• GUNA HARI KEERTHANA

India is an agrarian nation, with agriculture employing more than 70% of the population. Agriculture contributes to a percentage of our national revenue. Agriculturalists are losing money owing to numerous crop illnesses, and cultivators find it difficult to keep track of the crop on a regular basis when the cultivated area is large. As a result, plant disease identification is critical in the agricultural area. For the loss caused by agricultural diseases, which has a negative impact on crop quality and output, timely and precise disease identification is critical. Early detection and response can help to prevent plant loss due to disease and wasteful drug use. Image processing was previously used to detect plant disease automatically. Image processing tools as well as a machine learning mechanism are proposed for disease detection and classification. Crop disease will be detected via image processing stages such as image acquisition, image pre-processing, image feature extraction, feature classification, disease prediction, and fertilizer recommendation. Disease detection is important because it may assist farmers in providing appropriate solutions to prevent this disease. The proposed project seeks to develop a system that uses CNN to detect leaf diseases with increased accuracy on real-time leaf datasets with unstable or distorted backgrounds.

classification, image processing, plant leaf disease identification, convolutional neural network, deep learning.

"Identification of plant leaf diseases using deep learning model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 10, page no.a659-a666, October-2021, Available :http://www.jetir.org/papers/JETIR2110083.pdf

"Identification of plant leaf diseases using deep learning model", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 10, page no. ppa659-a666, October-2021, Available at : http://www.jetir.org/papers/JETIR2110083.pdf