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  Subjects -> AGRICULTURE (Total: 846 journals)
    - AGRICULTURAL ECONOMICS (75 journals)
    - AGRICULTURE (594 journals)
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AGRICULTURE (594 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: 4)
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: 11)
Advances in Agronomy     Full-text available via subscription   (Followers: 12)
Advances in Horticultural Science     Open Access  
Advances in Life Science and Technology     Open Access   (Followers: 16)
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: 46)
Agricultural History     Full-text available via subscription   (Followers: 171)
Agricultural History Review     Full-text available via subscription   (Followers: 11)
Agricultural Research     Hybrid Journal   (Followers: 4)
Agricultural Science     Open Access   (Followers: 2)
Agricultural Science     Full-text available via subscription   (Followers: 2)
Agricultural Sciences     Open Access   (Followers: 4)
Agricultural Systems     Hybrid Journal   (Followers: 31)
Agricultural Water Management     Hybrid Journal   (Followers: 43)
Agriculture     Open Access   (Followers: 7)
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  
Agrisost     Open Access  
Agritech     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: 10)
Agrosearch     Open Access   (Followers: 2)
Agrotekma : Jurnal Agroteknologi dan Ilmu Pertanian     Open Access  
Agrovigor     Open Access   (Followers: 1)
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: 8)
American Journal of Botany     Full-text available via subscription   (Followers: 14)
American Journal of Economics and Sociology     Hybrid Journal   (Followers: 30)
American Journal of Potato Research     Hybrid Journal   (Followers: 2)
American Journal of Rural Development     Open Access   (Followers: 5)
Anais da Academia Brasileira de Ciências     Open Access   (Followers: 2)
Annales des Sciences Agronomiques     Full-text available via subscription  
Annals of Agricultural Sciences     Open Access   (Followers: 2)
Annals of Silvicultural Research     Open Access   (Followers: 1)
Annals Valahia University of Targoviste - Agriculture     Open Access  
Annual Review of Resource Economics     Full-text available via subscription   (Followers: 9)
APCBEE Procedia     Partially Free   (Followers: 1)
Applied Financial Economics Letters     Hybrid Journal   (Followers: 8)
Aquacultura Indonesiana     Open Access  
Arboricultural Journal : The International Journal of Urban Forestry     Hybrid Journal   (Followers: 7)
Archivos de Zootecnia     Open Access   (Followers: 1)
ARO. The Scientific Journal of Koya University     Open Access  
Arquivos do Instituto Biológico     Open Access   (Followers: 1)
Arthropod-Plant Interactions     Hybrid Journal   (Followers: 2)
Asian Economic Papers     Hybrid Journal   (Followers: 7)
Asian Journal of Agricultural Research     Open Access   (Followers: 4)
Asian Journal of Agriculture     Open Access  
Asian Journal of Medical and Biological Research     Open Access   (Followers: 3)
Asian Journal of Plant Sciences     Open Access   (Followers: 1)
Australian Cottongrower, The     Full-text available via subscription   (Followers: 1)
Australian Economic Papers     Hybrid Journal   (Followers: 32)
Australian Economic Review     Hybrid Journal   (Followers: 3)
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: 2)
Australian Holstein Journal     Full-text available via subscription   (Followers: 1)
Australian Journal of Agricultural and Resource Economics     Hybrid Journal   (Followers: 2)
Australian Journal of Agricultural Engineering     Open Access   (Followers: 2)
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: 24)
Biodiversity : Research and Conservation     Open Access   (Followers: 26)
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 Ilmu Makanan Ternak     Open Access  
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: 20)
Capitalism Nature Socialism     Hybrid Journal   (Followers: 17)
Ceiba     Open Access  
Central European Forestry Journal     Open Access   (Followers: 1)
Cereal Chemistry     Full-text available via subscription   (Followers: 4)
CERNE     Open Access   (Followers: 1)
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: 11)
Computers and Electronics in Agriculture     Hybrid Journal   (Followers: 5)
Contributions to Tobacco Research     Open Access   (Followers: 3)
Corpoica Ciencia y Tecnología Agropecuaria     Open Access  
Corps et culture     Open Access   (Followers: 3)
Cuadernos de Desarrollo Rural     Open Access   (Followers: 1)
Cultivos Tropicales     Open Access   (Followers: 1)
Cultural Geographies     Hybrid Journal   (Followers: 17)
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: 18)
Culture, Agriculture, Food and Environment     Hybrid Journal   (Followers: 14)
Culture, Agriculture, Food and Environment     Hybrid Journal   (Followers: 6)
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: 4)
Developments in Agricultural Economics     Full-text available via subscription   (Followers: 3)
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: 154)
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: 1)
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: 9)
European Journal of American Culture     Hybrid Journal   (Followers: 1)
European Journal of Health Economics     Hybrid Journal   (Followers: 20)
European Journal of Law and Economics     Hybrid Journal   (Followers: 65)
European Review of Agricultural Economics     Hybrid Journal   (Followers: 10)
EvoDevo     Open Access   (Followers: 2)
Extensão Rural     Open Access   (Followers: 1)

        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]
  • Finite element model to study the thawing of packed frozen vegetables as
           influenced by working environment temperature
    • Authors: Chiara Cevoli; Angelo Fabbri; Urszula Tylewicz; Pietro Rocculi
      Pages: 1 - 11
      Abstract: Publication date: June 2018
      Source:Biosystems Engineering, Volume 170
      Author(s): Chiara Cevoli, Angelo Fabbri, Urszula Tylewicz, Pietro Rocculi
      Freezing is the most common process for long-time preservation of food. In order to avoid changes of texture, colour or flavour, the frozen products should not be subjected to temperature fluctuations; however, in between the packaging and cold storage steps, the products are frequently subjected to routine controls at an environment temperature of about 10 °C, which risk inducing a heating of the frozen foods. To study the effect of environment temperature on heat transfer inside frozen foods a parametric finite element model capable of describing the conduction and convection phenomena, inside and on the surface of the packages, was developed and validated for three products (peas, spinach cubes and grilled aubergines). The initial and final thawing temperatures were measured by using a differential scanning calorimeter. Acceptable agreement was obtained between numerical and experimental results with a maximum error of 1.8 °C. The relation between calculated product temperatures, environment temperature and time was investigated and a good fit was obtained (R2 > 0.97). Furthermore, specific relations between time required to reach the initial thawing temperature and the ambient temperature, were determined. The model could be used for other different vegetable products by changing material properties.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2018.03.005
      Issue No: Vol. 170 (2018)
  • Optimisation of the harvesting time of rice in moist and non-moist
           dispersed fields
    • Authors: Pengfei He; Jing Li; Dongqing Zhang; Shan Wan
      Pages: 12 - 23
      Abstract: Publication date: June 2018
      Source:Biosystems Engineering, Volume 170
      Author(s): Pengfei He, Jing Li, Dongqing Zhang, Shan Wan
      Optimisation of the harvesting time of rice is significant for wheat growth and reducing the operational cost of harvesting rice in wheat-rice rotation regions. In China, rice grows in discontinuous and dispersed fields, and it is harvested with combine-harvesters provided by an agricultural machinery cooperative. The dispersed fields are divided into two types, moist and non-moist. The moist fields can be harvested only by a crawler-harvester, and the non-moist farmlands can be harvested by a crawler-harvester or a wheeled harvester. The objective of the agricultural machinery cooperative is to minimise the harvesting time of the rice. Minimising the differences in the operational time between different types of combine-harvesters is constrained. In this study, we proposed an operational model that considers using two different types of combine-harvester to harvest rice in dispersed fields with different soil moisture levels. Three versions of this operational model were derived for different types of farmland. Actual data from a village in Bengbu city were used to parameterise the model. The results indicate that optimisation significantly decreased the harvesting time of rice. The characteristics of the operational time of combine-harvesters are discussed separately to promote the efficient use of combine-harvesters.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2018.03.008
      Issue No: Vol. 170 (2018)
  • Drying rate control in microwave assisted processing of sliced apples
    • Authors: Gennaro Cuccurullo; Laura Giordano; Antonio Metallo; Luciano Cinquanta
      Pages: 24 - 30
      Abstract: Publication date: June 2018
      Source:Biosystems Engineering, Volume 170
      Author(s): Gennaro Cuccurullo, Laura Giordano, Antonio Metallo, Luciano Cinquanta
      The most enhanced microwave systems for the preparation of quality-dehydrated fruits continuously adjust the power level in order to maintain the product temperature above a target value. As a result, typical drying curves that exhibit high drying rates in the middle stage are obtained. This can often lead to quality damage or undesirable changes on food colour and texture. In response to these issues, a microwave system is proposed that can realise drying processes keeping drying rates constant. This approach required a continuous temperature adjustment of the apple slices under test, whose temperature was detected by a computer-aided infrared thermography system. Since temperature corrections were required only during the middle stage of the process, the overall drying time was only slightly affected by the proposed control strategy. Nevertheless, compared to microwave drying with different constant temperatures (60, 70 and 80 °C), the resultant benefits of operating at constant drying rates included an improvement of texture and rehydration properties. No differences in colour of sliced apples were observed.
