Plant Disease Detection Using Machine Learning Techniques
Author: Saish Vilas Kamble
Department: MSc Information Technology
Institute: D.G. Ruparel College of Arts, Science and Commerce
Academic Year: 2025–2026
ABSTRACT
Plant diseases are one of the major causes of reduced agricultural productivity worldwide. Early and accurate detection of plant diseases is essential to prevent crop loss and ensure food security. Traditional disease diagnosis methods depend on visual inspection by experts, which is time-consuming, expensive, and often unavailable in rural regions. This research proposes an automated Plant Disease Detection System using machine learning techniques to identify and classify plant diseases from leaf images. The system performs image preprocessing, feature extraction, and supervised classification to achieve reliable and accurate disease detection. Experimental evaluation demonstrates that the proposed approach offers high accuracy with low computational cost, making it suitable for real-time agricultural applications.
In recent years, advancements in machine learning and computer vision have enabled the development of intelligent systems capable of analyzing agricultural data efficiently. Plant diseases, if not detected at an early stage, can spread rapidly and cause severe economic losses to farmers. An automated plant disease detection system can assist farmers by providing fast, reliable, and cost-effective disease diagnosis without requiring expert knowledge. Such systems are especially beneficial in remote and rural areas where access to agricultural specialists is limited.
The proposed system utilizes digital leaf images captured using standard cameras or mobile devices, making it easily accessible for practical field use. Image preprocessing techniques such as noise removal, image resizing, color normalization, and segmentation are applied to enhance image quality and isolate the affected regions of the leaf. Feature extraction methods are then employed to obtain relevant characteristics related to color, texture, and shape, which are crucial indicators of plant diseases.
