Literature Review of Disease Detection in Tomato Leaf using Deep Learning Techniques

David, Hepzibah Elizabeth; Ramalakshmi, K.; Gunasekaran, Hemalatha; Venkatesan, Rajkumar

doi:10.1109/icaccs51430.2021.9441714

Cited by 21 publications

(5 citation statements)

References 25 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This whitepaper additionally talks about the qualities and shortcomings of the proposed procedure. The article closes by proposing a sickness early location strategy to distinguish tomato leaf illnesses utilizing a mixture profound learning design [3].…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach for Plant Leaf Disease Predictions Using Recurrent Neural Network RNN Classification Method

Vidya Sivalingam,

Rawia Elarabi,

J. Bhargavi

et al. 2023

ARASET

View full text Add to dashboard Cite

Agriculture is an essential part of our country and is only necessary for its development. The problem here is leaf spot disease, which significantly affects the growth of food production. Food production is considered one of the most important in our nation. We must detect it early and bring it under control. It is important to analyze one of the versions of leaf disease in its development. The existing system of machine learning could not give the proper prediction result of the lead disease prediction and classification. Various plant diseases affect leaf areas, and leaf areas have some black marks. Black marks are one type of leaf disease in plants. Find out the disease levels and accuracy. One standard learning method of deep learning classification has been used to classify leaf disease prediction. Here pre-processing and feature extraction using CNN and feature extraction and type using the RNN-based LSTM method are used. The data has been found in the Kaggle dataset; the total dataset is divided into 20000 plant village dataset images using training, and the validation dataset has available there. This dataset predicts the disease name and related data is collected to gather the leaf disease information. The RNN algorithm result is improved accuracy of identification compared to other methods but best performance for the LSTM method.

show abstract

Section: Related Workmentioning

confidence: 99%

A Novel Approach for Plant Leaf Disease Predictions Using Recurrent Neural Network RNN Classification Method

Vidya Sivalingam,

Rawia Elarabi,

J. Bhargavi

et al. 2023

ARASET

View full text Add to dashboard Cite

show abstract

“…ResNet-50 is a 50-layer deep convolutional neural network. The network can be loaded in its pre-trained state, which has been exposed to over a million images in the ImageNet database [16].…”

Section: B Resnet 50 and Resnet 101mentioning

confidence: 99%

A Review on Tomato Leaf Disease Detection using Deep Learning Approaches

Vengaiah,

Konda

2023

IJRITCC

View full text Add to dashboard Cite

Agriculture is one of the major sectors that influence the India economy due to the huge population and ever-growing food demand. Identification of diseases that affect the low yield in food crops plays a major role to improve the yield of a crop. India holds the world's second-largest share of tomato production. Unfortunately, tomato plants are vulnerable to various diseases due to factors such as climate change, heavy rainfall, soil conditions, pesticides, and animals. A significant number of studies have examined the potential of deep learning techniques to combat the leaf disease in tomatoes in the last decade. However, despite the range of applications, several gaps within tomato leaf disease detection are yet to be addressed to support the tomato leaf disease diagnosis. Thus, there is a need to create an information base of existing approaches and identify the challenges and opportunities to help advance the development of tools that address the needs of tomato farmers. The review is focussed on providing a detailed assessment and considerations for developing deep learning-based Convolutional Neural Networks (CNNs) architectures like Dense Net, ResNet, VGG Net, Google Net, Alex Net, and LeNet that are applied to detect the disease in tomato leaves to identify 10 classes of diseases affecting tomato plant leaves, with distinct trained disease datasets. The performance of architecture studies using the data from plantvillage dataset, which includes healthy and diseased classes, with the assistance of several different architectural designs. This paper helps to address the existing research gaps by guiding further development and application of tools to support tomato leaves disease diagnosis and provide disease management support to farmers in improving the crop.

show abstract

“…Over the past few decades, India has seen substantial growth in vegetable production [2], positioning itself as a significant contributor to the global vegetable market, second only to China. As a cornerstone of the Indian economy [3], agriculture faces considerable challenges due to plant diseases, which, if unaddressed, can drastically reduce output [4].…”

Section: Introductionmentioning

confidence: 99%

A Comparative Study of Convolutional Neural Network Architectures for Enhanced Tomato Leaf Disease Classification Using Refined Statistical Features

Vengaiah,

Konda

2024

View full text Add to dashboard Cite

Tomatoes, a staple in culinary practices, are currently in high demand yet low supply in India, rendering them unaffordable to the general population. This issue largely stems from the inability of farmers to identify and control prevalent tomato leaf diseases, leading to significant crop losses. Early detection and classification of leaf diseases are paramount to mitigate this problem, thereby boosting crop productivity. Despite extensive research in this domain, the precise localization and identification of various tomato leaf diseases present a complex task. This complexity arises from the significant overlap between the healthy and diseased portions of the leaves. The process is further complicated by the minimal contrast between the background and foreground of the specimen under investigation. To address these challenges, this study conducts a comprehensive performance analysis of several Convolutional Neural Networks (CNNs) models, namely, ResNet-152, ResNet-101, VGGNet, Alex Net, and LeNet, applied to the PlantVillage dataset. The results indicate that the ResNet-152 and ResNet-101 models yield superior accuracy rates when applied to both full-resolution source images and their background-removed counterparts. The performance outcomes reported herein surpass those documented in the existing literature, demonstrating the potential for significant advancements in the early detection and classification of tomato leaf diseases.

show abstract

Literature Review of Disease Detection in Tomato Leaf using Deep Learning Techniques

Cited by 21 publications

References 25 publications

A Novel Approach for Plant Leaf Disease Predictions Using Recurrent Neural Network RNN Classification Method

A Novel Approach for Plant Leaf Disease Predictions Using Recurrent Neural Network RNN Classification Method

A Review on Tomato Leaf Disease Detection using Deep Learning Approaches

A Comparative Study of Convolutional Neural Network Architectures for Enhanced Tomato Leaf Disease Classification Using Refined Statistical Features

Contact Info

Product

Resources

About