      Graphical abstract image

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2018.03.010
      Issue No: Vol. 170 (2018)
  • Characterisation of pig manure for methane emission modelling in
           Sub-Saharan Africa
    • Authors: Ngwa M. Ngwabie; Bren N. Chungong; Fabrice L. Yengong
      Pages: 31 - 38
      Abstract: Publication date: June 2018
      Source:Biosystems Engineering, Volume 170
      Author(s): Ngwa M. Ngwabie, Bren N. Chungong, Fabrice L. Yengong
      There is limited information in the literature regarding models or direct measurements of CH4 emissions from the rapidly growing pig industry in Sub-Saharan Africa. Measurements were conducted in pig fattening barns with slated and concreted floors in Cameroon to measure manure parameters and to subsequently model CH4 emissions. Each barn was partitioned into pens containing pigs of a similar body mass range. Manure, collected over 24 h from each pen was weighed to calculate the average daily production per pig. Manure collected from each pen was mixed homogenously, subsampled into 60 ml vials for subsequent analyses of its moisture, dry matter (DM), ash and volatile solid (VS) contents. The daily VS excretion rate was used to model CH4 production. On average, a 50 kg pig produced about 3 kg of manure day−1, of which 2.09 kg (∼70%) was the moisture content and 0.91 kg (∼30%) was the DM. The ash content of the manure was 17% DM (0.14 kg pig−1 day−1) while the VS was 83% DM (0.77 kg pig−1 day−1). A 28 kg pig produced about 0.21 kg VS pig−1 day−1, which is lower than the 0.30 kg VS pig−1 day−1 value recommended by IPCC, 2006 for Africa. The average CH4 production rate was estimated as 1.68 ± 1.14 kg pig−1 day−1. Exponential equations with coefficients of determinations (R2) > 88% were used to describe the relationships between manure, DM and VS excretion rates, as well as CH4 production rates as a function of the mass of the pigs.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2018.03.009
      Issue No: Vol. 170 (2018)
  • Recognising weeds in a maize crop using a random forest machine-learning
           algorithm and near-infrared snapshot mosaic hyperspectral imagery
    • Authors: Junfeng Gao; David Nuyttens; Peter Lootens; Yong He; Jan G. Pieters
      Pages: 39 - 50
      Abstract: Publication date: June 2018
      Source:Biosystems Engineering, Volume 170
      Author(s): Junfeng Gao, David Nuyttens, Peter Lootens, Yong He, Jan G. Pieters
      This study explores the potential of a novel hyperspectral snapshot mosaic camera for weed and maize classification. The image processing, feature engineering and machine learning techniques were discussed when developing an optimal classification model for the three kinds of weeds and maize. A total set of 185 spectral features including reflectance and vegetation index features was constructed. Subsequently, the principal component analysis was used to reduce the redundancy of the constructed features, and the first 5 principal components, explaining over 95% variance ratio, were kept for further analysis. Furthermore, random forests as one of machine learning techniques were built for developing the classifier with three different combinations of features. Accuracy-oriented feature reduction was performed when choosing the optimal number of features for building the classification model. Moreover, hyperparameter tuning was explored for the optimal selection of random forest model. The results showed that the optimal random forest model with 30 important spectral features can achieve a mean correct classification rate of 1.0, 0.789, 0.691 and 0.752 for Zea mays, Convolvulus arvensis, Rumex and Cirsium arvense, respectively. The McNemar test showed an overall better performance of the optimal random forest model at the 0.05 significance level compared to the k-nearest neighbours (KNN) model.
      Graphical abstract image

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2018.03.006
      Issue No: Vol. 170 (2018)
  • A flexible and practical approach for real-time weed emergence prediction
           based on Artificial Neural Networks
    • Authors: Guillermo R. Chantre; Mario R. Vigna; Juan P. Renzi; Aníbal M. Blanco
      Pages: 51 - 60
      Abstract: Publication date: June 2018
      Source:Biosystems Engineering, Volume 170
      Author(s): Guillermo R. Chantre, Mario R. Vigna, Juan P. Renzi, Aníbal M. Blanco
      Most popular emergence prediction models require species-specific population-based parameters to modulate thermal/hydrothermal accumulation. Such parameters are frequently unknown and difficult to estimate. Moreover, such models also rely on hardly available and difficult to estimate soil site-specific microclimate conditions, which in turn depend on soil heterogeneity at a field spatial level. On the other hand, modern agriculture benefits from easily available real-time information, in particular on-line meteorological data generated by forecasts and automatic local weather stations. In this context, Artificial Neural Networks (ANN) provide a flexible option for the development of prediction models, especially to study species which show a highly distributed emergence pattern along the year. In this work, an ANN approach based on easily obtainable meteorological data (daily minimum and maximum temperatures; daily precipitation) is proposed for weed emergence prediction. Relative Daily Emergence (RDE), expressed as a proportion of the total emergence, was the adopted output variable. Field emergence data recorded on a weekly basis were used to generate RDE patterns through linear interpolation. Results for three study cases from the Semiarid Pampean Region of Argentina (Lolium multiflorum, Avena fatua and Vicia villosa), which show irregular and time-distributed field emergence patterns, are reported. In all cases, ANN model selection was based on the Root Mean Square Error of the test set which showed better consistency than other typical Information Theory performance metrics. The combination of large ANN with a Bayesian Regularization Algorithm generated satisfactory estimations based on the RMSE values for independent Cumulative Emergence data.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2018.03.014
      Issue No: Vol. 170 (2018)
  • 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)
  • Full-scale experiments in forced-air precoolers for citrus fruit: Impact
           of packaging design and fruit size on cooling rate and heterogeneity
    • Authors: Wentao Wu; Philippe Häller; Paul Cronjé; Thijs Defraeye
      Pages: 115 - 125
      Abstract: Publication date: May 2018
      Source:Biosystems Engineering, Volume 169
      Author(s): Wentao Wu, Philippe Häller, Paul Cronjé, Thijs Defraeye
      Forced-air cooling (FAC) is a widely applied postharvest technology to rapidly remove the field heat of packed fresh fruit. The cooling uniformity of the fruit in different pallets and cartons during FAC is critical but often remains unknown in commercial operations. This study investigated the cooling rate and heterogeneity of packed citrus fruit in a full-scale, forced-air precooler with 40 pallets of fruit. The influence of package design (package type and wrapping) and fruit size on the precooling performance was quantified in several experiments with three types of citrus fruits (‘Navel’ orange fruit, ‘Nova’ mandarin fruit and ‘Eureka’ lemon fruit). Results showed that the cooling heterogeneity mainly occurred along the flow direction. Cooling was uniform between horizontal regions at the same side of the pallets and between the different heights in the precooler. High resolution measurements with 25–30 sensors in a single pallet gave an even better insight in this heterogeneity. Fruit wrapping induced a much slower cooling rate and larger cooling heterogeneity, especially in the cartons at the outflow side of the pallet. The ‘Nova’ mandarin fruit in Opentop cartons cooled 24% (at the inflow side of the pallet) and 42% (at the outflow side of the pallet) faster than the ‘Eureka’ lemon fruit with similar fruit size in Supervent cartons, showing the impact of packaging design. These experiments quantified the cooling heterogeneity of the commercial precoolers.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2018.02.003
