• Mr. Gangadhar Doma
• Mrs. Tulasi Miriyala

In India agriculture plays a crucial role in countries economy, here the problem is rapid growth of diseases in plants.Early diagnosis of crop diseases is essential for global well-being and health. The traditional method of diagnosis is for a pathologist to visit the site and visually analyze each plant. However, in-person testing for some plant diseases is limited due to low accuracy and limited staff availability. Solving these problems requires the development of computational approaches to identify and classify various plant diseases. Presence of low-intensity information in image background and foreground, extreme color similarity between normal and infected plant regions, sample disturbances, and changes in plant leaf position, chrominance, structure and size provides accurate identification and classification of plants. Illness is difficult. With the help of CNN, we created a reliable plant disease classification system. In this study, we proposed a CNN-based deep learning technique for identifying various plant leaf diseases and providing disease-specific nutrients. Our goal is to identify and classify plant diseases before providing treatment. The record in question is from his Kaggle, a well-known public source. The dataset contains his 70,295 botanical photographs of the following fruits and vegetables like Apples, blueberries, cherries, corn, grapes, oranges, peaches, pepper bells, potatoes, raspberries, soybeans, strawberries, and tomatoes. The proposed method is capable of handling complex scenarios from a plant perspective, successfully detecting various disease forms and prescribing specific supplements against diseases.

Kaggle , CNN, Plant Disease, Pathologist, Image background & Foreground.

"PLANT DISEASE DETECTION AND SUPPLEMENT RECOMMENDATION USING CNN ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 5, page no.q200-q207, May-2024, Available :http://www.jetir.org/papers/JETIR2405H29.pdf

"PLANT DISEASE DETECTION AND SUPPLEMENT RECOMMENDATION USING CNN ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 5, page no. ppq200-q207, May-2024, Available at : http://www.jetir.org/papers/JETIR2405H29.pdf