Abstract

Abstract:- Plant disease is a persistent problem for farmers, and it is one of the most serious risks to income and food security. This initiative aims to enhance the quality and quantity of agricultural output in the nation by classifying plant leaves into sick and healthy leaf types. The smart farming system is an innovative technology that aids in the improvement of agricultural quality and quantity. Deep learning using convolutional neural networks (CNNs) has achieved significant success in the classification of various plant leaf diseases, and it represented a contemporary technique that offers cost-effective disease diagnosis. CNN presents a simplified version of a much broader picture. CNN used to almost exclusively analyse visual images and is often involved in picture categorization behind the scenes. To extract generic characteristics and then categorise them into various categories depending on the attributes found. This initiative will provide financial assistance to farmers in order to increase crop yields and overall agricultural output. The article examines the anticipated techniques of plant leaf disease detection systems that aid agricultural progress. Image pre-processing, image categorization, feature extraction, and identifying healthy or sick are some of the stages.Keywords: Crop Monitoring; Deep Learning; Agricultural Crops; Insects, Crop Disease Prediction, Classification