Next Article in Journal
Voltammetric Electronic Tongue and Support Vector Machines for Identification of Selected Features in Mexican Coffee
Next Article in Special Issue
Estimation of the Age and Amount of Brown Rice Plant Hoppers Based on Bionic Electronic Nose Use
Previous Article in Journal
Algal Biomass Analysis by Laser-Based Analytical Techniques—A Review
Previous Article in Special Issue
Detection of Potato Storage Disease via Gas Analysis: A Pilot Study Using Field Asymmetric Ion Mobility Spectrometry
Article Menu

Export Article

Open AccessArticle
Sensors 2014, 14(9), 17753-17769; doi:10.3390/s140917753

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

1
University of Florida, Gainesville, FL 32611, USA
2
USDA-ARS, P.O. Drawer 10, Bushland, TX 79012, USA
3
Texas A&M AgriLife Research & Extension, Amarillo Blvd., Amarillo, TX 79109, USA
*
Author to whom correspondence should be addressed.
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)
View Full-Text   |   Download PDF [1517 KB, uploaded 24 September 2014]   |  

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. 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).

Scifeed alert for new publications

Never miss any articles matching your research from any publisher
  • Get alerts for new papers matching your research
  • Find out the new papers from selected authors
  • Updated daily for 49'000+ journals and 6000+ publishers
  • Define your Scifeed now

SciFeed Share & Cite This Article

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.

Show more citation formats Show less citations formats

Related Articles

Article Metrics

Article Access Statistics

1

Comments

[Return to top]
Sensors EISSN 1424-8220 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top