Crop field paddy crop disease detection using SVM and CNN algorithm

Agriculture is essential for the economy and plant leaf disease must be minimized. Early recognition of problems is important, but the manual inspection is slow, error-prone, and has high manpower and time requirements. Artificial intelligence can be used to extract leaf color, shape, or texture data, thus aiding the detection of infections. Rice is one of the major cultivated crops in India which is affected by various diseases at various stages of its cultivation. It is very difficult for the farmers to manually identify these diseases accurately with their limited knowledge. Recent developments in Deep Learning show that Automatic Image Recognition systems using Convolutional Neural Network (CNN) with Transfer Learning (TL) models can be very beneficial in such problems. Since rice and tomato leaf disease image dataset is not easily available, we have created our own dataset which is small in size hence we have used Transfer Learning to develop our deep learning model. The proposed CNN architecture is based on MobileNet and is trained and tested on the dataset collected from rice fields and the internet.

KEYWORDS: Convolutional Neural Network, Deep Learning, Tomato leaf diseases, Rice Leaf Diseases, Transfer Learning.