Machine Learning Approach for Analysis of Tomato Plant Leaf Disease Detection in Early Stages for better Crop Yield

Le résumé fourni par la source

In India’s rural regions, tomato are the primary extensively raised economic crop. Varying surroundings and various other elements affect the standard progress of tomato plants. Plant disease is a major contributor to financial loss and a big problem in agricultural output, in addition to severe weather and natural disasters. The traditional methods for detecting diseases in tomato crops proved ineffective, and the duration required for infection detection was prolonged. Early identification of diseases can yield better outcomes than existing detection algorithms. Consequently, Machine Learning methodologies leveraging Image Processing technology may be employed to detect tomato plant leaf diseases at an early stage. This research delivers an in-depth examination of the division and detection approaches provided for confirming illnesses in tomato leaves. The current research assesses the merits and drawbacks of the suggested methodologies. This study inevitably calls for the early identification of tomato leaf disease using Convolutional Neural Networks.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.