Involution-Infused DenseNet with Two-Step Compression for Resource-Efficient Plant Disease Classification

TL;DR

This paper introduces a resource-efficient plant disease classification model using a two-step compression method with pruning and knowledge distillation, combined with Involutional Layers in DenseNet, achieving high accuracy on plant datasets.

Contribution

It presents a novel two-step compression approach and hybrid DenseNet-Involutional architecture for efficient plant disease classification on resource-limited devices.

Findings

ResNet50s achieved over 99% accuracy after compression.

DenseNet-based models maintained high accuracy with fewer parameters.

Hybrid models supported effective deployment on energy-efficient devices.

Abstract

Agriculture is vital for global food security, but crops are vulnerable to diseases that impact yield and quality. While Convolutional Neural Networks (CNNs) accurately classify plant diseases using leaf images, their high computational demands hinder their deployment in resource-constrained settings such as smartphones, edge devices, and real-time monitoring systems. This study proposes a two-step model compression approach integrating Weight Pruning and Knowledge Distillation, along with the hybridization of DenseNet with Involutional Layers. Pruning reduces model size and computational load, while distillation improves the smaller student models performance by transferring knowledge from a larger teacher network. The hybridization enhances the models ability to capture spatial features efficiently. These compressed models are suitable for real-time applications, promoting precision agriculture through rapid disease identification and crop management. The results demonstrate ResNet50s superior performance post-compression, achieving 99.55% and 98.99% accuracy on the PlantVillage and PaddyLeaf datasets, respectively. The DenseNet-based model, optimized for efficiency, recorded 99.21% and 93.96% accuracy with a minimal parameter count. Furthermore, the hybrid model achieved 98.87% and 97.10% accuracy, supporting the practical deployment of energy-efficient devices for timely disease intervention and sustainable farming practices.