift and deposition characteristics.

      PubDate: 2018-02-05T00:24:27Z
      DOI: 10.1016/j.biosystemseng.2017.11.017
      Issue No: Vol. 166 (2018)
  • Hybrid Fickian–Darcian flow model for high pressure impregnation of
           fluids into porous biomaterials
    • Authors: Hamed Vatankhah; Abdolhamid Akbarzadeh; Hosahalli S. Ramaswamy
      Pages: 200 - 209
      Abstract: Publication date: February 2018
      Source:Biosystems Engineering, Volume 166
      Author(s): Hamed Vatankhah, Abdolhamid Akbarzadeh, Hosahalli S. Ramaswamy
      Pressure-driven fluid flow is an inevitable consequence which occurs during the high pressure processing of porous biological materials confined in a fluid phase. A hybrid numerical model was adopted to simulate the mass flow of fluids into an enclosed biological porous matrix under constant high hydrostatic pressure treatments. The numerical model was based on the finite element simulation of time-dependent Fickian mass transfer represented as saturation rate of fluid flow in the unsaturated media. The Kozeny–Carman model was used for correction of relative permeability because of the pressure-induced textural changes. As a case study, the proposed methodology was applied to simulate the high pressure impregnation of apple cubes by ascorbic acid solution (1% by mass). The proposed model demonstrated the existence of a low-pressure zone in the geometrical centre of the computational domain associated with the operating pressure level which provides a sufficient driving force for liquid migration. The numerical results were corroborated through implementing gravimetric and image processing experiments. Finally, a linear flow front rate of 0.03 cm s−1 was estimated along the porous matrix, and the analytical solutions to the Darcian model were used to determine the lumped permeability as a function of pressure difference at the impregnation flow front.

      PubDate: 2018-02-05T00:24:27Z
      DOI: 10.1016/j.biosystemseng.2017.12.002
      Issue No: Vol. 166 (2018)
  • Dormant stem water potential responds to laboratory manipulation of
           hydration as well as contrasting rainfall field conditions in deciduous
           tree crops
    • Authors: Luke K. Milliron; Andres Olivos; Sebastian Saa; Blake L. Sanden; Ken A. Shackel
      Pages: 2 - 9
      Abstract: Publication date: January 2018
      Source:Biosystems Engineering, Volume 165
      Author(s): Luke K. Milliron, Andres Olivos, Sebastian Saa, Blake L. Sanden, Ken A. Shackel
      Pressure chamber measurement of midday stem water potential (SWP) during the growing season has become a practical and widely adopted tool for irrigation management in many woody perennial and some annual crops, but this technique has not been applied to perennial crops during dormancy. The reliability of SWP measurements in dormant trees has in fact been questioned based on concerns that these tissues typically have a low percent of living tissue and/or a high level of embolism. Accurate psychrometer measurements of water potential do not depend on either of these properties, and hence should be useful in evaluating the accuracy of pressure chamber measured SWP in dormant trees. Pressure chamber and in-situ stem psychrometer methods were compared on dormant branches exposed to different levels of hydration in the laboratory. A very highly significant (Pr < 0.0001) linear regression was found between the two methods over a wide range of SWP (0 to about −2 MPa) in almond, cherry, and walnut, with r-square values ranging from 0.90 to 0.98. For almond and cherry, the slope of the regression was close to unity. Field measurements showed systematically lower SWP during a dry winter compared to a wet winter, and SWP was found to increase in response to a winter irrigation. This evidence strongly supports the validity of pressure chamber measured SWP as a reliable indicator of dormant tree water status, and hence its use as a tool to evaluate the need for winter irrigation in dormant tree crops.

