Tomato leaf diseases recognition based on deep convolutional neural networks

Tian, Kai; Zeng, Jiefeng; Song, Tianci; Li, Zhuliu; Asenso, Evans; Li, Jiuhao

doi:10.4081/jae.2022.1432

Cited by 9 publications

(4 citation statements)

References 38 publications

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“…In this study [39], CNN models were employed to categorize diseases found in tomato plants, achieving an accuracy of 98.6% across 10 different disease classes. In the study [42], the success rate for tomato leaf disease recognition using VGG16, InceptionV3, and Resnet50 models were 99.62%, 99.75%, and 99.5%, respectively. The goal of this study [37] was for the purpose of diagnosing diseases present in tomato leaves by categorizing healthy and unhealthy tomato leaf photos using two pretrained CNNs, namely InceptionV3 and Inception ResNetV2.…”

Section: Related Workmentioning

confidence: 89%

Ensemble of Deep Learning Models for Multi-plant Disease Classification in Smart Farming

Vo¹,

Quach²,

Ngoc³

2023

IJACSA

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Plant disease identification at an early stage plays a crucial role in ensuring efficient management of the diseases and crop protection. The occurrence of plant ailments can result in substantial reductions in both crop yield and quality, which may cause financial setbacks for farmers and lead to food shortages for consumers. Traditional methods of disease detection rely on visual observation, which can consume a significant amount of time, be a labor-intensive, and often be inaccurate. Automated disease detection systems, based on techniques for machine learning have the potential to greatly improve the precision and speed of disease detection. This article presents a model for classifying plant diseases that combines the output of two transfer learning models, EfficientNetB0 and MobileNetV2, to improve disease classification accuracy. The PlantVillage Dataset was used to train and test the model under consideration, which contains 54,305 photos of 38 different plant disease classes, achieving an accuracy rate of 99.77% in disease classification. The use of an ensemble of deep learning models in this study shows promising results, indicating that the technique can enhance the accuracy of plant disease classification. Besides, this study contributes to the development of accurate and reliable automated disease detection systems, thereby supporting sustainable agriculture and global food security.

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Section: Related Workmentioning

confidence: 89%

Ensemble of Deep Learning Models for Multi-plant Disease Classification in Smart Farming

Vo¹,

Quach²,

Ngoc³

2023

IJACSA

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“…This comprehensive evaluation aims to determine which model performed the best given our particular dataset. Tomato Leaves Diseases 9 14,828 GoogleNet 99.18% [63] The Tomato Plant 10 19,553 Inception-V3 99.75% [46] The grapevine leaves 5 3000 EfficientNet B0 99.67% [64] Grape Leaf Diseases 4 3,885 GoogleNet 94.05% [65] Grape Leaf Diseases 4 9,027 EfficientNet B7 98.70% [47] Grapevine Leaves We then moved on to the crucial step of fine-tuning the hyperparameters of the model that was performing at its best. This fundamental improvement process increases the model's overall efficacy in accurately recognizing grape leaf illnesses.…”

Section: Discussionmentioning

confidence: 99%

Optimizing Grape Leaf Disease Identification Through Transfer Learning and Hyperparameter Tuning

Vo,

Mui,

Thien

et al. 2024

IJACSA

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Grapes are a globally cultivated fruit with significant economic and nutritional value, but they are susceptible to diseases that can harm crop quality and yield. Identifying grape leaf diseases accurately and promptly is vital for effective disease management and sustainable viticulture. To address this challenge, we employ a transfer learning approach, utilizing well-established pre-trained models such as ResNet50V2, ResNet152V2, MobileNetV2, Xception, and In-ceptionV3, renowned for their exceptional performance across various tasks. Our primary objective is to identify the most suitable network architecture for the classification of grape leaf diseases. This is achieved through a rigorous evaluation process that considers key metrics such as accuracy, F1 score, precision, recall, and loss. By systematically assessing these models, we aim to select the one that demonstrates the best performance on our dataset. Following model selection, we proceed to the crucial phase of fine-tuning the model's hyperparameters. This fine-tuning process is essential to enhance the model's predictive capabilities and overall effectiveness in disease identification. To accomplish this, we conduct an extensive hyperparameter search using the Hyperband strategy. Hyperparameters play a pivotal role in shaping the behavior and performance of deep learning models, and by systematically exploring a wide range of hyperparameter combinations, our goal is to identify the most optimal configuration that maximizes the model's performance on the given dataset. Additionally, the study's results were compared with those of numerous relevant studies.

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“…In this study, three different deep learning network architectures (VGG16, Inception_v3, and Resnet50) were used, and an Android application was also developed. The application can identify tomato diseases with a test accuracy of 99% (Tian et al 2022).…”

Section: Deep Learning-based Methodsmentioning

confidence: 99%

A novel approach for tomato leaf disease classification with deep convolutional neural networks

IRMAK,

SAYGILI

2023

J Agr Sci-Tarim Bili

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Computer-aided automation systems that detect plant diseases are one of the challenging research areas that provide effective results in the agricultural field. Tomato crops are a major product with high commercial value worldwide and are produced in large quantities. This study proposes a new approach for the automatic detection of tomato leaf diseases, which employs classical learning methods and deep neural networks for image classification. Specifically, Local Binary Pattern (LBP) method was used for feature extraction in classical learning methods, while Extreme Learning Machines, k-Nearest Neighborhood (kNN), and Support Vector Machines (SVM) were used for classification. On the other hand, a novel Convolutional Neural Network (CNN) framework with its parameters and layers was employed for deep learning. The study shows that the accuracy values obtained from the proposed approach are better than the state-of-the-art studies. The classification process was carried out with different numbers of classes, including binary classification (healthy vs. unhealthy), 6-class, and 10-class classification for distinguishing different types of diseases. The results indicate that the CNN model outperforms classical learning methods, with accuracy values of 99.5%, 98.50%, and 97.0% obtained for the classification of 2, 6, and 10 classes, respectively. In future studies, computer-aided automated systems can be utilized to detect different diseases for various plant species.

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Tomato leaf diseases recognition based on deep convolutional neural networks

Cited by 9 publications

References 38 publications

Ensemble of Deep Learning Models for Multi-plant Disease Classification in Smart Farming

Ensemble of Deep Learning Models for Multi-plant Disease Classification in Smart Farming

Optimizing Grape Leaf Disease Identification Through Transfer Learning and Hyperparameter Tuning

A novel approach for tomato leaf disease classification with deep convolutional neural networks

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