BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model

Bhandari, Mohan; Shahi, Tej Bahadur; Neupane, Arjun; Walsh, Kerry B.

doi:10.3390/jimaging9020053

Cited by 26 publications

(13 citation statements)

References 51 publications

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“…in their study. They used the EfficientNetB5 transfer learning method in their research and reported an average training accuracy of 99.84% and an average testing accuracy of 99.07% (Bhandari et al 2023). Tian and their team proposed a model to identify diseases using tomato leaf images.…”

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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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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“…In [32], Jing, Jiaping, et al visually differentiates between nine different infectious diseases in tomato leaves and healthy leaves. The study applied EfficientNetB5 to a dataset of tomato leaf diseases (TLD) without segmentation, achieving an impressive average test accuracy of 99.07%.…”

Section: Related Workmentioning

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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“…The tomato plant is a nutrient-dense crop commonly grown and consumed as a vegetable [1]. Globally, an estimated 160 million tonnes of tomatoes are consumed annually [2]. The tomato is Despite rapid population growth, agriculture feeds everyone.…”

Section: Introductionmentioning

confidence: 99%

“…The growing utilization of tomatoes has led to a surge in demand for this commodity. As per statistical data, it has been observed that small-scale farmers are responsible for generating over 80% of the agricultural yield [2]. However, they face a significant challenge in crop loss, which amounts to approximately 50% due to the impact of diseases and pests.…”

Section: Introductionmentioning

confidence: 99%

Investigation of early symptoms of tomato leaf disorder by using analysing image and deep learning models

Vinta,

Koshariya,

Kumar S

et al. 2024

EAI Endorsed Trans IoT

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Despite rapid population growth, agriculture feeds everyone. To feed the people, agriculture must detect plant illnesses early. Predicting crop diseases early is unfortunate. The publication educates farmers about cutting-edge plant leaf disease-reduction strategies. Since tomato is a readily accessible vegetable, machine learning and image processing with an accurate algorithm are used to identify tomato leaf illnesses. This study examines disordered tomato leaf samples. Based on early signs, farmers may quickly identify tomato leaf problem samples. Histogram Equalization improves tomato leaf samples after re sizing them to 256 × 256 pixels. K-means clustering divides data space into Voronoi cells. Contour tracing extracts leaf sample boundaries. Discrete Wavelet Transform, Principal Component Analysis, and Grey Level Co-occurrence Matrix retrieve leaf sample information.

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BotanicX-AI: Identification of Tomato Leaf Diseases Using an Explanation-Driven Deep-Learning Model

Cited by 26 publications

References 51 publications

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

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

Optimizing Grape Leaf Disease Identification Through Transfer Learning and Hyperparameter Tuning

Investigation of early symptoms of tomato leaf disorder by using analysing image and deep learning models

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