Exploring DenseNet architectures with particle swarm optimization: efficient tomato leaf disease detection

Abstract

The critical challenge of tomato leaf disease demands effective solutions surpassing manual detection limitations, ensuring rapid intervention, optimal crop health, and maximizing yield for farmers. DenseNet, a convolutional neural network (CNN) architecture, is lauded for its adept handling of gradient flow issues by extensive interlayer connectivity. Its application holds significant promise in tackling the intricate task of identifying tomato leaf diseases. This research introduces an innovative methodology employing particle swarm optimization (PSO) to fine-tune the DenseNet architecture and hyperparameter. The proposed approach efficiently converges on optimal configurations encompassing parameters, such as the number of layers in dense blocks, growth rates, dropout rates, activation functions, and optimizers tailored for DenseNet. The DenseNet-PSO model achieves remarkable accuracy and precision in classifying various tomato leaf diseases, outperforming alternative architectures in total parameters, computational efficiency, and overall performance compared with six other architecture models. These outcomes elucidate DenseNet-PSO's efficacy in tomato leaf disease detection and demonstrate.