The Experts below are selected from a list of 288 Experts worldwide ranked by ideXlab platform
E. J. Henten - One of the best experts on this subject based on the ideXlab platform.
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Adaptive detection of Volunteer potato Plants in sugar beet fields
Precision Agriculture, 2010Co-Authors: A. T. Nieuwenhuizen, J. W. Hofstee, E. J. HentenAbstract:Volunteer potato is an increasing problem in crop rotations where winter temperatures are often not cold enough to kill tubers leftover from harvest. Poor control, as a result of high labor demands, causes diseases like Phytophthora infestans to spread to neighboring fields. Therefore, automatic detection and removal of Volunteer Plants is required. In this research, an adaptive Bayesian classification method has been developed for classification of Volunteer potato Plants within a sugar beet crop. With use of ground truth images, the classification accuracy of the Plants was determined. In the non-adaptive scheme, the classification accuracy was 84.6 and 34.9% for the constant and changing natural light conditions, respectively. In the adaptive scheme, the classification accuracy increased to 89.8 and 67.7% for the constant and changing natural light conditions, respectively. Crop row information was successfully used to train the adaptive classifier, without having to choose training data in advance.
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Colour based detection of Volunteer potatoes as weeds in sugar beet fields using machine vision
Precision Agriculture, 2007Co-Authors: A. T. Nieuwenhuizen, L. Tang, J. W. Hofstee, J. Müller, E. J. HentenAbstract:The possible spread of late blight from Volunteer potato Plants requires the removal of these Plants from arable fields. Because of high labour, energy, and chemical demands, a method of automatic detection and removal is needed. The development and comparison of two colour-based machine vision algorithms for in-field Volunteer potato plant detection in two sugar beet fields are discussed. Evaluation of the results showed that both methods gave closely matched results within fields, although large differences exist between the fields. At plant level, in one field up to 97% of the Volunteer potato Plants were correctly classified. In another field, only 49% of the Volunteer Plants were correctly identified. The differences between the fields were higher than the differences between the methods used for plant classification.
A. T. Nieuwenhuizen - One of the best experts on this subject based on the ideXlab platform.
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Adaptive detection of Volunteer potato Plants in sugar beet fields
Precision Agriculture, 2010Co-Authors: A. T. Nieuwenhuizen, J. W. Hofstee, E. J. HentenAbstract:Volunteer potato is an increasing problem in crop rotations where winter temperatures are often not cold enough to kill tubers leftover from harvest. Poor control, as a result of high labor demands, causes diseases like Phytophthora infestans to spread to neighboring fields. Therefore, automatic detection and removal of Volunteer Plants is required. In this research, an adaptive Bayesian classification method has been developed for classification of Volunteer potato Plants within a sugar beet crop. With use of ground truth images, the classification accuracy of the Plants was determined. In the non-adaptive scheme, the classification accuracy was 84.6 and 34.9% for the constant and changing natural light conditions, respectively. In the adaptive scheme, the classification accuracy increased to 89.8 and 67.7% for the constant and changing natural light conditions, respectively. Crop row information was successfully used to train the adaptive classifier, without having to choose training data in advance.
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Performance evaluation of an automated detection and control system for Volunteer potatoes in sugar beet fields
Biosystems Engineering, 2010Co-Authors: A. T. Nieuwenhuizen, J. W. Hofstee, E.j. Van HentenAbstract:Incomplete control of Volunteer potato Plants causes a high environmental load through increased crop protection chemical usage in potato cropping. A joint effort of industry, policy makers and science initiated a four year scientific project on detection and control of Volunteer potato Plants. A proof-of-principle machine for automated detection and control of Volunteer potato Plants in sugar beet fields has been tested in experimental fields. Machine vision-based detection at 100 mm2 precision is combined with a micro-sprayer with five needles and a working width of 0.2 m. The accuracy of the system was ±14 mm in longitudinal direction and ±7.5 mm in transverse direction. The main error source was the variability in micro-sprayer droplet velocity that caused longitudinal errors. However, 77% of Volunteer Plants with a size larger than 1200 mm2 were successfully controlled at machine speeds up to 0.8 m s−1. Within the crop row, glyphosate was applied on weed potato Plants and this resulted in the unwanted death of up to 1.0% of sugar beet Plants.
