Open AccessDisease detection on plant leaf using K-means segmentation with fuzzy logic SVM algorithm
Author(s) -
M. Sowmya,
Bojan Subramani
Publication year - 2022
Publication title -
international journal of health sciences (ijhs) (en línea)
Language(s) - English
Resource type - Journals
eISSN - 2550-6978
pISSN - 2550-696X
DOI - 10.53730/ijhs.v6ns2.6127
Subject(s) - cercospora , support vector machine , fuzzy logic , leaf spot , segmentation , alternaria , stage (stratigraphy) , plant disease , pattern recognition (psychology) , artificial intelligence , image segmentation , computer science , algorithm , biology , botany , microbiology and biotechnology , paleontology
Detection of plant diseases requisite at its early stage to manage the large crop field. In plants existence of diseases result reduced yields of crops and therefore it is imperative to identify at its early stage. Leaf is the main part where the diseases symptoms are shown in the initial stage itself. Image processing techniques are used at the computing part whereas in this research a hybrid KMFLs (K-Means Fuzzy logics) and SVMs (Support Vector Machines) are implemented to identify and categorize diseased plants based on leaf disease grades. This work’s proposed method is implemented by examining images of leaves for diseases including Alternaria alternates, Anthracnoses, Bacterial blights and Cercospora leaf spots. The input leaf image features are extracted which are subsequently used for categorizations into classes.