      Issue No: Vol. 169 (2018)
  • Non-destructive investigation of cellular level moisture distribution and
           morphological changes during drying of a plant-based food material
    • Authors: Mohammad M. Rahman; Mohammad U.H. Joardder; Azharul Karim
      Pages: 126 - 138
      Abstract: Publication date: May 2018
      Source:Biosystems Engineering, Volume 169
      Author(s): Mohammad M. Rahman, Mohammad U.H. Joardder, Azharul Karim
      This study investigates the complex microstructural changes and cell-level water transportation in plant-based food materials during drying, using X-ray micro-computed tomography (X-ray μCT). The investigations were performed on apple tissue to uncover the cellular level moisture distribution and the structural changes during convective drying at 50 °C, 60 °C, and 70 °C. Image analysis revealed that significant changes occurred in moisture content, and cell and pore size distribution with drying time and temperature. The moisture content determined using the X-ray μCT images was compared with that determined by the electronic moisture analyser (EMA) and good agreement was found. The results show a strong relationship between drying temperature, pore formation and deformation of the food material. At high drying temperature, the pore formation increased, which led to reduced shrinkage of the food material. The porosity of a sample of dried apple increased by 35% as drying temperature increased from 50 °C to 70 °C. However, a significant amount of cell rupture was observed during drying at the higher temperature. The cellular level moisture distribution profile confirmed that a traceable amount of water was still present in the centre cells of the tissue although the sample was deemed dried from the bulk moisture analysis. The findings of this study substantially enhance our understanding of instantaneous cellular level moisture distribution in a food sample over the time of drying.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2018.02.007
      Issue No: Vol. 169 (2018)
  • Research on insect pest image detection and recognition based on
           bio-inspired methods
    • Authors: Limiao Deng; Yanjiang Wang; Zhongzhi Han; Renshi Yu
      Pages: 139 - 148
      Abstract: Publication date: May 2018
      Source:Biosystems Engineering, Volume 169
      Author(s): Limiao Deng, Yanjiang Wang, Zhongzhi Han, Renshi Yu
      Insect pest recognition and detection are vital for food security, a stable agricultural economy and quality of life. To realise rapid detection and recognition of insect pests, methods inspired by human visual system were proposed in this paper. Inspired by human visual attention, Saliency Using Natural statistics model (SUN) was used to generate saliency maps and detect region of interest (ROI) in a pest image. To extract the invariant features for representing the pest appearance, we extended the bio-inspired Hierarchical Model and X (HMAX) model in the following ways. Scale Invariant Feature Transform (SIFT) was integrated into the HMAX model to increase the invariance to rotational changes. Meanwhile, Non-negative Sparse Coding (NNSC) is used to simulate the simple cell responses. Moreover, invariant texture features were extracted based on Local Configuration Pattern (LCP) algorithm. Finally, the extracted features were fed to Support Vector Machines (SVM) for recognition. Experimental results demonstrated that the proposed method had an advantage over the compared methods: HMAX, Sparse Coding and Natural Input Memory with Bayesian Likelihood Estimation (NIMBLE), and was comparable to the Deep Convolutional Network. The proposed method has achieved a good result with a recognition rate of 85.5% and could effectively recognise insect pest under complex environments. The proposed method has provided a new approach for insect pest detection and recognition.
      Graphical abstract image

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2018.02.008
      Issue No: Vol. 169 (2018)
  • Development of a rescue system for agricultural machinery operators using
           machine vision
    • Authors: Yan Zhang; Pengbo Gao; Tofael Ahamed
      Pages: 149 - 164
      Abstract: Publication date: May 2018
      Source:Biosystems Engineering, Volume 169
      Author(s): Yan Zhang, Pengbo Gao, Tofael Ahamed
      In this study, an automatic rescue system was proposed to monitor agricultural machinery operators using machine vision. The rescue system was developed to recognise the driver inattention status, that is, the distraction and fatigue by recognising the driver's actions. A Kinect sensor was used to collect image sequences of the operators, and the recognition system relied on the “player extraction” function of the Kinect sensor. A Hankel-based Kernel Mutual Subspace Method (KMSM) was developed to monitor tractor drivers and recognise driver inattention behaviours. To reduce the computational complexity for fulfilling the requirements of recognition, low-dimensional image vectors were used to generate low-dimensional block Hankel matrixes as representations for input action sequences. To evaluate the performance of the proposed KMSM, a driver action dataset was established that included 10 tractor drivers and 5 types of action that denote inattention. The drivers' inattention actions were classified into three danger levels, and the corresponding countermeasures for the actions at each danger level were similarly classified. Both offline and online experiments using similar subjects and different subjects were conducted to evaluate the designed inattention action recognition algorithm. In the offline experiment, the proposed Hankel-based KMSM achieved recognition rates of 91.18% and 86.18% when using similar and different subjects, respectively; and in the online experiment, the proposed method achieved 87.02 and 79.97% when using similar and different subjects, respectively. The average computation time of the Hankel-based KMSM was 0.07 s in the online experiment. Thus, the proposed Hankel-based KMSM method satisfies both the accuracy and the real-time requirements for a driver rescue system.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2018.02.009
      Issue No: Vol. 169 (2018)
  • Development of passive flux samplers based on adsorption to estimate
           greenhouse gas emissions from agricultural sources
    • Authors: Araceli D. Larios; Stéphane Godbout; Satinder K. Brar; Joahnn H. Palacios; Dan Zegan; Antonio Avalos-Ramírez; Fabiola Sandoval-Salas
      Pages: 165 - 174
      Abstract: Publication date: May 2018
      Source:Biosystems Engineering, Volume 169
      Author(s): Araceli D. Larios, Stéphane Godbout, Satinder K. Brar, Joahnn H. Palacios, Dan Zegan, Antonio Avalos-Ramírez, Fabiola Sandoval-Salas
      Passive flux samplers (PFSs) packed with adsorbents are used to estimate gaseous emissions. A key condition of their use is maintaining a linear relationship between internal and external air velocities. The performance of PFSs designs depends on the characteristics of the adsorption bed and on the sampler design. The parameters required to enable PFSs to estimate greenhouse (GHG) emissions from agricultural sources were studied. The effect of the particle size of the adsorbent used as collector medium was analyzed theoretically using the Ergun equation. Three orifice plates with 0.5, 0.7 and 1 mm bore diameter were evaluated in order to determine the most appropriate diameter to control air flow through a new passive flux sampler (PFS) prototype while maintaining adequate linearity between internal and external air velocity. The effect of the adsorbent bed thickness (19, 50, 100 and 200 mm) on the internal-external air velocity relationship in the PFS was evaluated. The best performance was obtained using the 0.7 mm orifice plate and an adsorbent bed thickness of 50 mm. Spherical adsorbents with high adsorption capacity are recommended in order to decrease the adsorbent bed thickness and improve sampling performance. A series of experiments showed that the estimated mass flow obtained by the developed PFS was close to the confidence interval of values obtained by direct detection. Thus, the developed PFS can be used as a tool for the estimating of GHG emissions from agricultural sources.