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Colour based detection of Volunteer potatoes as weeds in sugar beet fields using machine vision
Precision Agriculture, 2007Co-Authors: A. T. Nieuwenhuizen, L. Tang, J. W. Hofstee, J. Müller, E. J. HentenAbstract:The possible spread of late blight from Volunteer potato Plants requires the removal of these Plants from arable fields. Because of high labour, energy, and chemical demands, a method of automatic detection and removal is needed. The development and comparison of two colour-based machine vision algorithms for in-field Volunteer potato plant detection in two sugar beet fields are discussed. Evaluation of the results showed that both methods gave closely matched results within fields, although large differences exist between the fields. At plant level, in one field up to 97% of the Volunteer potato Plants were correctly classified. In another field, only 49% of the Volunteer Plants were correctly identified. The differences between the fields were higher than the differences between the methods used for plant classification.
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Color-Based In-Field Volunteer Potato Detection Using A Bayesian Classifier And An Adaptive Neural Network
2005 Tampa FL July 17-20 2005, 2005Co-Authors: A. T. Nieuwenhuizen, L. Tang, J. W. Hofstee, J.h.w. Van Den Oever, J. MuellerAbstract:The possible spread of late blight from Volunteer potato Plants requires that these Plants being removed from fields. However, because of high labour, energy and chemical inputs associated with this removal process, an automatic detection and removal system becomes necessary. In this paper, the development and comparison of two colour-based machine vision algorithms for in-field Volunteer potato Plants detection in sugar beet fields were reported. When classification accuracy was evaluated at plant level, an Adaptive Neural Network classifier and a joint classifier of K-means clustering and Bayes classification produced closely matched results. Specifically, from 192 top view images, 92% of Volunteer potato Plants were correctly detected both methods. There were 4% sugar beet Plants being wrongly identified as Volunteer potato Plants, which was largely caused by occlusions of leaves. At pixel level, K-means/Bayes classifier gave slightly better results on both top view and slant view images. Although K-means/Bayes with a static lookup table gave slightly better results, an adaptive neural network could be more suitable for the changing conditions in the fields. Especially for the case of using an outdoor autonomous robot for Volunteer Plants removal, adaptive methods possesses a greater potential.
Carola Pekrun - One of the best experts on this subject based on the ideXlab platform.
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Modelling seedbank dynamics of Volunteer oilseed rape (Brassica napus)
Agricultural Systems, 2005Co-Authors: Carola Pekrun, Peter W. Lane, P. J. W. LutmanAbstract:A simple mechanistic model is presented which describes population dynamics of Volunteer oilseed rape within a field. The model calculates the number of Volunteers appearing in each crop and the seedbank after each crop. The main input variables are harvesting losses when the crop is oilseed rape, crop rotation, soil cultivation, soil moisture content within the arable soil layer and the level of Volunteer control in each crop in the rotation. Simulation studies suggest that there are a number of agronomic means of minimising Volunteer oilseed rape populations effectively. The amount of harvesting losses, the time span between oilseed rape harvest and the first tillage operation post-harvest, the efficiency of controlling oilseed rape in other crops and rotation itself are key components of a programme for ensuring that Volunteer oilseed rape populations are minimised. Simulation runs showed that the proportion of Volunteer Plants within a crop of oilseed rape will be relatively high, even though the density of Volunteers is low in other crops. This contamination of a rape crop could be a particular problem in the context of the cultivation of genetically modified rape. The model would benefit from improved estimates of some parameters. More data are particularly necessary on the long-term development of a seedbank of oilseed rape and the relationship between the size of the seedbank and the number of Volunteers in various crops.
