Image processing algorithms for a selective vineyard robotic sprayer

R. Berenstein, O. Ben Shahar, A. Shapiro, A. Bechar, Y. Edan

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

This paper presents image processing algorithms for a selective robotic sprayer in vineyards. Two types of machine vision algorithms were developed to directly spray grape clusters and foliage. The first algorithm is based on the difference in the distribution of edges between the foliage and the grape clusters. The second detection algorithm uses a decision tree algorithm for separating the grape clusters from the background based on a training dataset from 100 images. Both image processing algorithms were tested on data from movies acquired in vineyards during the growing season of 2008. Results indicate high reliability of both foliage detection and grape clusters detection. Preliminary results show 90% percent accuracy of grape clusters detection, leading to 30% reduction in the use of pesticides.

Original languageEnglish
Title of host publicationPrecision Agriculture 2009 - Papers Presented at the 7th European Conference on Precision Agriculture, ECPA 2009
Pages749-757
Number of pages9
StatePublished - 1 Dec 2009
Event7th European Conference on Precision Agriculture, ECPA 2009 - Wageningen, Netherlands
Duration: 6 Jul 20098 Jul 2009

Publication series

NamePrecision Agriculture 2009 - Papers Presented at the 7th European Conference on Precision Agriculture, ECPA 2009

Conference

Conference7th European Conference on Precision Agriculture, ECPA 2009
Country/TerritoryNetherlands
CityWageningen
Period6/07/098/07/09

Keywords

  • Decision tree
  • Edge detection
  • Machine learning
  • Precision agriculture

ASJC Scopus subject areas

  • Agronomy and Crop Science

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