      Graphical abstract image

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2018.02.010
      Issue No: Vol. 169 (2018)
  • Temperature gradient control during microwave combined with hot air drying
    • Authors: Wanxiu Xu; Chunfang Song; Zhenfeng Li; Feihu Song; Shaogang Hu; Jing Li; Guanyu Zhu; G.S. Vijaya Raghavan
      Pages: 175 - 187
      Abstract: Publication date: May 2018
      Source:Biosystems Engineering, Volume 169
      Author(s): Wanxiu Xu, Chunfang Song, Zhenfeng Li, Feihu Song, Shaogang Hu, Jing Li, Guanyu Zhu, G.S. Vijaya Raghavan
      Fresh carrot cubes were blanched and dried by microwave processing combined with hot air drying. Material temperature, temperature difference and size of cube were optimised in terms of total vitamin C content, colour change, drying time, sensory evaluation and rehydration ratio of dried samples. During a stage under the optimal drying condition, drying rate was affected by temperature gradient, as evidenced by changed shrinkage. The effect of different temperature gradients on the drying rate and quality was compared with that under the optimal condition by using fitted shrinkage models of the optimised material temperature, temperature difference and size of cube. The quality of dried carrot cubes under the average temperature gradient (ATG) of 6 °C mm−1 was better than that under other ATGs. A simple linear control method was developed based on the control for industrial production. Results showed that the product quality and drying time of samples in linear control were similar to those dried at 6 °C mm−1.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2018.02.013
      Issue No: Vol. 169 (2018)
  • A machine learning approach for pixel wise classification of residue and
           vegetation cover under field conditions
    • Authors: Peter Riegler-Nurscher; Johann Prankl; Thomas Bauer; Peter Strauss; Heinrich Prankl
      Pages: 188 - 198
      Abstract: Publication date: May 2018
      Source:Biosystems Engineering, Volume 169
      Author(s): Peter Riegler-Nurscher, Johann Prankl, Thomas Bauer, Peter Strauss, Heinrich Prankl
      Soil cover is a crucial factor for sustainable cultivation of arable land. A certain degree of residue and vegetation cover reduces erosion significantly and has positive effects on plant development. In order to accomplish these positive effects, it is necessary to measure and control the amount of soil cover on fields. Manual measurement methods are time consuming and/or subjective. Available image analysis methods often lack of generalisation and accuracy. Many approaches only focus on residue or on vegetation cover and do not consider different camera hardware. Recent advancements in machine learning techniques are promising to overcome these issues. The proposed method, the entangled random forest, a variant of a random decision forest, classifies individual pixels into soil, residue, living plants and stones. Simple and efficient pixel-wise comparisons to neighbouring pixels are integrated as decision-features into the random forest. To validate our method, the result of the automatic classification was compared with results of manual classifications from evaluators on image grid points. The classification of soil results in a regression equation between the results of the new introduced method and a manual image classification of y = 0.99x + 2.02 (R2 = 0.93). Living plant classification results in a regression between both methods in y = 0.94x − 0.70 with (R2 = 0.98) and for dead residues in y = 1.04x − 0.64 (R2 = 0.84). It is possible to access a demo of the algorithm by using a web and a mobile application on

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2018.02.011
      Issue No: Vol. 169 (2018)
  • Comparison of the discrete element and finite element methods to model the
           interaction of soil and tool cutting edge
    • Authors: Mustafa Ucgul; Chris Saunders; John M. Fielke
      Pages: 199 - 208
      Abstract: Publication date: May 2018
      Source:Biosystems Engineering, Volume 169
      Author(s): Mustafa Ucgul, Chris Saunders, John M. Fielke
      Soil tillage is an energy intensive operation and design improvements that reduce forces have been pursued for many different tools. Due to the high cost of prototyping and testing, computer modelling has been adopted to design tillage equipment. In order to model soil-tool interaction two methods namely; finite element method (FEM) and discrete element method (DEM) have been used. Fielke (1994 and 1996) found that for shallow working low rake angle tools the cutting edge geometry of a tillage tool has a major effect on tillage forces and soil movement. FEM modelling of the tests was also carried out in Fielke (1999). In this paper the experimental work of Fielke (1994) was simulated using DEM techniques and the results were compared to both the measured data and FEM predicted results. The results of the study showed that better vertical force prediction was obtained using DEM whereas forward soil movements below the tillage depth were simulated more accurately using FEM. This can be attributed to the larger size of particles used in DEM simulation than needed to pass around the cutting edge. It was also shown that DEM can be used to accurately predict soil failure planes.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2018.03.003
      Issue No: Vol. 169 (2018)
  • Estimation of ground canopy cover in agricultural crops using
           downward-looking photography
    • Authors: Francesco Chianucci; Andrea Lucibelli; Maria T. Dell'Abate
      Pages: 209 - 216
      Abstract: Publication date: May 2018
      Source:Biosystems Engineering, Volume 169
      Author(s): Francesco Chianucci, Andrea Lucibelli, Maria T. Dell'Abate
      Fast and accurate estimates of canopy cover are central for a wide range of agricultural applications and studies. Visual assessment is a traditionally employed method to estimate canopy cover in the field, but it is limited by the costs, subjectivity and non-reproducibility of the produced estimates. Digital photography is a low-cost alternative method. In this study we tested two automated image classification methods, the first one based on a histogram-analysis method (Rosin), the second one based on a combination of a visible vegetation index and the L*a*b* colour space conversion (LAB2), which have both been previously tested in forestry, and a supervised image classification method (Winscanopy), to estimate canopy cover from downward-looking images of agricultural crops. These methods were tested using artificial images with known cover; this allowed exploring the influence of canopy density and object size on canopy cover estimation from photography. The Rosin method provided the best estimates of canopy cover in artificial images, whose accuracy was largely unaffected by variation in canopy density and object size. By contrast, LAB2 systematically overestimated canopy cover, because of the sensitivity of the method to small variations of chromaticity in artificial images. Winscanopy showed good performance when at least two regions per class were manually selected from a representative image. The results were replicated in real images of cultivated aromatic crops. The main findings indicate that digital photography is an effective method to obtain rapid, robust and reproducible measures of canopy cover in downward-looking images of agricultural crops, including aromatic plants.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2018.02.012
      Issue No: Vol. 169 (2018)
  • Developing and validating radio frequency pasteurisation processes for
           almond kernels
    • Authors: Rui Li; Xiaoxi Kou; Lixia Hou; Bo Ling; Shaojin Wang
      Pages: 217 - 225
      Abstract: Publication date: May 2018
      Source:Biosystems Engineering, Volume 169
      Author(s): Rui Li, Xiaoxi Kou, Lixia Hou, Bo Ling, Shaojin Wang
      The efficacy of radio frequency (RF) treatment to inactivate Escherichia coli ATCC 25922 used as a surrogate of pathogenic Salmonella in almond kernels was investigated. A pilot-scale, 27 MHz, 6 kW RF unit was used to study the heating uniformity of almond kernels at the moisture content of 10.01% w.b. using two types of plastic containers. Pasteurisation processes were developed and validated for almond kernels and the product quality after RF treatments was evaluated. The results showed that only 6.4 min was needed to raise the central temperature of 1.5 kg almond kernels from 25 °C to 72 °C by using RF energy as compared to 276 min using 72 °C hot air in a five-layer container. An effective RF treatment protocol was obtained using an electrode gap of 11 cm, 72 °C hot air surface heating, intermittently rearranging the five-layers in less than 1 min, and holding in 72 °C hot air for 15 min for pasteurisation to achieve more than 4-log reductions of E. coli ATCC 25922, drying to reduce the moisture level, and cooling in the single layer by forced room air to avoid product quality degradation. The moisture content, fatty acid, peroxide value and colour values of RF treated almond kernels met the good quality standard used by the almond industry. Therefore, RF treatments are an effective and rapid heating method for potentially pasteurising Salmonella in almond kernels.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2018.03.001
