Pub Date : 2018-01-01DOI: 10.1017/S2040470018000134
{"title":"ABS volume 9 issue 3 Cover and Back matter","authors":"","doi":"10.1017/S2040470018000134","DOIUrl":"https://doi.org/10.1017/S2040470018000134","url":null,"abstract":"","PeriodicalId":7228,"journal":{"name":"Advances in Animal Biosciences","volume":"9 3","pages":"Pages b1-b2"},"PeriodicalIF":0.0,"publicationDate":"2018-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1017/S2040470018000134","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"137299213","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-01DOI: 10.1017/S2040470017000966
K. Piikki, M. Söderström, H. Stadig
The publicly available Digital Soil Map of Sweden (DSMS) contains topsoil clay content information in a 50m× 50m grid, and can be used as decision support in precision agriculture. However, it is also common that farmers have undertaken their own soil sampling (one sample per hectare with texture analysed in every third sample). In the present study, such soil samples from 403 farms were used to validate topsoil clay content information derived from 1) DSMS, 2) DSMS locally adapted by residual kriging, 3) DSMS locally adapted by regression kriging and 4) inverse distance weighting interpolation of the soil sample data without using DSMS. The latter has been common practice until now. The best method differed depending on the local accuracy of DSMS, the quality of the soil sampling and the spatial variation structure of the topsoil texture. The ‘Best method’ strategy, which meant to apply all the above methods and choose the one that performed best at each farm, significantly reduced the mean absolute error. We recommend using this strategy to locally adapt regional digital soil maps to derive accurate decision support for use in precision agriculture.
{"title":"Local adaptation of a national digital soil map for use in precision agriculture","authors":"K. Piikki, M. Söderström, H. Stadig","doi":"10.1017/S2040470017000966","DOIUrl":"https://doi.org/10.1017/S2040470017000966","url":null,"abstract":"The publicly available Digital Soil Map of Sweden (DSMS) contains topsoil clay content information in a 50m× 50m grid, and can be used as decision support in precision agriculture. However, it is also common that farmers have undertaken their own soil sampling (one sample per hectare with texture analysed in every third sample). In the present study, such soil samples from 403 farms were used to validate topsoil clay content information derived from 1) DSMS, 2) DSMS locally adapted by residual kriging, 3) DSMS locally adapted by regression kriging and 4) inverse distance weighting interpolation of the soil sample data without using DSMS. The latter has been common practice until now. The best method differed depending on the local accuracy of DSMS, the quality of the soil sampling and the spatial variation structure of the topsoil texture. The ‘Best method’ strategy, which meant to apply all the above methods and choose the one that performed best at each farm, significantly reduced the mean absolute error. We recommend using this strategy to locally adapt regional digital soil maps to derive accurate decision support for use in precision agriculture.","PeriodicalId":7228,"journal":{"name":"Advances in Animal Biosciences","volume":"23 1","pages":"430-432"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73740062","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-01DOI: 10.1017/S2040470017000322
M. C. Pineda, C. Perdomo, R. Caballero, A. Valera, J. A. Martínez-Casasnovas, J. Viloria
Precision agriculture (PA) requires reasonably homogeneous areas for site-specific management. This work explores the applicability of digital terrain classes obtained from a digital elevation model derived from UAV-acquired images, to define management units in in a relative flat area of about 6 ha. Elevation, together with other terrain variables such as: slope degree, profile curvature, plan curvature, topographic wetness index, sediment transport index, were clustered using the Fuzzy Kohonen Clustering Network (FKCN). Four terrain classes were obtained. The result was compared with a map produced by a classification of soil properties previously interpolated by ordinary kriging. The results suggest that areas for site-specific management can be defined from terrain classes based on environmental covariates, saving time and cost in comparison with interpolation of soil variables.
