Detection of potato diseases using image segmentation and multiclass support vector machine

  1. Available on June 15, 2018
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Proceedings title2017 IEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE)
ConferenceIEEE 30th Canadian Conference on Electrical and Computer Engineering (CCECE), 30 April-3 May 2017, Windsor, ON, Canada
Article number16963440
SubjectSVM; disease detection; plant Phenotyping
AbstractModern phenotyping and plant disease detection provide promising step towards food security and sustainable agriculture. In particular, imaging and computer vision based phenotyping offers the ability to study quantitative plant physiology. On the contrary, manual interpretation requires tremendous amount of work, expertise in plant diseases, and also requires excessive processing time. In this work, we present an approach that integrates image processing and machine learning to allow diagnosing diseases from leaf images. This automated method classifies diseases (or absence thereof) on potato plants from a publicly available plant image database called `Plant Village'. Our segmentation approach and utilization of support vector machine demonstrate disease classification over 300 images with an accuracy of 95%. Thus, the proposed approach presents a path toward automated plant diseases diagnosis on a massive scale.
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AffiliationNational Research Council Canada; Aquatic and Crop Resource Development
Peer reviewedYes
NRC numberNRC-ACRD-56316
NPARC number23002103
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Record identifier5ec843d4-a03b-4e60-ae51-37e600fef16d
Record created2017-08-18
Record modified2017-08-22
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