A Compressed and Accelerated SegNet for Plant Leaf Disease Segmentation: A Differential Evolution Based Approach

Abstract

SegNet is a Convolution Neural Network (CNN) architecture consisting of encoder and decoder for pixel-wise classification of input images. It was found to give better results than state of the art pixel-wise segmentation of images. In proposed work, a compressed version of SegNet has been developed using Differential Evolution for segmenting the diseased regions in leaf images. The compressed model has been evaluated on publicly available street scene images and potato late blight leaf images from PlantVillage dataset. Using the proposed method a compression of 25x times is achieved on original SegNet and inference time is reduced by 1.675x times without loss in mean IOU accuracy.