Plant Disease Detection and Classification

Abstract

Abstract—Everyone’s daily existence in the twenty-first century involves a large amount of machine learning.These days, a wide range of applications, including object identification, object categorisation, and medicinal uses, use it.This seeks to identify illnesses in plant leaves using deep convolutional neural networks. Farmers typically use manual disease detection techniques since they are ignorant of illnesses on plant leaves. They frequently become less abundant as the virus multiplies.However, in many parts of the world, rapid identification has to be enhanced due to a lack of necessary infrastructure.The efficient handling of plant diseases is essential for both food security and agricultural production.The proposed application uses CNNs for plant disease detection and classification. It attempts to identify illnesses from photos and offers disease descriptions and preventive steps and suggests suitable pesticides along with product link for further action and other features of the application are crop recommendation and fertilizer prediction based on some values(nitrogen,phosphorous,pottasium).The CNN model archi- tecture is optimized for accurate disease classification,taking into account various types of plant diseases and leaf variations.This aims to enhance agricultural practices by providing a scal- able and efficient tool for early disease detection,classification, and treatment recommendation,crop recommendation,thereby contributing to improved crop yield and sustainable farming practices. Index Terms—Plant Disease Detection, CNN (Convolutional Neural Network), Automated Disease Description,Pesticide Sug- gestion,Precision Farming.

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