Plant Disease Detection Using CNN with The Optimization Called Beluga Whale Optimization Mechanism

Abstract

Plant disease detection is critical for ensuring agricultural productivity. Early and accurate identification of plant diseases can help in the timely application of remedies, reducing yield loss and improving crop quality. This paper presents a deep learning(DL) approach using Convolutional Neural Networks (CNN) for plant disease detection, combined with an advanced optimization technique named the Beluga Whale Optimization Mechanism (BWOM). The CNN is implemented to extract and features from plant images, providing a robust model capable of differentiating between healthy and diseased plants. The BWOM is utilized to optimize the CNN's hyper parameters and weights, enhancing model accuracy and efficiency by reducing overfitting and improving generalization. The BWOM mimics the social behaviour and echolocation techniques of beluga whales to navigate and optimize solution spaces effectively. By iterating through population-based exploration and exploitation phases, BWOM provides a balanced search mechanism to fine-tune CNN parameters. Further the results demonstrate the effectiveness of combining CNN with BWOM in achieving high accuracy rates for plant disease classification.