{"title":"Expedited generation of terrain digital classes in flat areas from UAV images for precision agriculture purposes","authors":"M. C. Pineda, C. Perdomo, R. Caballero, A. Valera, J. A. Martínez-Casasnovas, J. Viloria","doi":"10.1017/S2040470017000322","DOIUrl":"https://doi.org/10.1017/S2040470017000322","url":null,"abstract":"Precision agriculture (PA) requires reasonably homogeneous areas for site-specific management. This work explores the applicability of digital terrain classes obtained from a digital elevation model derived from UAV-acquired images, to define management units in in a relative flat area of about 6 ha. Elevation, together with other terrain variables such as: slope degree, profile curvature, plan curvature, topographic wetness index, sediment transport index, were clustered using the Fuzzy Kohonen Clustering Network (FKCN). Four terrain classes were obtained. The result was compared with a map produced by a classification of soil properties previously interpolated by ordinary kriging. The results suggest that areas for site-specific management can be defined from terrain classes based on environmental covariates, saving time and cost in comparison with interpolation of soil variables.","PeriodicalId":7228,"journal":{"name":"Advances in Animal Biosciences","volume":"14 1","pages":"828-832"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75545215","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-01DOI: 10.1017/S2040470017000899
C. Leroux, Hazaël Jones, A. Clenet, B. Dreux, M. Becu, B. Tisseyre
Yield maps are a powerful tool with regard to managing upcoming crop productions but can contain a large amount of defective data that might result in misleading decisions. The objective of this work is to help improve and compare yield data filtering algorithms by generating simulated datasets as if they had been acquired directly in the field. Two stages were implemented during the simulation process (i) the creation of spatially correlated datasets and (ii) the addition of known yield sources of errors to these datasets. A previously published yield filtering algorithm was applied on these simulated datasets to demonstrate the applicability of the methodology. These simulated datasets allow results of yield data filtering methods to be compared and improved.
{"title":"Simulating yield datasets: an opportunity to improve data filtering algorithms","authors":"C. Leroux, Hazaël Jones, A. Clenet, B. Dreux, M. Becu, B. Tisseyre","doi":"10.1017/S2040470017000899","DOIUrl":"https://doi.org/10.1017/S2040470017000899","url":null,"abstract":"Yield maps are a powerful tool with regard to managing upcoming crop productions but can contain a large amount of defective data that might result in misleading decisions. The objective of this work is to help improve and compare yield data filtering algorithms by generating simulated datasets as if they had been acquired directly in the field. Two stages were implemented during the simulation process (i) the creation of spatially correlated datasets and (ii) the addition of known yield sources of errors to these datasets. A previously published yield filtering algorithm was applied on these simulated datasets to demonstrate the applicability of the methodology. These simulated datasets allow results of yield data filtering methods to be compared and improved.","PeriodicalId":7228,"journal":{"name":"Advances in Animal Biosciences","volume":"80 1","pages":"600-605"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77562872","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-01DOI: 10.1017/S2040470017001042
F. Marinello, A. Pezzuolo, F. Meggio, J. A. Martínez-Casasnovas, T. Yezekyan, L. Sartori
Monitoring grapevine canopy size and evolution during time is of great interest for the management of the vineyard. An interesting and cost effective solution for 3D characterization is provided by the Kinect sensor. To assess its practical applicability, field experiments were carried out on two different grapevines varieties (Glera and Merlot) for a three months period. The results from 3D digital imaging were compared with those achieved by direct hand-made measurements. Estimated volume was then effectively correlated to the number of leaves and to the leaf area index. The experiments demonstrated how a low cost 3D sensor can be applied for fast and repeatable reconstruction of vine vegetation, opening up for new potential improvements in variable rate application or pruning
{"title":"Application of the Kinect sensor for three dimensional characterization of vine canopy","authors":"F. Marinello, A. Pezzuolo, F. Meggio, J. A. Martínez-Casasnovas, T. Yezekyan, L. Sartori","doi":"10.1017/S2040470017001042","DOIUrl":"https://doi.org/10.1017/S2040470017001042","url":null,"abstract":"Monitoring grapevine canopy size and evolution during time is of great interest for the management of the vineyard. An interesting and cost effective solution for 3D characterization is provided by the Kinect sensor. To assess its practical applicability, field experiments were carried out on two different grapevines varieties (Glera and Merlot) for a three months period. The results from 3D digital imaging were compared with those achieved by direct hand-made measurements. Estimated volume was then effectively correlated to the number of leaves and to the leaf area index. The experiments demonstrated how a low cost 3D sensor can be applied for fast and repeatable reconstruction of vine vegetation, opening up for new potential improvements in variable rate application or pruning","PeriodicalId":7228,"journal":{"name":"Advances in Animal Biosciences","volume":"28 1","pages":"525-529"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77754344","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-01DOI: 10.1017/S2040470017000814
