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Open AccessArticle

Development of a Wireless Computer Vision Instrument to Detect Biotic Stress in Wheat

University of Florida, Gainesville, FL 32611, USA
USDA-ARS, P.O. Drawer 10, Bushland, TX 79012, USA
Texas A&M AgriLife Research & Extension, Amarillo Blvd., Amarillo, TX 79109, USA
Author to whom correspondence should be addressed.
Sensors 2014, 14(9), 17753-17769;
Received: 21 June 2014 / Revised: 9 September 2014 / Accepted: 15 September 2014 / Published: 23 September 2014
(This article belongs to the Special Issue Agriculture and Forestry: Sensors, Technologies and Procedures)
PDF [1517 KB, uploaded 24 September 2014]


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. View Full-Text
Keywords: crop stress; image segmentation; irrigation management; maximum expectation algorithm crop stress; image segmentation; irrigation management; maximum expectation algorithm
This is an open access article distributed under the Creative Commons Attribution License (CC BY 3.0).

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MDPI and ACS Style

Casanova, J.J.; O'Shaughnessy, S.A.; Evett, S.R.; Rush, C.M. Development of a Wireless Computer Vision Instrument to Detect Biotic Stress in Wheat. Sensors 2014, 14, 17753-17769.

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