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

TL;DR

This paper introduces a lightweight multi-view CNN for plant disease detection that is efficient, accurate, and suitable for resource-limited environments, outperforming baseline models on the PlantVillage dataset.

Contribution

The paper proposes a novel, resource-efficient multi-view CNN that improves plant disease classification accuracy with fewer parameters compared to traditional deep models.

Findings

Achieved 2.9% higher accuracy than baseline CNN.

Less computationally expensive than state-of-the-art models.

Performed well on the PlantVillage dataset.

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.