Jorge Martínez-Guanter, M. Garrido-Izard, J. Agüera, C. Valero, M. Pérez-Ruiz
New Super-High-Density (SHD) olive orchards designed for mechanical harvesting are increasing very rapidly in Spain. Most studies have focused in effectively removing the olive fruit, however the machine needs to put significant amount of energy on the canopy that could result in structural damage or extra stress on the trees. During harvest, a series of 3-axis accelerometers were installed on the tree structure in order to register vibration patterns. A LiDAR (Light Detection and Ranging) and a camera sensing device were also mounted on a tractor. Before and after harvest measurements showed significant differences in the LiDAR and image data. A fast estimate of the damage produced by an over-the-row harvester with contactless sensing could be useful information for adjusting the machine parameters in each olive grove automatically in the future.
{"title":"Over-the-row harvester damage evaluation in super-high-density olive orchard by on-board sensing techniques","authors":"Jorge Martínez-Guanter, M. Garrido-Izard, J. Agüera, C. Valero, M. Pérez-Ruiz","doi":"10.1017/S2040470017000814","DOIUrl":"https://doi.org/10.1017/S2040470017000814","url":null,"abstract":"New Super-High-Density (SHD) olive orchards designed for mechanical harvesting are increasing very rapidly in Spain. Most studies have focused in effectively removing the olive fruit, however the machine needs to put significant amount of energy on the canopy that could result in structural damage or extra stress on the trees. During harvest, a series of 3-axis accelerometers were installed on the tree structure in order to register vibration patterns. A LiDAR (Light Detection and Ranging) and a camera sensing device were also mounted on a tractor. Before and after harvest measurements showed significant differences in the LiDAR and image data. A fast estimate of the damage produced by an over-the-row harvester with contactless sensing could be useful information for adjusting the machine parameters in each olive grove automatically in the future.","PeriodicalId":7228,"journal":{"name":"Advances in Animal Biosciences","volume":"22 1","pages":"487-491"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81627895","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-01DOI: 10.1017/S2040470017000127
L. Urso, J. Wegener, D. Hörsten, Till-Fabian Minßen, Cord-Christian Gaus
A view into future requires maybe a new approach of current arable production systems. So far crop production systems have been adapted to the machinery available on the market. New technological progresses in the range of precision and digital farming show the possibilities meeting the requirements of plants even more small-spatial and efficiently. Especially small autonomous machinery may offer opportunities achieving sustainable intensification in crop farming. Different crop productions and cultivation systems have to be devised, analyzed and assessed in this context.
{"title":"Crop Production of the future – possible with a new approach?","authors":"L. Urso, J. Wegener, D. Hörsten, Till-Fabian Minßen, Cord-Christian Gaus","doi":"10.1017/S2040470017000127","DOIUrl":"https://doi.org/10.1017/S2040470017000127","url":null,"abstract":"A view into future requires maybe a new approach of current arable production systems. So far crop production systems have been adapted to the machinery available on the market. New technological progresses in the range of precision and digital farming show the possibilities meeting the requirements of plants even more small-spatial and efficiently. Especially small autonomous machinery may offer opportunities achieving sustainable intensification in crop farming. Different crop productions and cultivation systems have to be devised, analyzed and assessed in this context.","PeriodicalId":7228,"journal":{"name":"Advances in Animal Biosciences","volume":"19 1","pages":"734-737"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84484350","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-01DOI: 10.1017/S2040470017000723
A. Linz, D. Brunner, J. Fehrmann, T. Herlitzius, R. Keicher, A. Ruckelshausen, H. Schwarz
Precise applying of PPP (Plant Protection Products) in orchards and vineyards requires new kinds of sprayer technologies and new methods of sensor data evaluation. In this paper a selective electrical driven sprayer, carried by the autonomous robotic platform elWObot, is introduced. A 3D-Simulation environment and the framework ROS (Robot Operating System) helps developing and testing the interaction between the sprayer and the robot. The calculated leaf wall area (LWA) and the distance from the sprayer to the leaves in the spray region, control the flow-rate and the air-assist of eight adjustable sprayers individually. First field trials showed that the adaption of the software from the simulation to the hardware worked as expected.
