• V.Sahasranamam

Classification of diseases in plants by using neural-networks based classification and supervised learning method to identify disease in plants. Here training data sets for five disease is deployed to match the image captured to the trained data and to give valuable output. Hidden layer are used to give scalable data to find and predict correct disease for final identification of disease through Deep learning and also artificial intelligence techniques without or less human intervention. Techniques -tensorflow application and python are used to develop an app so that ordinary poor farmer can use this app and get appropriate pest control suggestion.Large scale farmers are most likely able to handle the loss incurred and also employ agricultural professionals to detect and provide a cure for the disease. But small scale farmers cannot afford it and by the time they get the supposed help from the government, the plants will be beyond redemption. In this modern era, in every nook and corner of our country there is at least a single Smartphone available. Using this to our advantage, we were able to devise an android Smartphone app which identifies the disease through the Smartphone camera and immediately give the best pest control for usage. Our app also displays whether the said plant is infected if give best pest control suggestion.

TensorFlow Lite, Pest and Nutrient suggestion, MobileNet, Android, Classifier, Quantization

"Mobile based tomato-Disease identification Pest suggestion,Nutrient Management using deep learning and neural networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.332-340, June 2019, Available :http://www.jetir.org/papers/JETIR1907A73.pdf

"Mobile based tomato-Disease identification Pest suggestion,Nutrient Management using deep learning and neural networks", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp332-340, June 2019, Available at : http://www.jetir.org/papers/JETIR1907A73.pdf