A Light Weight Multi-Features-View Convolution Neural Network For Plant Disease Identification

2026-04-28

AI Summary

This study addresses the limitations of conventional plant disease identification methods, which suffer from low efficiency, and the high computational demands of existing deep learning models that hinder deployment in resource-constrained rural settings. To overcome these challenges, this work proposes a lightweight multi-feature-view convolutional neural network that integrates additional image features beyond standard RGB channels. By doing so, the model achieves a notable improvement in classification accuracy while substantially reducing both parameter count and computational complexity. Experimental results on the PlantVillage dataset demonstrate a 2.9% increase in accuracy compared to baseline models using only RGB images, alongside significantly lower computational overhead than mainstream deep architectures, thereby offering a practical solution that balances high performance with real-world deployability.

Abstract

Agriculture is a key sector of the economies of developing countries. It serves as a primary source of income and employment for rural populations. However, each year, a large portion of crops is wasted because of pests and diseases. Well-timed prediction of plant diseases is crucial to sustainable, high-quality agricultural production. Detection of plant diseases through conventional methods is both labour-intensive and time-consuming. Researchers have developed image classification based automated techniques for this purpose. Most accurate methods are based on deep convolutional neural networks, which are computationally intensive, with many layers and millions of trainable parameters. In resource-constrained settings, especially in rural areas, it is difficult to deploy deep convolutional neural network models for efficient plant disease identification. To address these issues, an efficient and light-weight Multi-View Convolutional Neural Network is proposed. These additional features aid the proposed model to identify the plant diseases accurately and efficiently with less number of parameters. The proposed model is tested on a benchmark Plantvillage dataset and achieves an improvement of $ 2.9\%$ in classification accuracy compared to the baseline convolutional neural network model, which was trained only on Red, Green, and Blue (RGB) plant images. Compared with state-of-the-art deep convolutional neural network models, the proposed model is less computationally expensive and achieves comparable accuracy for plant disease identification on the PlantVillage dataset.

Problem

• plant disease identification
• light-weight model
• resource-constrained environments
• convolutional neural network
• agricultural automation

Innovation

• Light-weight CNN
• Multi-Features-View
• Plant Disease Identification
• Computational Efficiency
• Image Classification