CROP DISEASE PREDICTION AND MANAGEMENT SYSTEM

Abstract

Agriculture continues to be one of the most important sectors contributing to food production and economic stability across the world. A significant portion of the global population relies on agriculture as their primary source of livelihood. However, crop diseases remain a major challenge for farmers because they can significantly reduce crop yield and affect the quality of agricultural products. Plant diseases may occur due to fungal infections, bacterial pathogens, viruses, or environmental factors such as excessive moisture, temperature variations, and soil conditions. If these diseases are not identified at an early stage, they may spread rapidly across the field and lead to severe agricultural losses. Traditionally, farmers identify crop diseases through manual observation of plant leaves and stems. This approach often depends on the experience and knowledge of the farmer or agricultural expert. In many cases, farmers may not have sufficient expertise to correctly diagnose diseases, especially during the early stages when symptoms are less visible. As a result, improper treatment methods may be applied, which further affects crop productivity. With the advancement of artificial intelligence, machine learning, and computer vision technologies, automated systems can now be developed to support farmers in identifying plant diseases more accurately. Image processing techniques allow computers to analyze digital images of crop leaves and detect visual patterns associated with different plant diseases.This work presents a crop disease prediction and management system that utilizes machine learning and image processing techniques to detect plant diseases from leaf images. The system processes crop images through multiple stages including image acquisition, preprocessing, feature extraction, and classification. A trained machine learning model analyzes the extracted features and predicts the disease affecting the crop. disease prediction, the system also provides recommendations for disease management and preventive measures that can help farmers control the spread of infections. Experimental analysis indicates that the proposed system can effectively detect crop diseases and assist farmers in making better agricultural decisions. Such intelligent systems can contribute to improving agricultural productivity and supporting sustainable farming practices.