• SACHIN CHAUDHARY
• SANDHYA TARAR

Diseases in Crops are one of the major reasons that affects the crops output. Crops play a very significant role for survival of beings including but not limited to us (human beings), as they are one of the significant sources of daily life needs including food. Identification of the diseases in a rapid way so that a cure can be take in time, is still a cumbersome process. Fortunately, in this era where smartphones are picking up rapidly and recent advances in machine learning, it is now possible to build smartphone assisted disease diagnosis. Using an open source database of around 54,306 images of crops with and without disease, we trained a Deep (CNN) Convolutional neural network to identify 14 distinct species with 26 diseases or their absence thereof. The trained model is observed to show approximately 99.35 % accuracy on the held out test set, illustrating that the approach is viable.

Neural Network, TensorFlow, Image Classification, Diseases in Crops.

"Deep Learning based Plant Disease Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 5, page no.455-459, May-2019, Available :http://www.jetir.org/papers/JETIR1905166.pdf

"Deep Learning based Plant Disease Detection", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 5, page no. pp455-459, May-2019, Available at : http://www.jetir.org/papers/JETIR1905166.pdf