Wolf-Bird Optimised SqueezeNet With Adaptive Pooling for Multiclass Plant Disease Detection From Leaf Images

Plant disease is accountable for majority of economic losses in agricultural industry across the globe. Hence, plant disease detection at an earlier phase is highly important to provide food safety and enhancement of farming systems. The manual detection techniques are challengeable and consume much time for classifying plant leaf diseases. Here, Wolf-Bird Skill Optimizer based Optimal Pooling SqueezeNet (WBSO_OptimalPool SqNet) is presented for a multiclass plant disease detection. Initially, plant leaf image is pre-processed employing Kuwahara filter. Afterwards, plant leaf disease segmentation is conducted by Spine-Generative Adversarial Network (Spine-GAN). Next, image augmentation is done and next, Convolutional Neural Network (CNN) features and DAISY with statistical features are extracted. Thereafter, two levels of classification namely plant type classification and plant disease classification are conducted. The classification of plant type and plant disease are accomplished by SqueezeNet, wherein pooling layers are modified by optimal pooling layer based on weights. The weights are calculated utilising WBSO, which is devised by incorporating Wolf-Bird Optimizer (WBO) with Skill Optimization Algorithm (SOA). In addition, WBSO_OptimalPool SqNet achieved maximal accuracy and True Positive Rate (TPR) about 91.897% and 90.815% as well as minimal False Positive Rate (FPR) about 7.186%.