      Issue No: Vol. 169 (2018)
  • Electrical impedance phase angle as an indicator of plant root stress
    • Authors: Imre Cseresnyés; Kálmán Rajkai; Tünde Takács; Eszter Vozáry
      Pages: 226 - 232
      Abstract: Publication date: May 2018
      Source:Biosystems Engineering, Volume 169
      Author(s): Imre Cseresnyés, Kálmán Rajkai, Tünde Takács, Eszter Vozáry
      This study aimed to demonstrate that single-frequency (1 kHz) measurement of impedance phase angle (Φ) in root–soil systems is suitable for monitoring plant responses to environmental stresses. Potted wheat, soybean and maize plants were exposed to cadmium contamination, alkaline stress, drought or weed competition. Φ was detected at regular intervals between a ground and a plant electrode during plant development, at the end of which root and shoot biomass were measured. Each type of stress significantly reduced both Φ and the root and shoot dry mass, to an extent proportional to the stress level. The decrease in Φ was attributed to various physicochemical changes in root cell membranes, the accelerated maturation of the exo- and endodermis and altered root morphology. These stress responses modified the dielectric properties of the root tissues, influencing the apoplast and symplast pathways of the electrical current inside the roots. The stress-induced increase in the amount of electrically insulating lignin and suberin in root tissues was considered to be an influential factor in decreasing Φ. These results show that in pot experiments the measurement of the impedance phase angle in intact root systems is a potentially useful in situ method for detecting plant responses to stresses affecting roots.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2018.03.004
      Issue No: Vol. 169 (2018)
  • Computational tools to support soil management decisions
    • Authors: Giovanna Vessia; Ruth Falconer; Beate Zimmermann; Ana M. Tarquis
      Pages: 1 - 3
      Abstract: Publication date: April 2018
      Source:Biosystems Engineering, Volume 168
      Author(s): Giovanna Vessia, Ruth Falconer, Beate Zimmermann, Ana M. Tarquis

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2018.03.012
      Issue No: Vol. 168 (2018)
  • Random field theory to interpret the spatial variability of lacustrine
    • Authors: Giovanna Vessia; Savino Russo
      Pages: 4 - 13
      Abstract: Publication date: April 2018
      Source:Biosystems Engineering, Volume 168
      Author(s): Giovanna Vessia, Savino Russo
      The mechanical characterisation of heterogeneous soils, such as alluvial deposits, is commonly performed through a deterministic approach. This latter consists on applying the engineering judgment to choose a mean trend from continuous vertical readings of in field investigations (e.g. Cone Penetration Tests – CPTs) or discontinuous ones as Standard Penetration Tests (SPTs). Conversely, in order to take into account the spatial variability of mechanical measurements of the soil the spatial standard deviation, the scale of fluctuation and the autocorrelation function have to be calculated. This latter approach follows the stochastic field theory and it can be fruitfully applied to all those soil formations that are inherently heterogeneous. In this paper, theoretical bases of this approach has been briefly described and a practical application to lacustrine soil deposits at Popoli site located in Abruzzi Region (Italy) are presented. The methods introduced are not straightforward but they provide information that can be used to improve both the reliability of the geotechnical design and the efficiency of the soil use depending on the investigated depths and the measurement intervals.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2017.08.023
      Issue No: Vol. 168 (2018)
  • Detrended fluctuation analysis for spatial characterisation of landscapes
    • Authors: M.T. Castellanos; M.C. Morató; P.L. Aguado; J.P. del Monte; A.M. Tarquis
      Pages: 14 - 25
      Abstract: Publication date: April 2018
      Source:Biosystems Engineering, Volume 168
      Author(s): M.T. Castellanos, M.C. Morató, P.L. Aguado, J.P. del Monte, A.M. Tarquis
      The interactions among abiotic, biotic, and anthropic factors and their influence at different scales create a complex dynamic in landscape evolution. Scaling and multifractal analysis have the potential to characterise landscapes in terms of the statistical signature of the selected measure, in this case, altitude. This work evaluates the multifractality of altitude data points along transects that are obtained in several directions using Detrended Fluctuation Analysis (DFA) in a protected area adjacent to Madrid. The study data set consist of a matrix 2048 × 2048 pixels obtained at a 5 m resolution and extracted from a digital terrain model (DTM) using a Geographic Information System (GIS). We found that the distribution of altitude fluctuations at small scales revealed a non-Gaussian character in the statistical moments, indicating that Fractional Brownian modelling is not appropriate. Generalised Hurst dimensions (H(q)) were calculated on several transects crossing the area under study, all of which exhibited multifractality within a certain scale range. The results show a persistent behaviour in all directions because all of the H(q) values exceeded 0.5 and because there were differences in the intensities of the multifractality. The analysis of the directionality by means of a generalised Hurst rose plot showed differences in the scaling characteristics both along and across rivers and reservoirs. This indicates a clear anisotropy that is mainly due to the directions of the two river basins located in the area and the basement movement as a consequence of gradual tectonic displacement, which must be considered in two-dimensional DFAs.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2017.09.016
      Issue No: Vol. 168 (2018)
  • Using geographical information system to generate a drought risk map for
           rice cultivation: Case study in Babahoyo canton (Ecuador)
    • Authors: Omar Valverde-Arias; Alberto Garrido; José L. Valencia; Ana María Tarquis
      Pages: 26 - 41
      Abstract: Publication date: April 2018
      Source:Biosystems Engineering, Volume 168
      Author(s): Omar Valverde-Arias, Alberto Garrido, José L. Valencia, Ana María Tarquis
      Extreme weather events are occurring more frequently, and they affect people, infrastructure and crops. Drought is one of the most important hazards that threaten agriculture and livestock. Such adverse events can result in yield losses over large cropping areas. It is difficult for farmers to cope with prolonged periods of drought. In the absence of government support, rainfed farmers have very limited adaptation capabilities. In this study, we used geographical information system (GIS) tools to generate a drought risk map (DRM) for rice cropping in Babahoyo Canton, Ecuador. This map represents production risks incurred through the onset of a drought event based on the interaction between vulnerability and threat. The vulnerability of rice cropping is determined through a soil land evaluation for rice cropping and the availability of water during the crop cycle rather than approaching the issue based on drought indices, such as the standard precipitation index (SPI). Threat is the likelihood of the occurrence of a drought event that affects rice crops. The DRM was compared to another drought risk map that was generated based only on climatic variables as risk factors (DRcM). We adjusted the DRM and DRcM by adding a layer of operational irrigation projects and by removing drought hazards from areas that are currently irrigated. Based on Normalized difference vegetation index (NDVI) temporal series and drought claims insurance data, the obtained DRM and DRcM were validated. Both maps show a significant relationship between zone classification and NDVI values. However, the DRM adjusted better to insurance data than the DRcM, which does not consider soil variables. The DRM is an effective tool for agricultural risk management decision-making and it serves as an important source of information that could be used for index-based insurance implementation.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2017.08.007
      Issue No: Vol. 168 (2018)
  • Singularity maps applied to a vegetation index
    • Authors: Juan J. Martín-Sotoca; Antonio Saa-Requejo; Javier Borondo; Ana M. Tarquis
      Pages: 42 - 53
      Abstract: Publication date: April 2018
      Source:Biosystems Engineering, Volume 168
      Author(s): Juan J. Martín-Sotoca, Antonio Saa-Requejo, Javier Borondo, Ana M. Tarquis
