• S SIVAKAMA SUNDARI

Plants are the meals supply of the earth. Plant infections and illnesses are consequently a first-rate threat, however the maximum not un-usual place prognosis is basically to study flora for the presence or absence of visible symptoms. The agricultural production of the country gets affected majorly due to pests as they affect the plants and crops. The detection and identification of disease is been observed by farmers and experts through their naked eyes. Grounded the leaf image categorization, an approach of plant disease recognition model is being developed with the help of deep convolutional networks. Early detection of diseases to which plants are exposed is very important, especially in a country like India with a large population. The diseases caused by bacteria, virus and fungus results on lowering the crop yield in a huge aspect. The loss can be prevented by predicting the plant disease at the earliest. Improved disease detection performance and precision are possible through Deep Learning concepts. It uses image processing concepts for noise reduction, ML and DL concepts i.e., CNN for Problem Solving. This project captures plants and leaf disease and helps farmers to identify and detect the solutions for the problem that is being infected.

"PLANT DISEASE DETECTION USING CNN TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 2, page no.f185-f203, February-2023, Available :http://www.jetir.org/papers/JETIR2302523.pdf

"PLANT DISEASE DETECTION USING CNN TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 2, page no. ppf185-f203, February-2023, Available at : http://www.jetir.org/papers/JETIR2302523.pdf