A Novel Feature Extraction Model for the Detection of Plant Disease from Leaf Images in Low Computational Devices

TL;DR

This paper introduces a lightweight deep learning-based feature extraction method for rapid, accurate plant disease detection from leaf images on low-cost devices like smartphones, aiming to aid farmers in early disease identification.

Contribution

It proposes a novel feature extraction approach combining deep learning models optimized for low-resource devices, with comparative analysis on multiple models for plant disease detection.

Findings

AlexNet achieved 87% accuracy on tomato leaf disease dataset.

The proposed method is suitable for real-time detection on smartphones.

Lightweight models like AlexNet are effective for embedded systems.

Abstract

Diseases in plants cause significant danger to productive and secure agriculture. Plant diseases can be detected early and accurately, reducing crop losses and pesticide use. Traditional methods of plant disease identification, on the other hand, are generally time-consuming and require professional expertise. It would be beneficial to the farmers if they could detect the disease quickly by taking images of the leaf directly. This will be a time-saving process and they can take remedial actions immediately. To achieve this a novel feature extraction approach for detecting tomato plant illnesses from leaf photos using low-cost computing systems such as mobile phones is proposed in this study. The proposed approach integrates various types of Deep Learning techniques to extract robust and discriminative features from leaf images. After the proposed feature extraction comparisons have been made on five cutting-edge deep learning models: AlexNet, ResNet50, VGG16, VGG19, and MobileNet. The dataset contains 10,000 leaf photos from ten classes of tomato illnesses and one class of healthy leaves. Experimental findings demonstrate that AlexNet has an accuracy score of 87%, with the benefit of being quick and lightweight, making it appropriate for use on embedded systems and other low-processing devices like smartphones.