Plant Disease detection for paddy crop using Ensemble of CNNs
Abhijit Acharya, Akhil Muvvala, Siddhesh Gawali, Ruhi Dhopavkar, Rutuja Kadam, Ashish Harsola
Abstract
Primary sector yield is the division of GDP on which the Indian Economy depends vastly, agriculture being the major contributor. This is one of the reasons that securing this income is of the utmost importance since diseases can affect the yield. Automating the process of detecting plant diseases can help in identifying the diseases in early stages and reduce their effects on the yield. In this paper a comparison between five widely known and new pre-trained Convolutional Neural Network (CNN) architectures viz. GoogLeNet, ResNet, ShuffleNet, ResNeXt and Wide ResNet is made along with their ensemble with different weights with respect to the detection of three paddy leaf diseases viz Blast, Bacterial Leaf Blight (BLB) and Brownspot. The models were compared on performance parameters like loss and accuracy of trained model. An accuracy of 95.54% is obtained for the final algorithm.