Plant Leaf Disease Detection using Machine Learning

Abstract

Abstract: A crucial component of describing plants for tracking plant growth is plant phenotyping. In this research, an effective method for identifying healthy, damaged, or infected leaves utilising image processing and machine learning approaches is presented. Many illnesses deplete the chlorophyll of brown or black markings appear on the leaf area of the leaves. They can be found out utilising machine learning methods for classification, feature extraction, picture preprocessing, and image segmentation. Grey Level Co-occurrence Matrix (GLCM) is used for feature extraction. One of themachine learning techniques used for classification is called the Support Vector Machine (SVM). When compared to the SVM method, the Convolutional Neural Network (CNN) produced better recognition accuracy. Finding disease on crops is a crucial responsibility in agricultural techniques.

Code and data availability

The paper describes leaf disease detection using CNN/SVM with image processing, but provides no public dataset, image collection, code, model checkpoint, or supplement with availability language or URLs. No paper-specific reproducible asset is identified.

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