title: Development of a wireless computer vision instrument to detect biotic stress in wheat.
abstract: 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.
authors.affiliation: University of Florida, Gainesville, FL 32611, USA. joaquin.casanova@gmail.com.
authors.forename: Joaquin J
authors.initials: JJ
authors.lastname: Casanova
authors.affiliation: USDA-ARS, P.O. Drawer 10, Bushland, TX 79012, USA. susan.oshaughnessy@ars.usda.gov.
authors.forename: Susan A
authors.initials: SA
authors.lastname: O'Shaughnessy
authors.affiliation: USDA-ARS, P.O. Drawer 10, Bushland, TX 79012, USA. steve.evett@ars.usda.gov.
authors.forename: Steven R
authors.initials: SR
authors.lastname: Evett
authors.affiliation: Texas A&M AgriLife Research & Extension, Amarillo Blvd., Amarillo, TX 79109, USA. crush@ag.tamu.edu.
authors.forename: Charles M
authors.initials: CM
authors.lastname: Rush
mesh_terms: D000383:Agriculture / Q000295:instrumentation; D000465:Algorithms; D001185:Artificial Intelligence; D058448:Biota / Q000502:physiology; D018556:Crops, Agricultural / Q000033:anatomy & histology / Q000502:physiology; D004867:Equipment Design; D019544:Equipment Failure Analysis; D007090:Image Interpretation, Computer-Assisted / Q000295:instrumentation* / Q000379:methods; D010363:Pattern Recognition, Automated / Q000379:methods; D010781:Photography / Q000295:instrumentation*; D013312:Stress, Physiological / Q000502:physiology*; D014908:Triticum / Q000033:anatomy & histology* / Q000502:physiology*; D059015:Wireless Technology / Q000295:instrumentation*