      Agricultural drought quantification is one of the most important tasks in the characterisation process of this natural hazard. Recently, several vegetation indexes based on remote-sensing data have been applied to quantify it, being the Normalized Difference Vegetation Index (NDVI) the most widely used. Some index-based drought insurances define a drought event through the comparison of actual NDVI values in a given period with a NDVI threshold based on historical data of that period extrapolating this result spatially to the surrounded areas. Hence, the spatial statistical approach is very relevant and has not been deeply studied in this context. Drought can be highly localised, and several authors have recognised the critical role of the spatial variability. Therefore, it is important to delimit areas that will share NDVI statistical distributions and in which the same criteria can be applied to define the drought event. In order to do so, we have applied for the first time in this context the method of singularity maps commonly used in localisation of mineral deposits. The NDVI singularity maps calculated for each season and different years are shown and discussed in this context. For this study we have selected a region that includes the whole Autonomous Community of Madrid (Spain). The resulting singularity maps show that areas where the NDVI follows theoretically a spatial normal/log-normal distribution ( α ≅ 2 ) are widely scattered in the area of study and vary across seasons and years. Therefore, the extrapolation of normal/log-normal NDVI statistics should be applied only inside these areas.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2017.08.008
      Issue No: Vol. 168 (2018)
  • Characterising effects of management practices, snow cover, and soil
           texture on soil temperature: Model development in DNDC
    • Authors: Baishali Dutta; Brian B. Grant; Katelyn A. Congreves; Ward N. Smith; Claudia Wagner-Riddle; Andrew C. VanderZaag; Mario Tenuta; Raymond L. Desjardins
      Pages: 54 - 72
      Abstract: Publication date: April 2018
      Source:Biosystems Engineering, Volume 168
      Author(s): Baishali Dutta, Brian B. Grant, Katelyn A. Congreves, Ward N. Smith, Claudia Wagner-Riddle, Andrew C. VanderZaag, Mario Tenuta, Raymond L. Desjardins
      Agro-ecosystem models, such as the DNDC (DeNitrification and DeComposition) model are useful tools when assessing the sustainability of agricultural management. Accuracy in soil temperature estimations is important as it regulates many important soil biogeochemical processes that lead to greenhouse gas emissions (GHG). The objective of this study was to account for the effects of snow cover in terms of the measured snow depth (mm of water), soil texture and crop management in temperate latitudes in order to improve the surface soil temperature mechanism in DNDC and thereby improve GHG predictions. The estimation of soil temperature driven by the thermal conductivity and heat capacity of the soil was improved by considering the soil texture under frozen and unfrozen conditions along with the effects of crop canopy and snow depth. Calibration of the developed model mechanisms was conducted using data from Alfred, ON under two contrasting soil textures (sandy loam vs. clay). Independent validation assessments were conducted using soil temperatures at different depths for contrasting managements for two field sites located in Canada (Guelph, ON and Glenlea, MB). The validation results indicated high model accuracy (R2 > 0.90, EF ≥ 0.90, RMSE < 3.00 °C) in capturing the effects of management on soil temperature. These developments in soil heat transfer mechanism improved the performance of the model in estimating N2O emissions during spring thaw and provide a foundation for future studies aimed at improving simulations in DNDC for better representations of other biogeochemical processes.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2017.02.001
      Issue No: Vol. 168 (2018)
  • Discrete element simulations and experiments of soil disturbance as
           affected by the tine spacing of subsoiler
    • Authors: Chengguang Hang; Xijie Gao; Mengchan Yuan; Yuxiang Huang; Ruixiang Zhu
      Pages: 73 - 82
      Abstract: Publication date: April 2018
      Source:Biosystems Engineering, Volume 168
      Author(s): Chengguang Hang, Xijie Gao, Mengchan Yuan, Yuxiang Huang, Ruixiang Zhu
      Tine spacing is a key parameter for the design of a subsoiler and has a significant effect on soil disturbance, which is a critical performance indicator of subsoiling. In this study, a subsoiling model was developed using the discrete element method (DEM). A subsoiling experiment was also conducted in a field with a loamy clay soil to serve the model development and model validations. In both the simulation and experiment, two V-shaped subsoiling tines were investigated at five different tine spacings (300, 350, 400, 450, and 500 mm), a constant working speed (0.83 m s−1) and a constant working depth (300 mm). The results showed that the 400 mm tine spacing resulted in the highest particle forces in the middle and deep soil layers. The height of the unloosened soil between two adjacent subsoilers increased as tine spacing increased. When the tine spacing was varied from 300 to 500 mm, the undisturbed soil height was changed from 100 to 226 mm in the experiment, and from 79 to 170 mm in the modelling. When the tine spacing was 400 mm, the number of soil particles disturbed in the shallow soil layer accounted for 45.6% of the total soil particles disturbed, which was the least among all the tine spacings. Considering the characteristics of soil disturbance, the tine spacing of 400 mm appeared to outperform the other spacings.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2017.03.008
      Issue No: Vol. 168 (2018)
  • Estimating soil thermal diffusivity at different water contents from
           easily available data on soil texture, bulk density, and organic carbon
    • Authors: Tatiana Arkhangelskaya; Ksenia Lukyashchenko
      Pages: 83 - 95
      Abstract: Publication date: April 2018
      Source:Biosystems Engineering, Volume 168
      Author(s): Tatiana Arkhangelskaya, Ksenia Lukyashchenko
      This study provides an algorithm to estimate soil thermal diffusivity at any water content from data on soil texture, bulk density, and percentage of organic carbon. Models were trained on the dataset of 77 soil samples including silty clays, silty clay loams, silt loams, clay loams, loams, sandy clay loams, sandy loams, loamy sands, and sands. The ranges of sand, silt, and clay within the dataset were 1–97, 2–80, and 1–52%; wet bulk density varied from 860 to 1820 kg m−3, organic carbon ranged from 0.1 to 6.5%. Thermal diffusivity of the undisturbed soil cores measured by the unsteady-state method was from 0.77 to 10.09 × 10−7 m2 s−1. The dataset was split randomly into the training set of 67 samples and the test set of 10 samples; the procedure was repeated three times. Models were developed from the measured thermal diffusivity vs. water content curves. The experimental data points for each sample were described by a 4-parameter function. Parameters of average curves for different textural classes were also determined. Then regression equations were obtained to estimate the parameters of the thermal diffusivity vs. water content function for different soils: (i) from soil texture; (ii) from soil texture and bulk density; (iii) from soil texture and organic carbon; (iv) from soil texture, bulk density, and organic carbon. The test set data were used to evaluate the model performance. The normalised root mean square errors of the best-performing models were from 20 to 33% depending on soil information available.
      Graphical abstract image

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2017.06.011
      Issue No: Vol. 168 (2018)
  • The frequency domain approach to analyse field-scale miscible flow
           transport experiments in the soils
    • Authors: Gerardo Severino; Gerardo Toraldo; Daniel M. Tartakovsky
      Pages: 96 - 104
      Abstract: Publication date: April 2018
      Source:Biosystems Engineering, Volume 168
      Author(s): Gerardo Severino, Gerardo Toraldo, Daniel M. Tartakovsky
      A new approach to the estimate of the parameters u (advective velocity) and λ (dispersivity) characterizing solute transport in soils is presented. The pair (u, λ) is estimated by matching in the frequency domain (FD) the theoretical expression of moments pertaining to the breakthrough curve (BTC) against to the one evaluated by means of the experimental data. In particular, we demonstrate that to reduce the impact of the random measurement-errors upon such an estimate, it is worth retaining in the Fourier's expansion of the moments only the harmonics associated to the smaller frequencies. This is due to the fact that the Fourier transform moves most of the measurement-errors affecting moments in the high-frequency range. As a consequence, by adopting a relatively small number of harmonics to compute the Fourier transform of the experimental moments, one may filter out most of the noise. It is also shown that the number of harmonics to retain (cut-off) depends upon the soil's water content as well as the magnitude of the characteristic length ℓ E of the error relative to the dispersivity λ. The proposed methodology has been applied to a recently conducted plot-scale transport experiment. For comparison purposes, we have also estimated the pair (u, λ) by the classical method of moments (MM). Both the methods lead to the same value of the advective velocity u. This is explained by recalling that u depends upon the first-order moment, a quantity that is scarcely influenced by the measurement-errors. Instead, the estimate of the dispersivity λ (which is related to the second-order moment) is largely different (with the value achieved by the MM larger than the one obtained by the FD approach). Such a difference is addressed to the fact that in the MM the distortion-effect due to the measurement-errors amplifies with the increasing order of the moments, a phenomenon which is completely avoided in the FD approach by adopting the above mentioned cut-off.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2016.10.002
