Intelligent Systems for Crop Disease Identification

This study proves that plant disease detection by CNNs is a valid and feasible method of the present-day agriculture. Through the leaf sampling of pomegranate, guava, and maize, the research demonstrates the usefulness of visible disease symptoms in order to be successfully correlated with AIdriven concepts of detection. The given system can help farmers detect the issues at the earliest stage, minimize losses related to crops, and minimize the use of unnecessary chemicals. As they evolve and are implemented on a mass scale, such smart systems will be instrumental in the creation of long-term and technology-focused agrarian activities.

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