A review of advanced techniques for detecting plant diseases

Sindhuja Sankaran, Ashish Mishra, Reza Ehsani, Cristina E Davis

Research output: Contribution to journalArticlepeer-review

556 Scopus citations

Abstract

Diseases in plants cause major production and economic losses in agricultural industry worldwide. Monitoring of health and detection of diseases in plants and trees is critical for sustainable agriculture. To the best of our knowledge, there is no sensor commercially available for real-time assessment of health conditions in trees. Currently, scouting is most widely used mechanism for monitoring stress in trees, which is an expensive, labor-intensive, and time-consuming process. Molecular techniques such as polymerase chain reaction are used for the identification of plant diseases that require detailed sampling and processing procedure. Early information on crop health and disease detection can facilitate the control of diseases through proper management strategies such as vector control through pesticide applications, fungicide applications, and disease-specific chemical applications; and can improve productivity. The present review recognizes the need for developing a rapid, cost-effective, and reliable health-monitoring sensor that would facilitate advancements in agriculture. It describes the currently used technologies that can be used for developing a ground-based sensor system to assist in monitoring health and diseases in plants under field conditions. These technologies include spectroscopic and imaging-based, and volatile profiling-based plant disease detection methods. The paper compares the benefits and limitations of these potential methods.

Original languageEnglish (US)
Pages (from-to)1-13
Number of pages13
JournalComputers and Electronics in Agriculture
Volume72
Issue number1
DOIs
StatePublished - Jun 2010

Keywords

  • GC-MS
  • Imaging techniques
  • Plant diseases
  • Spectroscopy
  • Volatile profiling

ASJC Scopus subject areas

  • Agronomy and Crop Science
  • Horticulture
  • Forestry
  • Computer Science Applications
  • Animal Science and Zoology

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