• DUVVURI BHARGAVI
• CH LAKSHMI NARAYANA
• DR. KONDAPALLI VENKATA RAMANA

Normally agriculture is considered as one of the primary sources of income for the farmers and Indian economy greatly depends on agriculture growth and development for better production. As we all know that farmers have been facing with some continuous challenges for centuries, such as different plant diseases during their cultivation. If the disease is predicted in the early stage, it will be very helpful for the end users to save their plants and take necessary precautions to stop its further spread. This is very difficult for one to identify the disease in the early stage and take necessary steps on that plant or crop. In some cases we can able to identify the disease directly by observing the physical changes occurred on external portion of the plants and in some cases we cannot able to diagnose what exactly it is suffering with. Hence this motivated me to develop this current application by using deep learning concept and then try to figure out the disease which is present on those plants. Nevertheless, manual detection of disease costs a large amount of time and labor, so it is inevitably prudent to have an automated system to detect disease. To solve the above problem, we are developing a model by taking VGGNet on ImageNet and Inception module are selected in our approach. Instead of starting the training from scratch by randomly initializing the weights, we initialize the weights using the pre-trained networks on the large labeled dataset, ImageNet. The proposed approach presents a substantial performance improvement with respect to other state-of-the-art methods; it achieves a validation accuracy of no less than 91.83% on the public dataset. Even under complex background conditions, the average accuracy of the proposed approach reaches 92.00% for the class prediction of rice plant images. By conducting various experiments on our proposed model, we achieved a best accurate classification of plant diseases.

Deep Learning, ImageNet, Validation Accuracy, Vggnet, Inception Model, Plant Diseases.

"Plant Disease Identification by Using Deep Learning Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.8, Issue 11, page no.b150-b157, November-2021, Available :http://www.jetir.org/papers/JETIR2111121.pdf

"Plant Disease Identification by Using Deep Learning Models", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.8, Issue 11, page no. ppb150-b157, November-2021, Available at : http://www.jetir.org/papers/JETIR2111121.pdf