The dataset is available at Mendeley: J, ARUN PANDIAN; GOPAL, GEETHARAMANI (2019), “Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network”, Mendeley Data, V1, doi: 10.17632/tywbtsjrjv.1 .
Open resource ↗Mendeley Data · 10.17632/tywbtsjrjv.1 · lines:498-522Unverified paper record
An enhanced lightweight T-Net architecture based on convolutional neural network (CNN) for tomato plant leaf disease classification.
PeerJ Computer Science · 2 Dec 2024 · 10.7717/peerj-cs.2495
Abstract
Tomatoes are a widely cultivated crop globally, and according to the Food and Agriculture Organization (FAO) statistics, tomatoes are the third after potatoes and sweet potatoes. Tomatoes are commonly used in kitchens worldwide. Despite their popularity, tomato crops face challenges from several diseases, which reduce their quality and quantity. Therefore, there is a significant problem with global agricultural productivity due to the development of diseases related to tomatoes. Fusarium wilt and bacterial blight are substantial challenges for tomato farming, affecting global economies and food security. Technological breakthroughs are necessary because existing disease detection methods are time-consuming and labor-intensive. We have proposed the T-Net model to find a rapid, accurate approach to tackle the challenge of automated detection of tomato disease. This novel deep learning model utilizes a unique combination of the layered architecture of convolutional neural networks (CNNs) and a transfer learning model based on VGG-16, Inception V3, and AlexNet to classify tomato leaf disease. Our suggested T-Net model outperforms earlier methods with an astounding 98.97% accuracy rate. We prove the effectiveness of our technique by extensive experimentation and comparison with current approaches. This study offers a dependable and understandable method for diagnosing tomato illnesses, marking a substantial development in agricultural technology. The proposed T-Net-based framework helps protect crops by providing farmers with practical knowledge for managing disease. The source code can be accessed from the given link.
Plant phenotyping relevance
トマト葉の病害状態を画像から分類する深層学習手法を開発・比較しており、植物病害表現型の取得・推定が研究の中心である。
abstractWe have proposed the T-Net model to find a rapid, accurate approach to tackle the challenge of automated detection of tomato disease.
abstractThis novel deep learning model utilizes a unique combination of the layered architecture of convolutional neural networks (CNNs) and a transfer learning model based on VGG-16, Inception V3, and AlexNet to classify tomato leaf disease.
abstractWe prove the effectiveness of our technique by extensive experimentation and comparison with current approaches.
Code and data availability
The paper's tomato leaf disease classification study uses the public PlantVillage-derived Mendeley dataset, provides its restructured training/validation data on Kaggle, and releases its T-Net analysis source code on GitHub and Zenodo, all with explicit availability statements and public URLs.
The dataset is available at Mendeley: J, ARUN PANDIAN; GOPAL, GEETHARAMANI (2019), “Data for: Identification of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network”, Mendeley Data, V1, doi: 10.17632/tywbtsjrjv.1 . The training and validation data of tomato leaf disease is available at Kaggle: https://www.kaggle.com/datasets/amreenbatool/plant-leaf-disease-data . The source code is available at GitHub and Zenodo: - https://github.com/Amreen-source/Tomato-leaf-disease-detection- - Amreen, B. (2024). Tomato-leaf-disease-detection [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14020689 . References Agarwal et al. (2020) Agarwal M, Singh A, Arjaria S, Sinha A, Gupta S. ToLeD
Open resource ↗Kaggle · lines:498-522on of Plant Leaf Diseases Using a 9-layer Deep Convolutional Neural Network”, Mendeley Data, V1, doi: 10.17632/tywbtsjrjv.1 . The training and validation data of tomato leaf disease is available at Kaggle: https://www.kaggle.com/datasets/amreenbatool/plant-leaf-disease-data . The source code is available at GitHub and Zenodo: - https://github.com/Amreen-source/Tomato-leaf-disease-detection- - Amreen, B. (2024). Tomato-leaf-disease-detection [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14020689 . References Agarwal et al. (2020) Agarwal M, Singh A, Arjaria S, Sinha A, Gupta S. ToLeD: tomato leaf disease detection using convolution neural network. Procedia Computer Science. 2020;167:293–
Open resource ↗GitHub · lines:498-522ing and validation data of tomato leaf disease is available at Kaggle: https://www.kaggle.com/datasets/amreenbatool/plant-leaf-disease-data . The source code is available at GitHub and Zenodo: - https://github.com/Amreen-source/Tomato-leaf-disease-detection- - Amreen, B. (2024). Tomato-leaf-disease-detection [Data set]. Zenodo. https://doi.org/10.5281/zenodo.14020689 . References Agarwal et al. (2020) Agarwal M, Singh A, Arjaria S, Sinha A, Gupta S. ToLeD: tomato leaf disease detection using convolution neural network. Procedia Computer Science. 2020;167:293–301. doi: 10.1016/j.procs.2020.03.225. Ahmad, Saraswat & El Gamal (2023) Ahmad A, Saraswat D, El Gamal A. A survey on using deep learn
Open resource ↗Zenodo · 10.5281/zenodo.14020689 · lines:498-522This is an automatically classified, unverified record. Curator approval is required before any resource enters the Catalog.