Development of a robotic detection system for greenhouse pepper plant diseases

Noa Schor, Sigal Berman, Aviv Dombrovsky, Yigal Elad, Timea Ignat, Avital Bechar

Research output: Contribution to journalArticlepeer-review

Abstract

Automation of disease detection and monitoring can facilitate targeted and timely disease control, which can lead to increased yield, improved crop quality and reduction in the quantity of applied pesticides. Further advantages are reduced production costs, reduced exposure to pesticides for farm workers and inspectors and increased sustainability. Symptoms are unique for each disease and crop, and each plant may suffer from multiple threats. Thus, a dedicated integrated disease-detection system and algorithms are required. The development of such a robotic detection system for two major threats of bell pepper plants: powdery mildew (PM) and Tomato spotted wilt virus (TSWV), is presented. Detection algorithms were developed based on principal component analysis using RGB and multispectral NIR-R-G sensors. High accuracy was obtained for pixel classification as diseased or healthy, for both diseases, using RGB imagery (PM: 95%, TSWV: 90%). NIR-R-G multispectral imagery yielded low classification accuracy (PM: 80%, TSWV: 61%). Accordingly, the final sensing apparatus was composed of a RGB sensor and a single-laser-beam distance sensor. A relatively fast cycle time (average 26.7 s per plant) operation cycle for detection of the two diseases was developed and tested. The cycle time was mainly influenced by sub-tasks requiring motion of the manipulator. Among these tasks, the most demanding were the determination of the required detection position and orientation. The time for task completion may be reduced by increasing the robotic work volume and by improving the algorithm for determining position and orientation.

Original languageAmerican English
Pages (from-to)394-409
Number of pages16
JournalPrecision Agriculture
Volume18
Issue number3
DOIs
StatePublished - 1 Jun 2017

Keywords

  • Disease detection
  • Multispectral imagery
  • Powdery mildew
  • RGB imagery
  • Tomato spotted wilt virus

All Science Journal Classification (ASJC) codes

  • General Agricultural and Biological Sciences

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