A Novel Approach for Tomato Leaf Disease Classification with Deep Convolutional Neural Networks

Gizem Irmak; Ahmet Saygılı

Makale detayları

A Novel Approach for Tomato Leaf Disease Classification with Deep Convolutional Neural Networks

Yazarlar:

Gizem Irmak | Ahmet Saygılı

Yayıncı:

Ankara University

Ana Başlıklar:

Artificial Intelligence (Other)

Dergi:

Journal of Agricultural Sciences Vol.30 Issue 2, pp.367-385

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001313943

pISSN:

1300-7580

Anahtar kelimeler:

Özet:

 Computer-aided automation systems for the detection of plant diseases represent a challenging and highly impactful research domain in the field of agriculture. Tomatoes, a major and globally significant agricultural commodity, are cultivated in large quantities. This study introduces a novel approach for the automated detection of diseases on tomato leaves, leveraging both classical machine learning methods and deep neural networks for image classification. Specifically, classical learning methods employed the local binary pattern LBP technique for feature extraction, while classification tasks were carried out using extreme learning machines, k-nearest neighborhood kNN, and support vector machines SVM. In contrast, a novel convolutional neural network CNN framework, complete with unique parameters and layers, was utilized for deep learning. The results of this study demonstrate that the proposed approach outperforms state-of-the-art studies in terms of accuracy. The classification process covered various scenarios, including binary classification healthy vs. unhealthy, 6-class classification, and 10-class classification for distinguishing different types of diseases. The findings indicate that the CNN model consistently outperformed classical learning methods, achieving accuracy rates of 99.5, 98.50, and 97.0 for 2-class, 6-class, and 10-class classifications, respectively. Future research may explore the use of computer-aided automated systems to detect diseases in diverse plant species.

 Computer-aided automation systems for the detection of plant diseases represent a challenging and highly impactful research domain in the field of agriculture. Tomatoes, a major and globally significant agricultural commodity, are cultivated in large quantities. This study introduces a novel approach for the automated detection of diseases on tomato leaves, leveraging both classical machine learning methods and deep neural networks for image classification. Specifically, classical learning methods employed the local binary pattern LBP technique for feature extraction, while classification tasks were carried out using extreme learning machines, k-nearest neighborhood kNN, and support vector machines SVM. In contrast, a novel convolutional neural network CNN framework, complete with unique parameters and layers, was utilized for deep learning. The results of this study demonstrate that the proposed approach outperforms state-of-the-art studies in terms of accuracy. The classification process covered various scenarios, including binary classification healthy vs. unhealthy, 6-class classification, and 10-class classification for distinguishing different types of diseases. The findings indicate that the CNN model consistently outperformed classical learning methods, achieving accuracy rates of 99.5, 98.50, and 97.0 for 2-class, 6-class, and 10-class classifications, respectively. Future research may explore the use of computer-aided automated systems to detect diseases in diverse plant species.

 Computer-aided automation systems for the detection of plant diseases represent a challenging and highly impactful research domain in the field of agriculture. Tomatoes, a major and globally significant agricultural commodity, are cultivated in large quantities. This study introduces a novel approach for the automated detection of diseases on tomato leaves, leveraging both classical machine learning methods and deep neural networks for image classification. Specifically, classical learning methods employed the local binary pattern LBP technique for feature extraction, while classification tasks were carried out using extreme learning machines, k-nearest neighborhood kNN, and support vector machines SVM. In contrast, a novel convolutional neural network CNN framework, complete with unique parameters and layers, was utilized for deep learning. The results of this study demonstrate that the proposed approach outperforms state-of-the-art studies in terms of accuracy. The classification process covered various scenarios, including binary classification healthy vs. unhealthy, 6-class classification, and 10-class classification for distinguishing different types of diseases. The findings indicate that the CNN model consistently outperformed classical learning methods, achieving accuracy rates of 99.5, 98.50, and 97.0 for 2-class, 6-class, and 10-class classifications, respectively. Future research may explore the use of computer-aided automated systems to detect diseases in diverse plant species.

 Computer-aided automation systems for the detection of plant diseases represent a challenging and highly impactful research domain in the field of agriculture. Tomatoes, a major and globally significant agricultural commodity, are cultivated in large quantities. This study introduces a novel approach for the automated detection of diseases on tomato leaves, leveraging both classical machine learning methods and deep neural networks for image classification. Specifically, classical learning methods employed the local binary pattern LBP technique for feature extraction, while classification tasks were carried out using extreme learning machines, k-nearest neighborhood kNN, and support vector machines SVM. In contrast, a novel convolutional neural network CNN framework, complete with unique parameters and layers, was utilized for deep learning. The results of this study demonstrate that the proposed approach outperforms state-of-the-art studies in terms of accuracy. The classification process covered various scenarios, including binary classification healthy vs. unhealthy, 6-class classification, and 10-class classification for distinguishing different types of diseases. The findings indicate that the CNN model consistently outperformed classical learning methods, achieving accuracy rates of 99.5, 98.50, and 97.0 for 2-class, 6-class, and 10-class classifications, respectively. Future research may explore the use of computer-aided automated systems to detect diseases in diverse plant species.

Makale detayları

A Novel Approach for Tomato Leaf Disease Classification with Deep Convolutional Neural Networks

Yazarlar:

Gizem Irmak | Ahmet Saygılı

Yayıncı:

Ankara University

Ana Başlıklar:

Artificial Intelligence (Other)

Dergi:

Journal of Agricultural Sciences Vol.30 Issue 2, pp.367-385

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001313943

pISSN:

1300-7580

Automated agriculture

Machine learning in agriculture

Convolutional neural networks in plant pathology

Deep learning in agriculture

Smart farming

Sayı

Makale detayları

A Novel Approach for Tomato Leaf Disease Classification with Deep Convolutional Neural Networks

Yazarlar: Gizem Irmak | Ahmet Saygılı

Yayıncı: Ankara University

Ana Başlıklar: Artificial Intelligence (Other)

Dergi: Journal of Agricultural Sciences Vol.30 Issue 2, pp.367-385

Belge Tipi: Makale

Yayın Tarihi: 3/2024

Belge Dili: İngilizce

Document ID (Org No.): 2001313943

pISSN: 1300-7580

Automated agriculture

Machine learning in agriculture

Convolutional neural networks in plant pathology

Deep learning in agriculture

Smart farming

Sayı

Yazarlar:

Gizem Irmak | Ahmet Saygılı

Yayıncı:

Ankara University

Ana Başlıklar:

Artificial Intelligence (Other)

Dergi:

Journal of Agricultural Sciences Vol.30 Issue 2, pp.367-385

Belge Tipi:

Makale

Yayın Tarihi:

3/2024

Belge Dili:

İngilizce

Document ID (Org No.):

2001313943

pISSN:

1300-7580