Authors: Sukesh G, Sanjay S
Abstract: Early detection of crop diseases is a critical requirement for ensuring agricultural productivity, food security, and economic stability for farmers. Crop diseases caused by fungi, bacteria, viruses, and pests often spread rapidly and remain unnoticed during their initial stages, leading to severe yield loss and financial damage. Traditional crop disease detection methods rely on manual inspection by farmers or agricultural experts, which is time-consuming, subjective, and often inaccurate due to human limitations and environmental variations. Moreover, expert support is not always accessible to farmers in rural and remote areas. With the rapid advancement of machine learning and image processing technologies, automated crop disease detection systems have gained significant attention in recent years. Leaf images contain rich visual information such as color variation, texture patterns, and shape irregularities that can be effectively analyzed using computer vision techniques. This paper presents an automated crop disease detection framework using machine learning and image processing techniques to identify plant diseases at an early stage. The proposed system involves image preprocessing, feature extraction, and classification using both traditional machine learning algorithms and deep learning models. The system aims to reduce crop loss, minimize excessive pesticide usage, and assist farmers in making timely and informed decisions. Experimental evaluation demonstrates that the proposed approach achieves improved accuracy and reliability, making it suitable for real-world agricultural applications.
