Development of a Wireless Computer Vision Instrument to Detect Biotic Stress in Wheat
Knowledge of crop abiotic and biotic stress is important for optimal irrigation management. While spectral reflectance and infrared thermometry provide a means to quantify crop stress remotely, these measurements can be cumbersome. Computer vision offers an inexpensive way to remotely detect crop st...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2014-09-01
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| Series: | Sensors |
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| Online Access: | http://www.mdpi.com/1424-8220/14/9/17753 |
| _version_ | 1798005483194286080 |
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| author | Joaquin J. Casanova Susan A. O'Shaughnessy Steven R. Evett Charles M. Rush |
| author_facet | Joaquin J. Casanova Susan A. O'Shaughnessy Steven R. Evett Charles M. Rush |
| author_sort | Joaquin J. Casanova |
| collection | DOAJ |
| description | Knowledge of crop abiotic and biotic stress is important for optimal irrigation management. While spectral reflectance and infrared thermometry provide a means to quantify crop stress remotely, these measurements can be cumbersome. Computer vision offers an inexpensive way to remotely detect crop stress independent of vegetation cover. This paper presents a technique using computer vision to detect disease stress in wheat. Digital images of differentially stressed wheat were segmented into soil and vegetation pixels using expectation maximization (EM). In the first season, the algorithm to segment vegetation from soil and distinguish between healthy and stressed wheat was developed and tested using digital images taken in the field and later processed on a desktop computer. In the second season, a wireless camera with near real-time computer vision capabilities was tested in conjunction with the conventional camera and desktop computer. For wheat irrigated at different levels and inoculated with wheat streak mosaic virus (WSMV), vegetation hue determined by the EM algorithm showed significant effects from irrigation level and infection. Unstressed wheat had a higher hue (118.32) than stressed wheat (111.34). In the second season, the hue and cover measured by the wireless computer vision sensor showed significant effects from infection (p = 0.0014), as did the conventional camera (p < 0.0001). Vegetation hue obtained through a wireless computer vision system in this study is a viable option for determining biotic crop stress in irrigation scheduling. Such a low-cost system could be suitable for use in the field in automated irrigation scheduling applications. |
| first_indexed | 2024-04-11T12:40:18Z |
| format | Article |
| id | doaj.art-7fe25e58ab014f51a119ddedab4abce0 |
| institution | Directory Open Access Journal |
| issn | 1424-8220 |
| language | English |
| last_indexed | 2024-04-11T12:40:18Z |
| publishDate | 2014-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Sensors |
| spelling | doaj.art-7fe25e58ab014f51a119ddedab4abce02022-12-22T04:23:31ZengMDPI AGSensors1424-82202014-09-01149177531776910.3390/s140917753s140917753Development of a Wireless Computer Vision Instrument to Detect Biotic Stress in WheatJoaquin J. Casanova0Susan A. O'Shaughnessy1Steven R. Evett2Charles M. Rush3University of Florida, Gainesville, FL 32611, USAUSDA-ARS, P.O. Drawer 10, Bushland, TX 79012, USAUSDA-ARS, P.O. Drawer 10, Bushland, TX 79012, USATexas A&M AgriLife Research & Extension, Amarillo Blvd., Amarillo, TX 79109, USAKnowledge of crop abiotic and biotic stress is important for optimal irrigation management. While spectral reflectance and infrared thermometry provide a means to quantify crop stress remotely, these measurements can be cumbersome. Computer vision offers an inexpensive way to remotely detect crop stress independent of vegetation cover. This paper presents a technique using computer vision to detect disease stress in wheat. Digital images of differentially stressed wheat were segmented into soil and vegetation pixels using expectation maximization (EM). In the first season, the algorithm to segment vegetation from soil and distinguish between healthy and stressed wheat was developed and tested using digital images taken in the field and later processed on a desktop computer. In the second season, a wireless camera with near real-time computer vision capabilities was tested in conjunction with the conventional camera and desktop computer. For wheat irrigated at different levels and inoculated with wheat streak mosaic virus (WSMV), vegetation hue determined by the EM algorithm showed significant effects from irrigation level and infection. Unstressed wheat had a higher hue (118.32) than stressed wheat (111.34). In the second season, the hue and cover measured by the wireless computer vision sensor showed significant effects from infection (p = 0.0014), as did the conventional camera (p < 0.0001). Vegetation hue obtained through a wireless computer vision system in this study is a viable option for determining biotic crop stress in irrigation scheduling. Such a low-cost system could be suitable for use in the field in automated irrigation scheduling applications.http://www.mdpi.com/1424-8220/14/9/17753crop stressimage segmentationirrigation managementmaximum expectation algorithm |
| spellingShingle | Joaquin J. Casanova Susan A. O'Shaughnessy Steven R. Evett Charles M. Rush Development of a Wireless Computer Vision Instrument to Detect Biotic Stress in Wheat Sensors crop stress image segmentation irrigation management maximum expectation algorithm |
| title | Development of a Wireless Computer Vision Instrument to Detect Biotic Stress in Wheat |
| title_full | Development of a Wireless Computer Vision Instrument to Detect Biotic Stress in Wheat |
| title_fullStr | Development of a Wireless Computer Vision Instrument to Detect Biotic Stress in Wheat |
| title_full_unstemmed | Development of a Wireless Computer Vision Instrument to Detect Biotic Stress in Wheat |
| title_short | Development of a Wireless Computer Vision Instrument to Detect Biotic Stress in Wheat |
| title_sort | development of a wireless computer vision instrument to detect biotic stress in wheat |
| topic | crop stress image segmentation irrigation management maximum expectation algorithm |
| url | http://www.mdpi.com/1424-8220/14/9/17753 |
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