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Agri-Crop Intelligent System for Detecting Crop Disease and Recommending Soil Nutrition Value Based on Soil Testing Using Machine Learning

R. Raja Sekar, K. Bavani, Nagaraju B., Anurag Sinha, Jibran Gulzar, Siva Sai Krishna S., Yarrapathruni Shanmukha Kumar


This research examines the economic importance of agriculture for nations like India as well as the ways in which innovation might advance agriculture. In order to assist farmers in increasing their production, the application can classify leaf diseases by evaluating provided photos and that will propose compatible crops and fertilisers according to soil characteristics and current meteorological data. The aim of this research is to develop a website that will help farmers detect plant diseases, prescribe fertiliser, and produce better crops. We will give soil nutrition values according to the results of the soil testing to improve crop recommendations.


Pooling, padding, Relu, dropout, dense, CNN, random forest, Naïve Bayes, logistic regression, XGBoost

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