An experimental study of tomato viral leaf diseases detection using machine learning classification techniques

Sagar, Sanjeela; Singh, Jaswinder

doi:10.11591/eei.v12i1.4385

Cited by 14 publications

(7 citation statements)

References 22 publications

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“…Also covered were biotic illnesses brought on by bacterial and fungal infections, specifically tomato leaf blight, blast, and browning. The precision of the recommended model's identification rate is 98.49% [9]. The recommended model was evaluated against variants of visual geometry group (VGG) and ResNet with the identical dataset.…”

Section: Methodsmentioning

confidence: 99%

Encouraging hygiene permanence in tomato leaf and applying machine learning techniques

Madderi Sivalingam,

Devi Badabagni

2024

IJEECS

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<div align="center"><span>Tomatoes are the major ingredient in food preparation, which leads to a huge food production rate. Most countries cultivate huge tomatoes at the same time that crop diseases affect the production rate due to many different types of diseases. The various types of diseases are bacterial spots, septoria leaf spot, left mold, late blight, early blight, arget and spot. Many research studies review these tomato leaf diseases with various statistics. The survey on disease will give a clear idea of reasons and prevention methods, also presenting how to reduce it in the early stages. In another study, tomato leaf images were taken to classify the diseased and non-diseased varieties. Few studies compare the standard model of disease prediction with the machine learning models. Therefore, this research study discusses tomato leaf disease detection and prevention methods used by various researchers in their studies and finally consolidate the observations. This study also deals with encouraging hygiene permanence in tomato leaf using machine learning algorithms. The convolutional neural network (CNN) was used to predict the early nature of the hygiene nature of leafy vegetable plants for the benefit of agriculture people and concluded with better future suggestions.</span></div>

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Section: Methodsmentioning

confidence: 99%

Encouraging hygiene permanence in tomato leaf and applying machine learning techniques

Madderi Sivalingam,

Devi Badabagni

2024

IJEECS

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“…The model is trained on a dataset of 8,000 photos and achieves an accuracy of 92.6% by utilizing CNN for feature extraction and SVM for classification. [18] presents an experimental study comparing traditional ML algorithms (RF, SVM, NB) with a deep learning CNN algorithm in order to classify tomato leaf disease. The results show that the CNN approach outperforms traditional methods, achieving over 95% accuracy in detection and classification.…”

Section: Related Workmentioning

confidence: 99%

Tomato Disease Recognition: Advancing Accuracy Through Xception and Bilinear Pooling Fusion

Vo,

Thien,

Mui

2023

IJACSA

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Accurate detection and classification of tomato diseases are essential for effective disease management and maintaining agricultural productivity. This paper presents a novel approach to tomato disease recognition that combines Xception, a pre-trained convolutional neural network (CNN), with bilinear pooling to advance accuracy. The proposed model consists of two parallel Xception-based CNNs that independently process input tomato images. Bilinear pooling is applied to combine the feature maps generated by the two CNNs, capturing intricate interactions between different image regions. This fusion of Xception and bilinear pooling results in a comprehensive representation of tomato diseases, leading to improved recognition performance. Extensive experiments were conducted on a diverse dataset of annotated tomato disease images to evaluate the effectiveness of the suggested approach. The model achieved a remarkable test accuracy of 98.7%, surpassing conventional CNN approaches. This high accuracy demonstrates the efficacy of the integrated Xception and bilinear pooling model in accurately identifying and classifying tomato diseases. The implications of this research are significant for automated tomato disease recognition systems, enabling timely and precise disease diagnosis. The model's exceptional accuracy empowers farmers and agricultural practitioners to implement targeted disease management strategies, minimizing crop losses and optimizing yields.

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“…In addtional, approach of study, utilizing the ResNet-50 model, delivered a remarkable outcome, achieving a substantial accuracy rate of 96.35% when tested on a balanced dataset split, with 50% used for training and the remaining 50% for testing. In this study [25], Sanjeela Sagar and Jaswinder Singh conducted an experimental and comparative analysis of tomato leaf disease classification, employing both traditional machine learning algorithms such as random forest (RF), support vector machines (SVM), and naïve bayes (NB), as well as a deep learning convolutional neural network (CNN) algorithm. Notably, our findings revealed that the CNN, specifically when integrated with a pre-trained Inception v3 model, outperformed traditional methods.…”

Section: Related Workmentioning

confidence: 99%

An Approach for Classification of Diseases on Leaves

2023

IJACSA

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In recent years, significant advancements have been made in the realm of plant disease classification, with a particular focus on leveraging the capabilities of deep learning techniques. This study delves into the utilization of renowned Convolutional Neural Network (CNN) models, including EfficientNetB5, Mo-bileNet, ResNet50, InceptionV3, and VGG16, for the purpose of plant disease classification. The core methodology involves employing transfer learning, wherein these established CNN models are employed as a foundation and subsequently finetuned using a publicly accessible plant disease dataset. The study also compared the results with some deep learning models and with state-of-the-art. Among the tested CNNs, EfficientNetB5 has shown the best performance. EfficientNetB5 has outperformed another model and obtained 99.2% classification accuracy.

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An experimental study of tomato viral leaf diseases detection using machine learning classification techniques

Cited by 14 publications

References 22 publications

Encouraging hygiene permanence in tomato leaf and applying machine learning techniques

Encouraging hygiene permanence in tomato leaf and applying machine learning techniques

Tomato Disease Recognition: Advancing Accuracy Through Xception and Bilinear Pooling Fusion

An Approach for Classification of Diseases on Leaves

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