      Issue No: Vol. 168 (2018)
  • Multifractal and joint multifractal analysis of general soil properties
           and altitude along a transect
    • Authors: Glécio M. Siqueira; Ênio F.F. Silva; Eva Vidal-Vázquez; Antonio Paz-González
      Pages: 105 - 120
      Abstract: Publication date: April 2018
      Source:Biosystems Engineering, Volume 168
      Author(s): Glécio M. Siqueira, Ênio F.F. Silva, Eva Vidal-Vázquez, Antonio Paz-González
      Multifractal characterisation of soil spatial variability has the potential for providing a better understanding of the distribution patterns of data values, and may contribute to improved resource management. We examined the scaling heterogeneity and multiple scale relationships of soil general properties and topography using multifractal and joint multifractal techniques. Soil samples were collected down to 0.20 m depth and altitude was recorded at equal intervals of 3 m along a 396 m transect in an Orthic Podzol at Pernambuco, Brazil. Soil properties studied were: textural fractions, pH, organic carbon (OC), exchangeable cations, exchangeable acidity (H + Al), sum of bases (SB), cation exchange capacity (CEC) and percent base saturation (V). The spatial distribution of altitude and soil general properties, characterised through generalised dimension, Dq, and singularity spectra, f(α)–α, showed a well-defined multifractal structure. Notwithstanding, these variables displayed several degrees of scaling heterogeneity, which was lowest for pH, sand and clay contents and highest for exchangeable cations and silt content. Joint multifractals showed that correlations between pairs of variables may or may not be stronger at the observation scale than across a range of spatial scales. Hence, soil OC and pH showed higher relationships to CEC, (H + Al), SB and V at the observation than at multiple spatial scales, while local topography effects on pH and CEC were greater at multiple scales. Multifractal and joint multifractal analysis provided new insights to characterise the spatial patterns and the relationships between soil properties at multiple scales, and to evaluate the effect of topography on soil heterogeneity.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2017.08.024
      Issue No: Vol. 168 (2018)
  • Multiscaling properties of soil images
    • Authors: Iván G. Torre; Juan C. Losada; Ana M. Tarquis
      Pages: 133 - 141
      Abstract: Publication date: April 2018
      Source:Biosystems Engineering, Volume 168
      Author(s): Iván G. Torre, Juan C. Losada, Ana M. Tarquis
      Soil structure may be defined as the spatial arrangement of soil particles, aggregates and pores. The geometry of each one of these elements and their spatial arrangement has a great influence on the transport of fluids and solutes through the soil. Soil thin sections (STS) have been widely used to characterise them and more recently computed tomography (CT) has provided an alternative for observing intact soil structure in 3D. Both types of images are grey-scale, normally with 8 bit depth. In this work we propose to quantify the structural complexity of their spatial arrangement by applying multifractal analysis (MFA) to the original grey images no previous binarisation and compare the results in 2D and 3D. Their singularities (α) and f(α) spectra calculated have been used for this comparison. With this purpose, an original CT-scan image of 256 × 256 × 256 voxel-thick slices of a soil was used. Three 2D subsamples were extracted in three different directions to analyse and compare with the 3D structure. All images analysed presented a multiscaling character, in 2D and 3D, pointing out that the lower grey values are mainly influencing the scaling behaviour. The multifractal parameters were influenced by 2D slice position and direction and their values were lower than the ones obtained in the 3D image analysis. Therefore, in order to compare soil structures based on grey images, a 3D volume is desirable. The multiscaling nature of these images suggests using these algorithms as a basis to create synthetic images for testing thresholding algorithms.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2016.11.006
      Issue No: Vol. 168 (2018)
  • A cluster-graph model for herd characterisation in dairy farms equipped
           with an automatic milking system
    • Authors: Filippo Bonora; Stefano Benni; Alberto Barbaresi; Patrizia Tassinari; Daniele Torreggiani
      Pages: 1 - 7
      Abstract: Publication date: March 2018
      Source:Biosystems Engineering, Volume 167
      Author(s): Filippo Bonora, Stefano Benni, Alberto Barbaresi, Patrizia Tassinari, Daniele Torreggiani
      The analysis of data recorded by Automatic Milking System (AMS) in dairy livestock barns has a great potential for herd management and farm building design. A big amount of data about milk production and cow welfare is available from milking robot and many researches are focussing on them in order to find relationships and correlations among the various parameters. The goal of the study is to develop and test an innovative procedure for the comprehensive analysis of AMS-generated multi-variable time-series, with a focus on herd segmentation, aiming to support dairy livestock farm management. In particular, the study purpose is to develop and test a cluster-graph model using AMS-generated data, designed to provide an automatic grouping of the cows based on production and behavioural features. First, a k-means cluster analysis has been implemented to the average of the time series of the main parameters recorded for each cow by AMS in a barn in Italy over a summer period. Then, all the resulting subgroups have been converted in a network and a cluster-graph analysis has been applied in order to find herd-descriptive subgraphs. The results of the study have the potential impact of improving herd characterisation and lending support to cow monitoring and management. Furthermore, this method could represent a feasible procedure to convert alphanumeric data in a simple graphic visualisation of the herd without losing the quantitative information about every single animal.
      Graphical abstract image

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2017.12.007
      Issue No: Vol. 167 (2018)
  • On-line separation and sorting of chicken portions using a robust
           vision-based intelligent modelling approach
    • Authors: Nima Teimouri; Mahmoud Omid; Kaveh Mollazade; Hossein Mousazadeh; Reza Alimardani; Henrik Karstoft
      Pages: 8 - 20
      Abstract: Publication date: March 2018
      Source:Biosystems Engineering, Volume 167
      Author(s): Nima Teimouri, Mahmoud Omid, Kaveh Mollazade, Hossein Mousazadeh, Reza Alimardani, Henrik Karstoft
      One of the major issues in food industry is automatic sorting of chicken portions. In the present study, we propose a new on-line method based on combined machine vision techniques and linear and nonlinear classifiers to categorise chicken portions automatically. Mechanical framework, conveyor belt, electrical and control units, lighting box, charge-coupled device (CCD) camera, separating unit, and air compressor are included in the study proposed system. Major classes of chicken portions can be categorised as breast, leg, fillet, wing, and drumstick. Imaging procedure in the study is carried out using CCD camera and a computer system. Geometrical aspects, colour, and textural features are extracted in the next step using the study dataset, so the best ones could be selected accordingly through Chi-Square methodology. Partial least squares regression (PLSR), linear discriminant analysis (LDA) and artificial neural network (ANN) respectively are employed to classify the data. Considering total accuracy level of PLSR, LDA and ANN obtained in the study, results indicated better performance level of ANN compared to linear models. The machine vision algorithm developed here together with the ANN classifier were evaluated on a sorting machine to separate test samples using separating units in the on-line mode. The processing time of proposed method is estimated as 15 ms for each image. The overall accuracy in maximum speed of conveyor, 0.2 m s−1, was obtained 93 percent that is appropriate in real-time applications. The total rate of processing and sorting chicken portions was also measured as approximately 2800 samples per hour.
