Agricultural Crop Monitoring with Computer Vision
Precision agriculture allows farmers to efficiently use their resources with site-specific applications. The current work looks to computer vision for the data collection method necessary for such a smart field, including cameras sensitive to visual (430-650~nm), near infrared (NIR,750-900~nm), shor...
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Virginia Tech
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ndltd-VTETD-oai-vtechworks.lib.vt.edu-10919-525632020-09-29T05:46:43Z Agricultural Crop Monitoring with Computer Vision Burns, James Ian Mechanical Engineering Wicks, Alfred L. Bird, John P. Woolsey, Craig A. Computer Vision Machine Learning Agriculture Multispectral Automation Precision agriculture allows farmers to efficiently use their resources with site-specific applications. The current work looks to computer vision for the data collection method necessary for such a smart field, including cameras sensitive to visual (430-650~nm), near infrared (NIR,750-900~nm), shortwave infrared (SWIR,950-1700~nm), and longwave infrared (LWIR,7500-16000~nm) light. Three areas are considered in the study: image segmentation, multispectral image registration, and the feature tracking of a stressed plant. The accuracy of several image segmentation methods are compared. Basic thresholding on pixel intensities and vegetation indices result in accuracies below 75% . Neural networks (NNs) and support vector machines (SVMs) label correctly at 89% and 79%, respectively, when given only visual information, and final accuracies of 97% when the near infrared is added. The point matching methods of Scale Invariant Feature Transform (SIFT) and Edge Orient Histogram (EOH) are compared for accuracy. EOH improves the matching accuracy, but ultimately not enough for the current work. In order to track the image features of a stressed plant, a set of basil and catmint seedlings are grown and placed under drought and hypoxia conditions. Trends are shown in the average pixel values over the lives of the plants and with the vegetation indices, especially that of Marchant and NIR. Lastly, trends are seen in the image textures of the plants through use of textons. Master of Science 2015-05-23T08:04:40Z 2015-05-23T08:04:40Z 2014-09-25 Thesis vt_gsexam:3816 http://hdl.handle.net/10919/52563 In Copyright http://rightsstatements.org/vocab/InC/1.0/ ETD application/pdf application/pdf Virginia Tech |
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Computer Vision Machine Learning Agriculture Multispectral Automation |
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Computer Vision Machine Learning Agriculture Multispectral Automation Burns, James Ian Agricultural Crop Monitoring with Computer Vision |
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Precision agriculture allows farmers to efficiently use their resources with site-specific applications. The current work looks to computer vision for the data collection method necessary for such a smart field, including cameras sensitive to visual (430-650~nm), near infrared (NIR,750-900~nm), shortwave infrared (SWIR,950-1700~nm), and longwave infrared (LWIR,7500-16000~nm) light. Three areas are considered in the study: image segmentation, multispectral image registration, and the feature tracking of a stressed plant.
The accuracy of several image segmentation methods are compared. Basic thresholding on pixel intensities and vegetation indices result in accuracies below 75% . Neural networks (NNs) and support vector machines (SVMs) label correctly at 89% and 79%, respectively, when given only visual information, and final accuracies of 97% when the near infrared is added.
The point matching methods of Scale Invariant Feature Transform (SIFT) and Edge Orient Histogram (EOH) are compared for accuracy. EOH improves the matching accuracy, but ultimately not enough for the current work.
In order to track the image features of a stressed plant, a set of basil and catmint seedlings are grown and placed under drought and hypoxia conditions. Trends are shown in the average pixel values over the lives of the plants and with the vegetation indices, especially that of Marchant and NIR. Lastly, trends are seen in the image textures of the plants through use of textons. === Master of Science |
author2 |
Mechanical Engineering |
author_facet |
Mechanical Engineering Burns, James Ian |
author |
Burns, James Ian |
author_sort |
Burns, James Ian |
title |
Agricultural Crop Monitoring with Computer Vision |
title_short |
Agricultural Crop Monitoring with Computer Vision |
title_full |
Agricultural Crop Monitoring with Computer Vision |
title_fullStr |
Agricultural Crop Monitoring with Computer Vision |
title_full_unstemmed |
Agricultural Crop Monitoring with Computer Vision |
title_sort |
agricultural crop monitoring with computer vision |
publisher |
Virginia Tech |
publishDate |
2015 |
url |
http://hdl.handle.net/10919/52563 |
work_keys_str_mv |
AT burnsjamesian agriculturalcropmonitoringwithcomputervision |
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