      PubDate: 2018-02-05T00:24:27Z
      DOI: 10.1016/j.biosystemseng.2017.09.001
      Issue No: Vol. 165 (2018)
  • Leaf water content estimation by functional linear regression of field
           spectroscopy data
    • Authors: José R. Rodríguez-Pérez; Celestino Ordóñez; Ana B. González-Fernández; Enoc Sanz-Ablanedo; José B. Valenciano; Victoriano Marcelo
      Pages: 36 - 46
      Abstract: Publication date: January 2018
      Source:Biosystems Engineering, Volume 165
      Author(s): José R. Rodríguez-Pérez, Celestino Ordóñez, Ana B. González-Fernández, Enoc Sanz-Ablanedo, José B. Valenciano, Victoriano Marcelo
      Grapevine water status is critical as it affects fruit quality and yield. We assessed the potential of field hyperspectral data in estimating leaf water content (C w) (expressed as equivalent water thickness) in four commercial vineyards of Vitis vinifera L. reflecting four grape varieties (Mencía, Cabernet Sauvignon, Merlot and Tempranillo). Two regression models were evaluated and compared: ordinary least squares regression (OLSR) and functional linear regression (FLR). OLSR was used to fit C w and vegetation indices, whereas FLR considered reflectance in four spectral ranges centred at the 960, 1190, 1465 and 2035 nm wavelengths. The best parameters for the FLR model were determined using cross-validation. Both models were compared using the coefficient of determination (R 2) and percentage root mean squared error (%RMSE). FLR using continuous stretches of the spectrum as input produced more suitable C w models than the vegetation indices, considering both the fit and degree of adjustment and the interpretation of the model. The best model was obtained using FLR in the range centred at 1465 nm (R 2 = 0.70 and %RMSE = 8.485). The results depended on grape variety but also suggested that leaf C w can be predicted on the basis of spectral signature.

      PubDate: 2018-02-05T00:24:27Z
      DOI: 10.1016/j.biosystemseng.2017.08.017
      Issue No: Vol. 165 (2018)
  • A practical method using a network of fixed infrared sensors for
           estimating crop canopy conductance and evaporation rate
    • Authors: Hamlyn G. Jones; Paul A. Hutchinson; Tracey May; Hizbullah Jamali; David M. Deery
      Pages: 59 - 69
      Abstract: Publication date: January 2018
      Source:Biosystems Engineering, Volume 165
      Author(s): Hamlyn G. Jones, Paul A. Hutchinson, Tracey May, Hizbullah Jamali, David M. Deery
      We describe the development and testing of a novel thermal infrared sensor incorporating a dry reference surface for incorporation into field wireless sensor networks (WSNs) that allows the estimation of absolute transpiration rates and canopy conductance. This ‘dry reference’ sensor provides a physical reference surface that mimics the temperature of a non-transpiring canopy and can therefore be used in conjunction with canopy temperature to estimate either canopy transpiration or canopy conductance. The dry reference sensor is based on a hemispherical surface that mimics the distribution of shaded and sunlit leaves in non-transpiring canopy. Three dry reference sensors were deployed in a commercial cotton crop from which canopy transpiration and conductance was estimated for the entire season. We provide evidence that fixed infrared sensors with a dry reference surface, when combined with limited meteorological data, can provide useful continuous monitoring of crop water use and canopy conductance that is potentially of value for irrigation management and crop phenotyping applications. Key to the success of this dry sensor application is the requirement that the spectral absorptance of the sensor is tailored to match the crop of interest.

      PubDate: 2018-02-05T00:24:27Z
      DOI: 10.1016/j.biosystemseng.2017.09.012
      Issue No: Vol. 165 (2018)
  • Linking thermal imaging and soil remote sensing to enhance irrigation
           management of sugar beet
    • Authors: L. Quebrajo; M. Perez-Ruiz; L. Pérez-Urrestarazu; G. Martínez; G. Egea
      Pages: 77 - 87
      Abstract: Publication date: January 2018
      Source:Biosystems Engineering, Volume 165
      Author(s): L. Quebrajo, M. Perez-Ruiz, L. Pérez-Urrestarazu, G. Martínez, G. Egea
      The use of reliable information and data that are rapidly and easily acquired is essential for farm water management and appropriate irrigation strategies. Over the past decade, new advances have been made in irrigation technology, such as platforms that continuously transmit data between irrigation controllers and field sensors, mobile apps, and equipment for variable rate irrigation. In this study, images captured with a thermal imaging camera mounted on an unmanned aerial vehicle (UAV) were used to evaluate the water status of sugar beet plants in a plot with large spatial variability in terms of soil properties. The results were compared with those of soil moisture measurements. No direct relationship was observed between the water status of the soil and that of the crops. However, the fresh root mass and sugar content tended to decrease when higher levels of water stress were detected in the crop using thermal imaging, with coefficients of determination of 0.28 and 0.94 for fresh root mass and sugar content, respectively. Differences were observed between different soil types, and therefore different irrigation strategies are needed in highly heterogeneous plots.