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Population dynamics of Volunteer oilseed rape (Brassica napus L.) affected by tillage
European Journal of Agronomy, 2004Co-Authors: Sabine Gruber, Carola Pekrun, Wilhelm ClaupeinAbstract:Volunteer Plants of oilseed rape (Brassica napus L.) from persistent seeds in soil can affect subsequent crops. Apart from the agricultural disadvantages, the environment and the marketing of the seeds may also be affected, particularly if Plants with special ingredients or genetically modified (gm) Plants are grown. In order to investigate the influence of soil cultivation and genotype on seed persistence and gene flow via Volunteers, a field experiment was set up testing four tillage treatments and two cultivars in a split-plot design. The cultivars tested were near-isogenic to two gm cultivars. To simulate harvesting losses, 10 000 seeds m −2 were broadcast on a soil in July. The subsequent tillage treatments were combinations of immediate or delayed stubble tillage by a rotary tiller, primary tillage with plough or cultivator, or zero tillage. Over the following year, the fate of the seeds was determined. Immediate stubble tillage with following cultivator or plough resulted in 586 resp. 246 seeds m −2 in the soil seed bank. After delayed stubble tillage with following plough, 76 seeds m −2 were found, and no soil seed bank was built up in the zero tillage treatment. Nevertheless, in the zero tillage treatment, several robust Volunteer Plants survived the herbicide application before the direct drilling in autumn until following spring. In the zero tillage treatment and in the cultivator treatment, 0.19 Volunteers m −2 resp. 0.06 Volunteers m −2 flowered simultaneously to ordinarily sown oilseed rape in the following crop of winter wheat and produced 73 resp. 18 seeds m −2 . Delayed stubble tillage reduced the risk of gene escape via the soil seed bank, while zero tillage resulted in the highest risk of gene escape by pollen and by production of a new generation of seeds. In terms of a labelling threshold for gm food this number of seeds would be below the threshold of 0.9% of transgenic parts in conventially bred food or feed. © 2003 Elsevier B.V. All rights reserved.
J. W. Hofstee - One of the best experts on this subject based on the ideXlab platform.
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Adaptive detection of Volunteer potato Plants in sugar beet fields
Precision Agriculture, 2010Co-Authors: A. T. Nieuwenhuizen, J. W. Hofstee, E. J. HentenAbstract:Volunteer potato is an increasing problem in crop rotations where winter temperatures are often not cold enough to kill tubers leftover from harvest. Poor control, as a result of high labor demands, causes diseases like Phytophthora infestans to spread to neighboring fields. Therefore, automatic detection and removal of Volunteer Plants is required. In this research, an adaptive Bayesian classification method has been developed for classification of Volunteer potato Plants within a sugar beet crop. With use of ground truth images, the classification accuracy of the Plants was determined. In the non-adaptive scheme, the classification accuracy was 84.6 and 34.9% for the constant and changing natural light conditions, respectively. In the adaptive scheme, the classification accuracy increased to 89.8 and 67.7% for the constant and changing natural light conditions, respectively. Crop row information was successfully used to train the adaptive classifier, without having to choose training data in advance.
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Performance evaluation of an automated detection and control system for Volunteer potatoes in sugar beet fields
Biosystems Engineering, 2010Co-Authors: A. T. Nieuwenhuizen, J. W. Hofstee, E.j. Van HentenAbstract:Incomplete control of Volunteer potato Plants causes a high environmental load through increased crop protection chemical usage in potato cropping. A joint effort of industry, policy makers and science initiated a four year scientific project on detection and control of Volunteer potato Plants. A proof-of-principle machine for automated detection and control of Volunteer potato Plants in sugar beet fields has been tested in experimental fields. Machine vision-based detection at 100 mm2 precision is combined with a micro-sprayer with five needles and a working width of 0.2 m. The accuracy of the system was ±14 mm in longitudinal direction and ±7.5 mm in transverse direction. The main error source was the variability in micro-sprayer droplet velocity that caused longitudinal errors. However, 77% of Volunteer Plants with a size larger than 1200 mm2 were successfully controlled at machine speeds up to 0.8 m s−1. Within the crop row, glyphosate was applied on weed potato Plants and this resulted in the unwanted death of up to 1.0% of sugar beet Plants.
