• SUNIL A
• RASHMI B R
• JOSNA O

One of the most widely grown crops in India is rice, which is afflicted by a number of diseases at different phases of cultivation. With their little understanding, farmers find it extremely difficult to appropriately identify these diseases by hand. Farmers spend a lot of time and money managing diseases, and they use a cheap, unreliable way of disease detection that results in unhealthy farming. The automatic identification of infectious microorganisms in the leaves of rice plants is made possible by the progress of technical support in agriculture. One of the deep learning algorithms that has been successfully used to solve computer vision problems such as segmentation of objects, analysis of images, and picture classification is the convolutional neural network algorithm (CNN). In our work, we use a sort of CNN model called VGG16 in conjunction with a transfer learning strategy to identify illnesses in photos of rice leaves. The suggested model's parameters were tuned for the classification task, and a respectable accuracy of 97.57% was attained.

Rice leaf diseases, CNN Deep Learning, Fine-tuning, Transfer learning

"RICE DISEASE RECOGNITION USING VGG16 AND TRANSFER LEARNING BASED ON DEEP LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.11, Issue 6, page no.h292-h299, June-2024, Available :http://www.jetir.org/papers/JETIR2406732.pdf

"RICE DISEASE RECOGNITION USING VGG16 AND TRANSFER LEARNING BASED ON DEEP LEARNING TECHNIQUES", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.11, Issue 6, page no. pph292-h299, June-2024, Available at : http://www.jetir.org/papers/JETIR2406732.pdf