      PubDate: 2018-02-05T00:24:27Z
      DOI: 10.1016/j.biosystemseng.2017.08.013
      Issue No: Vol. 165 (2018)
  • 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: 2018-02-25T22:05:58Z
      DOI: 10.1016/j.biosystemseng.2017.09.013
      Issue No: Vol. 164 (2018)
  • The role of bond and damping in the discrete element model of soil-sweep
    • Abstract: Publication date: May 2018
      Source:Biosystems Engineering, Volume 169
      Author(s): Kornél Tamás
      This paper introduces a discrete element method (DEM) model for studying soil-sweep interaction, where the investigated microscopic parameters were the viscous damping which was utilised for energy dissipation and parallel bonds for simulating the effect of moisture in the particle assembly. The aims were to improve the DEM model with the appropriate set of viscous damping to simulate the effect of the tool's working speed on draught and investigating the set of bond radius multiplier of the parallel bonds for modelling the soil's moisture content (mc) with the spherical particles used. For verification of the DEM model's input parameters, two laboratory soil bin studies were made. The first test confirmed that the draught forces were comparable with the viscous damping used, where the particle assemblies were overdamped at low speeds (0.5–2.8 m s−1) and underdamped at higher working speeds (2.8–4.1 m s−1). The second soil bin test proved that the appropriate set of parallel bonds enable the simulation of the soil volumetric moisture content over the range 2, 15, 30, 35%. The draught was lower in the simulation using radius multiplier of 0.0, 0.2 in the same manner as 2, 15% mc in the laboratory test. The draught was highest in the model at radius multiplier of 0.5, similarly to the 30% mc in the soil bin study. When the bond radius multiplier was set to 0.8, the DEM model able to simulate lower draught similarly to soil with 35% mc.

      PubDate: 2018-02-25T22:05:58Z
  • A pilot study to detect coccidiosis in poultry farms at early stage from
           air analysis
    • Authors: Guido Grilli; Federica Borgonovo; Emanuela Tullo; Ilaria Fontana; Marcella Guarino; Valentina Ferrante
      Abstract: Publication date: Available online 21 February 2018
      Source:Biosystems Engineering
      Author(s): Guido Grilli, Federica Borgonovo, Emanuela Tullo, Ilaria Fontana, Marcella Guarino, Valentina Ferrante
      Nowadays, the preventive use of antibiotics in intensive farming system is common and this management practice lead to the spreading of drugs in the environment, contributing to the phenomena of antibiotic resistance. For this reason, different professional figures work on the development of drug reduction strategies. Due to the high priority of this issue, early detection of any health problem is of great importance in intensive farming. Precision Livestock Farming (PLF), through the combination of cheap technologies and specific algorithms, can provide valuable and rapid information for farmers starting from the huge amount of data that can be collected in real time at farm level. A prototype, able to give information about air fingerprint, was developed and tested in an experimental poultry farm in order to observe if air quality data were related to the presence of coccidiosis. Air samples were collected once a week in Nalophan® bags and transported to the laboratory for instrumental analysis. The prototype was able to discriminate between infected and not infected pens at a very early stage, when only 250 oocysts g−1 [faeces] (opg) were present in one pen. These results were also confirmed by analysing air samples in a commercial poultry farm, since all samples were correctly classified by the prototype in infected or not infected pen. This pilot study has shown that this technology could be installed in farms to continuously monitor health status of broilers, supporting farmers in the sustainable management of their activities.