{"title":"Modelling environment for an electrical driven selective sprayer robot in orchards","authors":"A. Linz, D. Brunner, J. Fehrmann, T. Herlitzius, R. Keicher, A. Ruckelshausen, H. Schwarz","doi":"10.1017/S2040470017000723","DOIUrl":"https://doi.org/10.1017/S2040470017000723","url":null,"abstract":"Precise applying of PPP (Plant Protection Products) in orchards and vineyards requires new kinds of sprayer technologies and new methods of sensor data evaluation. In this paper a selective electrical driven sprayer, carried by the autonomous robotic platform elWObot, is introduced. A 3D-Simulation environment and the framework ROS (Robot Operating System) helps developing and testing the interaction between the sprayer and the robot. The calculated leaf wall area (LWA) and the distance from the sprayer to the leaves in the spray region, control the flow-rate and the air-assist of eight adjustable sprayers individually. First field trials showed that the adaption of the software from the simulation to the hardware worked as expected.","PeriodicalId":7228,"journal":{"name":"Advances in Animal Biosciences","volume":"58 1","pages":"848-853"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85726787","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2017-07-01DOI: 10.1017/S2040470017000371
E. Morimoto, K. Hayashi
This paper presents schematic review for the smart agricultural model in Japan using data-on-demand information exchange based on smart agricultural machinery systems (SAMS). Four machines were developed in this study, namely Smart rice trans-planter with on-the-go soil sensor; Smart 2nd fertilizer applicator based on CropSpec TM ; Yield monitor combine harvester with on-the-go lodging analysis system; and Farm Activity Record Management System (FARMS). The study obtained 450,000 datasets of topsoil accompanied by 65,000 datasets of crop status and 1 million images of lodging information from 50 ha of rice fields, taken in 2016. The results conclude that the field mapping using FARMS was available not only for manager’s decision on fertilizer application, but also for information sharing between employees. A two year feasibility study showed improvement of 20% fertilizer reduction and 30% harvest efficiency than conventional management. The study suggests that SAMS would play an important role for technology succession in the near future.
{"title":"Design of Smart Agriculture Japan Model","authors":"E. Morimoto, K. Hayashi","doi":"10.1017/S2040470017000371","DOIUrl":"https://doi.org/10.1017/S2040470017000371","url":null,"abstract":"This paper presents schematic review for the smart agricultural model in Japan using data-on-demand information exchange based on smart agricultural machinery systems (SAMS). Four machines were developed in this study, namely Smart rice trans-planter with on-the-go soil sensor; Smart 2nd fertilizer applicator based on CropSpec TM ; Yield monitor combine harvester with on-the-go lodging analysis system; and Farm Activity Record Management System (FARMS). The study obtained 450,000 datasets of topsoil accompanied by 65,000 datasets of crop status and 1 million images of lodging information from 50 ha of rice fields, taken in 2016. The results conclude that the field mapping using FARMS was available not only for manager’s decision on fertilizer application, but also for information sharing between employees. A two year feasibility study showed improvement of 20% fertilizer reduction and 30% harvest efficiency than conventional management. The study suggests that SAMS would play an important role for technology succession in the near future.","PeriodicalId":7228,"journal":{"name":"Advances in Animal Biosciences","volume":"14 1","pages":"713-717"},"PeriodicalIF":0.0,"publicationDate":"2017-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90495436","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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