      Graphical abstract image

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2017.12.009
      Issue No: Vol. 167 (2018)
  • Synchronous magnetic flux-induced electrical response of orange juice
    • Authors: Lunan Guo; Liping Xue; Yao Zhang; Dandan Li; Mengyue Zhang; Yamei Jin; Na Yang; Xueming Xu
      Pages: 21 - 31
      Abstract: Publication date: March 2018
      Source:Biosystems Engineering, Volume 167
      Author(s): Lunan Guo, Liping Xue, Yao Zhang, Dandan Li, Mengyue Zhang, Yamei Jin, Na Yang, Xueming Xu
      The study proposes a method for measuring the electrical properties of orange juice by using two magnetic fluxes with the same frequency (or synchronous magnetic fluxes) at 400–700 Hz. The juice was passed through two spiral glass tubes, which formed the secondary coils of the transformer with different connection modes. Five measurement points (a, b, o, −a, and −b) were arranged at different terminals of the two coils to evaluate output voltages (U −aa , U oa , U −bb , and U ob ) under the fluxes. Control parameters included the excitation voltage (U P ), frequency, and phase difference. Results indicated that the output voltage of the juice increased linearly with increasing excitation voltage at all points. In-phase output voltages were higher than reverse-phase output voltages. The value for λ −aa (U −aa /U P ) remained stable as the excitation voltage increased. In addition, different physicochemical properties of orange juice caused a change in the output voltages, which was consistent with Ohm's law. Soluble solids content and U −bb were linearly correlated, showing R 2 values at 0.875 and a root-mean-square error of 0.702 Brix° at 20 V and 700 Hz. The method showed potential for the rapid determination of the quality of liquid foods by using magnetic flux-induced electrical parameters.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2017.12.003
      Issue No: Vol. 167 (2018)
  • Removal of hydrogen sulphide from pig house using biofilter with fungi
    • Authors: Michael J. Hansen; Claus L. Pedersen; Louise H. Søgaard Jensen; Lise B. Guldberg; Anders Feilberg; Lars P. Nielsen
      Pages: 32 - 39
      Abstract: Publication date: March 2018
      Source:Biosystems Engineering, Volume 167
      Author(s): Michael J. Hansen, Claus L. Pedersen, Louise H. Søgaard Jensen, Lise B. Guldberg, Anders Feilberg, Lars P. Nielsen
      Biological air cleaners used for reducing emissions of odorants are often challenged by the low solubility of reduced sulphur compounds. In a recent study high removal of hydrogen sulphide (∼75%) from the exhaust air from a pig house was achieved using a biofilter. The aim of this study was to investigate if this high removal could be due to the presence of fungi. The removal of reduced sulphur compounds in a 600-mm wide cellulose biofilter was measured at depths of 0, 200, 400 and 600 mm and the results compared with estimated fungal hyphae surface area per biofilm area. Over 19 months, removal of hydrogen sulphide was measured during periods with and without fungi. The results demonstrate a correlation between the fungal hyphae surface area and the removal of hydrogen sulphide with the highest removal in the first 200 mm of the biofilter and decreasing removal with depth. During periods with presence of fungi, the removal of hydrogen sulphide (64%) was significantly higher than during periods without fungi (18%). It is hypothesised that the observed fungi oxidise hydrogen sulphide and may play a major role in biofilters treating air from pig houses due to the expansion of the active surface area caused by the hyphae.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2017.12.004
      Issue No: Vol. 167 (2018)
  • Quantifying colour and spot characteristics for the ventral petals in
           Sinningia speciosa
    • Authors: Hao-Chun Hsu; Kung-Ling Hsu; Chuan-Yi Chan; Chun-Neng Wang; Yan-Fu Kuo
      Pages: 40 - 50
      Abstract: Publication date: March 2018
      Source:Biosystems Engineering, Volume 167
      Author(s): Hao-Chun Hsu, Kung-Ling Hsu, Chuan-Yi Chan, Chun-Neng Wang, Yan-Fu Kuo
      This study examined the colour and spot patterns for the ventral petals of a cross line in Sinningia speciosa. The second-generation individuals of the cross line exhibited phenotypic segregation in floral colour and spot patterns. Three colour traits (colour region ratio, region of interest colour, and colour gradient) and five spot traits (spot quantity, spot density, spot area ratio, spot colour, and background colour) were defined and quantified using image processing techniques. The variation in the traits and the correlations between the traits were also investigated. The results indicated a considerable degree of variation among the traits. The proposed approach can quantify petal traits more objectively and precisely compared with conventional naked eye examination. Thus, it can be used for applications, such as new flower variety determination, that require precise trait quantification.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2017.12.010
      Issue No: Vol. 167 (2018)
  • Optimisation of water demand forecasting by artificial intelligence with
           short data sets
    • Authors: Rafael González Perea; Emilio Camacho Poyato; Pilar Montesinos; Juan Antonio Rodríguez Díaz
      Abstract: Publication date: Available online 5 April 2018
      Source:Biosystems Engineering
      Author(s): Rafael González Perea, Emilio Camacho Poyato, Pilar Montesinos, Juan Antonio Rodríguez Díaz
      Irrigated agriculture is one of the key factors responsible for decreasing freshwater availability in recent years. Thus, the development of new tools which will help Irrigation District managers in their daily decision making process about the use of water and energy is essential. On the other hand, the new era of Big Data and information and communications technologies (ICT) has made it possible to have a larger amount of information available, leading to the development of new prediction tools. However, the quality and quantity of this information in many fields such as irrigated agriculture is limited. Consequently, the way in which the development of new predictive models is addressed must be reformulated. Thus, in this work, a new methodology to provide short-term forecasting of daily irrigation water demand when data availability is limited has been developed by coupling dynamic Artificial Neural Networks (ANN) architecture, the Bayesian framework and Genetic Algorithms (GA). The methodology was applied in the Bembézar MD Irrigation District (Southern Spain). The developed model improved the prediction accuracy by between 3% and 11% with respect to previous work. The best ANN model had a Standard Error Prediction (SEP) and a determination coefficient (R2) of 8.7% and 96%, respectively. The accuracy of the model developed makes it a powerful tool for the daily management of irrigation districts.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2018.03.011
  • Evaluation of a depth sensor for mass estimation of growing and finishing
    • Authors: Isabella C.F.S. Condotta; Tami M. Brown-Brandl; Késia O Silva-Miranda; John P. Stinn
      Abstract: Publication date: Available online 31 March 2018
      Source:Biosystems Engineering
      Author(s): Isabella C.F.S. Condotta, Tami M. Brown-Brandl, Késia O Silva-Miranda, John P. Stinn
      A method of continuously monitoring animal mass would aid producers by ensuring all pigs are gaining mass and would increase the precision of marketing pigs. Therefore, the development of methods for monitoring the physical conditions of animals would improve animal well-being and maximise the profitability of swine production. The objective of this research was to validate the use of depth images in predicting live animal mass. Seven hundred and seventy-two depth images and mass measurements were collected from a population of grow–finish pigs (equally divided between barrows and gilts). Three commercial sire lines (Landrace, Duroc, and Yorkshire) were equally represented. The pigs' volumes were calculated from the depth image. Linear equations were developed to predict mass from volume. Independent equations were developed for both gilts and barrows, each of the three commercial sire lines used, and a global equation for all combined data. Efroymson's algorithm was used to test for differences between the global equation and the two equations for the gilts and barrows and between the three commercial sire lines. The results showed that there was no significant difference between the global equation and the individual equations for barrows and gilts (p < 0.05), and the global equation was also no different from individual equations for each of the three sire lines (p < 0.05). The global equation was developed to predict mass from a depth sensor with an R2 of 0.9905. In conclusion, it appears that the depth sensor would be a reasonable approach to continuously monitor pig mass.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2018.03.002
  • Use of vocalisation to identify sex, age, and distress in pig production
    • Authors: Alexandra F.da S. Cordeiro; Irenilza de A. Nääs; Felipe da Silva Leitão; Andréia C.M. de Almeida; Daniella Jorge de Moura
      Abstract: Publication date: Available online 29 March 2018
      Source:Biosystems Engineering
      Author(s): Alexandra F.da S. Cordeiro, Irenilza de A. Nääs, Felipe da Silva Leitão, Andréia C.M. de Almeida, Daniella Jorge de Moura
      To assess animal welfare at a pig production farm is a time-consuming task. The present study aimed to investigate the differences in pig vocalisation as a function of the sex, age, and distress conditions, and to propose a way of identifying distressful situations. The individual vocalisations of 40 pigs were recorded (20 male and 20 female) during exposure to different distress in the farrowing, nursery, growth, and finishing phases. Vocalisation pitch differed between males (194.5 Hz) and females (218.2 Hz). Pig vocalisation was also different according to age, especially for the attributes of maximum and minimum amplitudes, and the frequency of formant 2. Diverse distress situations also were identified by various acoustic attributes. A decision-tree for classifying the distress condition for pigs was built (with an accuracy of 81.92%) using the machine-learning technique. Results indicate the possibility of estimating pig welfare by recording the vocalisation. The algorithm is also promising to identify pig sex and age.

      PubDate: 2018-04-15T15:09:49Z
      DOI: 10.1016/j.biosystemseng.2018.03.007
  • 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
  • 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
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