      PubDate: 2018-02-25T22:05:58Z
      DOI: 10.1016/j.biosystemseng.2018.02.004
  • Soil texture effects on multifractal behaviour of nitrogen adsorption and
           desorption isotherms
    • Authors: Jorge Paz-Ferreiro; Mara de A. Marinho; Cleide A. de Abreu; Eva Vidal-Vázquez
      Abstract: Publication date: Available online 9 February 2018
      Source:Biosystems Engineering
      Author(s): Jorge Paz-Ferreiro, Mara de A. Marinho, Cleide A. de Abreu, Eva Vidal-Vázquez
      Nitrogen adsorption (NAI) and desorption (NDI) isotherms have been reasonably well described by multifractal analysis. This study aimed to assess effects of soil texture on the scaling heterogeneity of NAIs and NDIs. Contrasting medium textured and clayey soils, developed over parent materials with felsic or mafic compositions respectively, were sampled. These two soil groups also showed significant differences in specific surface area (SSA) and cation exchange capacity (CEC), but not in organic matter content (OMC). The scaling properties of all NAIs and NDIs studied exhibited a well-defined multifractal structure, which was assessed by generalised dimension, Dq, and singularity spectra, f (α), versus α functions. Width of Dq given by (D−5 −D 5 ) and therefore scaling heterogeneity was significantly higher for NAIs than for NDIs. Also, the former was less evenly distributed than the latter. There was significant interaction between isotherm branch (NAI versus NDI) and texture (medium to heavy) for several indices obtained from Dq, namely D−5, D1 D 2 , D 5 and (D−5 −D 5 ), so that the values of these indices were greater for clayey soils during adsorption and for medium textured soils during desorption. Therefore, NAIs of clayey soils exhibited higher scaling heterogeneity and were more evenly distributed than those of medium textured soils and the reverse was true for their respective NDIs counterparts. Differences in multifractality of NAIs and NDIs were consistent with a wider hysteresis loop of the medium textured soils compared to that of the clayey soils; this is because features of this loop influenced the proportion of high and low values in the probability distribution of the isotherms. Multifractal analysis of N2 isotherms was useful to characterise soil types with contrasting textures, accounting for peculiar agronomical and environmental characteristics; therefore, it may help in assessing links between soil quality and inherent soil properties.

      PubDate: 2018-02-25T22:05:58Z
      DOI: 10.1016/j.biosystemseng.2018.01.009
  • Region detection of lesion area of knee based on colour edge detection and
           bilateral projection
    • Authors: Yangyang Guo; Dongjian He; Huaibo Song
      Abstract: Publication date: Available online 3 January 2018
      Source:Biosystems Engineering
      Author(s): Yangyang Guo, Dongjian He, Huaibo Song
      Wear of the knee is an important indicator of the health status of dairy cows. However, the complex cattle environment and the presence of mud, excrement, and other interferences make examination of the lesion area difficult. We utilised a region detection method based on colour edge detection and bilateral projection to detect the knee area of cows. First, edge information of colour images was obtained by colour edge extraction. Second, most of the background was removed and the leg region was obtained using an open operation, vertical projection, and convex hull processing. Finally, threshold processing and horizontal projection were applied to determine the centre of the target region and the overall target region. To verify the validity of the proposed algorithm, a K-means algorithm and salient region detection were performed. In total, 81 test samples were randomly selected from 300 images, and the results showed that the average overlap rate (OR) was 6.5% and 17.3% higher than that of the K-means and saliency methods, respectively. The false-positive rate (FPR) was 0.8% higher than that of the K-means method and 6.2% lower than that of the saliency method, and the false-negative rate (FNR) decreased by 6.5% and 17.3%, respectively. The present method showed good robustness when background obstructions or ground reflection was present in the images. The results of the present work imply that our method can effectively extract the target region and could stimulate further analysis of cow knees containing swelling and scars.

      PubDate: 2018-02-05T00:24:27Z
      DOI: 10.1016/j.biosystemseng.2017.12.006
  • 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
    • 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
    • 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)
  • 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
    • 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
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