Pest Detection in Crops Using Machine Learning

Abstract: Agriculture plays a crucial role in the economy and food security of many countries. Crop diseases and pest infections significantly reduce yield and quality, causing major financial losses to farmers. Early detection of plant diseases using traditional manual inspection is time-consuming, requires expertise, and is often inaccurate. This project proposes an intelligent Crop Pest and Disease Detection System that uses machine learning and image processing techniques to identify plant diseases from leaf images. A deep learning model is trained using a dataset of healthy and infected crop leaves to classify different diseases automatically. The system is integrated with a web interface where users can upload images of crop leaves and instantly receive predictions. The proposed system helps farmers and agricultural experts detect diseases quickly, reduce crop loss, and take preventive measures at the right time. This solution promotes smart agriculture by combining artificial intelligence with real-time accessibility.

Keywords: Crop Disease Detection, Pest Identification, Machine Learning, Deep Learning, Image Processing, Smart Agriculture, Leaf Image Classification, Computer Vision, Web Application, Artificial Intelligence, Precision Farming, Automated Diagnosis, Crop Health Monitoring.