Vision Based Plant Leaf Disease Detection and Recognition Model Using Machine Learning Techniques

Abstract

Plant leaf infection recognition using supervised machine learning has emerged as a promising solution to address the pressing challenges in agriculture and plant pathology. This innovative approach leverages supervised learning techniques to develop robust models capable of accurately identifying diseases and abnormalities in plant leaves based on input images. The proposed process involves several key steps. Initially, a diverse real time data's of brinjal images containing both infected and normal plant leaf is collected and meticulously labeled. The real time dataset covered healthy brijal leafs (HL), Cercospora solani(CS) diseases, Tobacco Mosaic Virus (TMV) diseases, Pythium aphanidermatum (PA) diseases, Pseudomonas solanacearum (PS) deseases and Alternaria melongenea (AM) diseases. Data pre-processing stage, such as filtering, noise removal, resizing and extraction are then evaluated to ensure consistency and enhance the dataset's diversity. Next, meaningful information are taken out from the preprocessed brinjal frames to serve as inputs for the machine learning model. Leaf Intensity Vector (LIV) + Principle Component Analysis + Gray Level Co-occurrence Matrix (GLCM) + Support Vector Machine are employed for brinjal leaf disease reorganization. Finally, the extracted proposed features are classified using Polynomial and RBF kernel of SVM, KNN, Random Forests (RF) and Decision Trees (DTs). The performance of the proposed brinjal leaf diseases classification system gives higher accuracy of SVM RBF (98.48%) on brinjalleaf disordered models.

Plant phenotyping relevance

植物葉の画像から病害状態を認識・分類する機械学習手法の開発が中心であり、植物の病害表現型を直接推定している。

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