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Colour based detection of Volunteer potatoes as weeds in sugar beet fields using machine vision
Precision Agriculture, 2007Co-Authors: A. T. Nieuwenhuizen, L. Tang, J. W. Hofstee, J. Müller, E. J. HentenAbstract:The possible spread of late blight from Volunteer potato Plants requires the removal of these Plants from arable fields. Because of high labour, energy, and chemical demands, a method of automatic detection and removal is needed. The development and comparison of two colour-based machine vision algorithms for in-field Volunteer potato plant detection in two sugar beet fields are discussed. Evaluation of the results showed that both methods gave closely matched results within fields, although large differences exist between the fields. At plant level, in one field up to 97% of the Volunteer potato Plants were correctly classified. In another field, only 49% of the Volunteer Plants were correctly identified. The differences between the fields were higher than the differences between the methods used for plant classification.
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Color-Based In-Field Volunteer Potato Detection Using A Bayesian Classifier And An Adaptive Neural Network
2005 Tampa FL July 17-20 2005, 2005Co-Authors: A. T. Nieuwenhuizen, L. Tang, J. W. Hofstee, J.h.w. Van Den Oever, J. MuellerAbstract:The possible spread of late blight from Volunteer potato Plants requires that these Plants being removed from fields. However, because of high labour, energy and chemical inputs associated with this removal process, an automatic detection and removal system becomes necessary. In this paper, the development and comparison of two colour-based machine vision algorithms for in-field Volunteer potato Plants detection in sugar beet fields were reported. When classification accuracy was evaluated at plant level, an Adaptive Neural Network classifier and a joint classifier of K-means clustering and Bayes classification produced closely matched results. Specifically, from 192 top view images, 92% of Volunteer potato Plants were correctly detected both methods. There were 4% sugar beet Plants being wrongly identified as Volunteer potato Plants, which was largely caused by occlusions of leaves. At pixel level, K-means/Bayes classifier gave slightly better results on both top view and slant view images. Although K-means/Bayes with a static lookup table gave slightly better results, an adaptive neural network could be more suitable for the changing conditions in the fields. Especially for the case of using an outdoor autonomous robot for Volunteer Plants removal, adaptive methods possesses a greater potential.
P. J. W. Lutman - One of the best experts on this subject based on the ideXlab platform.
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Modelling seedbank dynamics of Volunteer oilseed rape (Brassica napus)
Agricultural Systems, 2005Co-Authors: Carola Pekrun, Peter W. Lane, P. J. W. LutmanAbstract:A simple mechanistic model is presented which describes population dynamics of Volunteer oilseed rape within a field. The model calculates the number of Volunteers appearing in each crop and the seedbank after each crop. The main input variables are harvesting losses when the crop is oilseed rape, crop rotation, soil cultivation, soil moisture content within the arable soil layer and the level of Volunteer control in each crop in the rotation. Simulation studies suggest that there are a number of agronomic means of minimising Volunteer oilseed rape populations effectively. The amount of harvesting losses, the time span between oilseed rape harvest and the first tillage operation post-harvest, the efficiency of controlling oilseed rape in other crops and rotation itself are key components of a programme for ensuring that Volunteer oilseed rape populations are minimised. Simulation runs showed that the proportion of Volunteer Plants within a crop of oilseed rape will be relatively high, even though the density of Volunteers is low in other crops. This contamination of a rape crop could be a particular problem in the context of the cultivation of genetically modified rape. The model would benefit from improved estimates of some parameters. More data are particularly necessary on the long-term development of a seedbank of oilseed rape and the relationship between the size of the seedbank and the number of Volunteers in various crops.
