Image based anthracnose and red-rust leaf disease detection using deep learning

Abstract

Deep residual learning frameworks have achieved great success in image classification. This article presents the use of transfer learning which is applied on mango leaf image dataset for its disease's detection. New methodology and training have been used to facilitate the easy and rapid implementation of the mango leaf disease detection system in practice. Proposed system can be used to identify the mango leaf for whether it is healthy or infected with the diseases like anthracnose or red rust. This paper describes all the steps which are considered during the experimentation and design. These steps include leaf image data collection, its preparation, data assessment by agricultural experts, and selection and tranning of deep neural network architectures. A deep residual framework, residual neural network (ResNET), was used to perform deep convolutional neural network training. ResNETs are easy to optimize and can achieve better accuracies. The experimental results obtained from "ResNET architectures, such as ResNet18, ResNet34, ResNet50, and ResNet101" show the accuracies from 94% to 98%. ResNET18 architecture selected from above for system design as it gives 98% accuracy for mango leaf disease's detection. System will help farmers to identify leaf diseases in quick and efficient manner and facilitate decision-making in this front.

An Overview of Recent Advances in Greenhouse Strawberry Cultivation Using Deep Learning Techniques: A Review for Strawberry Practitioners

“…He et al (2016 [64]) proposed the ResNet model as part of their research at the Microsoft Research Institute. The ResNet model has been found to exhibit high accuracy in various applications and has been observed to possess a high degree of compatibility with other network structures, making it a promising candidate for integration into complex network architectures [8,15,41,53,57,[65][66][67][68]. The ResNet architecture incorporates a direct link, also referred to as a highway network, to enable the original input information to be transmitted directly to the subsequent layer.…”

“…Verticillium wilt detection: Sometimes emphasizing a single approach to solving a problem will result in more trustworthy solutions and an in-depth understanding of the specific situation [67,78]. Accordingly, a prior study [31] based on multi-task learning and attention networks was created to identify Verticillium wilt in strawberries.…”

Efficient deep learning architecture for the classification of diseased plant leaves

“…After that, the HDLCNN is used to detect and classify the disease. Different deep learning architectures are designed for a particular plant diseases classification such as tomato plant [17]- [19], anthracnose and red-rust leaf disease [20], coffee leaf disease [21], peanut leaf disease [22], and citrus leaf disease [23]. The main objective of this study is to design an efficient deep learning architecture for accurate and robust classification of plant leaf diseases using leaf images in order to overcome the challenges that have been presented.…”

Development of a Robust CNN Model for Mango Leaf Disease Detection and Classification: A Precision Agriculture Approach

“…26 Patil et al developed a CNN model that achieved an accuracy of 89.7% on a data set of mango leaf images. 27 In 2020, Arivazhagan et al developed a CNN model that achieved an accuracy of 96.6% on a data set of mango leaf images. 28 Venkatesh et al introduced a modified version of the VGGNet model called V2IncepNet, which integrates the best features of VGGNet and the inception module for the classification of anthracnose disease in mango leaves.…”