NDLI: Semi-automatic leaf disease detection and classification system for soybean culture

Content Provider	IET Digital Library
Author	Kaur, Sukhvir Pandey, Shreelekha Goel, Shivani
Abstract	Development of automatic disease detection and classification system is significantly explored in precision agriculture. In the past few decades, researchers have studied several cultures exploiting different parts of a plant. A similar study is performed for Soybean using leaf images. A rule based semi-automatic system using concepts of k-means is designed and implemented to distinguish healthy leaves from diseased leaves. In addition, a diseased leaf is classified into one of the three categories (downy mildew, frog eye, and Septoria leaf blight). Experiments are performed by separately utilising colour features, texture features, and their combinations to train three models based on support vector machine classifier. Results are generated using thousands of images collected from PlantVillage dataset. Acceptable average accuracy values are reported for all the considered combinations which are also found to be better than existing ones. This study also attempts to discover the best performing feature set for leaf disease detection in Soybean. The system is shown to efficiently compute the disease severity as well. Visual examination of leaf samples further proves the suitability of the proposed system for detection, classification, and severity calculation.
Starting Page	1038
Ending Page	1048
Page Count	11
ISSN	17519659
Volume Number	12
e-ISSN	17519667
Issue Number	Issue 6, Jun (2018)
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/iet-ipr/12/6
Alternate Webpage(s)	https://digital-library.theiet.org/content/journals/10.1049/iet-ipr.2017.0822
Journal	IET Image Processing
Publisher Date	2018-02-01
Access Restriction	Open
Rights Holder	© The Institution of Engineering and Technology
Subject Keyword	Agricultural Engineering Agriculture Automatic Disease Detection And Classification System Colour Feature Computer Vision And Image Processing Technique Feature Extraction Feature Set Fisheries Computing Food Product Forestry Image Classification Image Colour Analysis Image Recognition Image Texture Industrial Application of IT K-means Concepts Knowledge Engineering Technique Leaf Image Plant Diseases PlantVillage Dataset Precision Agriculture Product And Commodities Semiautomatic Leaf Disease Detection And Classification System Soybean Culture Support Vector Machine Support Vector Machine Classifier Texture Feature
Content Type	Text
Resource Type	Article
Subject	Signal Processing Electrical and Electronic Engineering Computer Vision and Pattern Recognition Software

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