Research Article
BibTex RIS Cite

Year 2025, Volume: 8 Issue: 4, 606 - 620

Buse Çalişir , Bihter Daş

https://doi.org/10.35377/saucis...1634387

Abstract

References

  • T. Saranya, C. Deisy, S. Sridevi, and K. S. M. Anbananthen, “A comparative study of deep learning and Internet of Things for precision agriculture,” Eng. Appl. Artif. Intell., vol. 122, p. 106034, 2023.
  • S. N. Benli, Derin evrişimli yapay sinir ağı kullanarak meyve yaprağı hastalık tespiti, M.S. thesis, Fen Bilimleri Enstitüsü, Bilecik Şeyh Edebali Üniversitesi, Bilecik, Turkey, 2021.
  • R. Thangaraj, S. Anandamurugan, P. Pandiyan, and V. K. Kaliappan, “Artificial intelligence in tomato leaf disease detection: A comprehensive review and discussion,” J. Plant Dis. Prot., vol. 129, no. 3, pp. 469–488, 2022.
  • S. K. Upadhyay and A. Kumar, “A novel approach for rice plant diseases classification with a deep convolutional neural network,” Int. J. Inf. Technol., vol. 14, no. 1, pp. 185–199, 2022.
  • Z. Yao and M. Huang, “Deep learning in tropical leaf disease detection: Advantages and applications,” Trop. Plants, vol. tp-0024-0018, pp. 1–11, 2024.
  • L. Li, S. Zhang, and B. Wang, “Plant disease detection and classification by deep learning—A review,” IEEE Access, vol. 9, pp. 56683–56698, 2021.
  • J. Lu, L. Tan, and H. Jiang, “Review on convolutional neural network (CNN) applied to plant leaf disease classification,” Agriculture, vol. 11, no. 8, p. 707, 2021.
  • M. H. Saleem, J. Potgieter, and K. M. Arif, “Plant disease classification: A comparative evaluation of convolutional neural networks and deep learning optimizers,” Plants, vol. 9, no. 10, p. 1319, 2020.
  • V. K. Shrivastava and M. K. Pradhan, “Rice plant disease classification using color features: A machine learning paradigm,” J. Plant Pathol., vol. 103, no. 1, pp. 17–26, 2021.
  • Y. Borhani, J. Khoramdel, and E. Najafi, “A deep learning-based approach for automated plant disease classification using vision transformer,” Sci. Rep., vol. 12, no. 1, p. 11554, 2022.
  • D. Argüeso, A. Picon, U. Irusta, A. Medela, M. G. San-Emeterio, A. Bereciartua, and A. Alvarez-Gila, “Few-shot learning approach for plant disease classification using images taken in the field,” Comput. Electron. Agric., vol. 175, p. 105542, 2020.
  • Y. Zhao, C. Sun, X. Xu, and J. Chen, “RIC-Net: A plant disease classification model based on the fusion of Inception and residual structure and embedded attention mechanism,” Comput. Electron. Agric., vol. 193, p. 106644, 2022.
  • A. Haridasan, J. Thomas, and E. D. Raj, “Deep learning system for paddy plant disease detection and classification,” Environ. Monit. Assess., vol. 195, no. 1, p. 120, 2023.
  • S. U. M. Rao, K. Sreekala, P. S. Rao, N. Shirisha, G. Srinivas, and E. Sreedevi, “Plant disease classification using novel integration of deep learning CNN and graph convolutional networks,” Indones. J. Electr. Eng. Comput. Sci., vol. 36, no. 3, pp. 1721–1730, 2024.
  • C. Topçu and P. Güneş, “Bitki hastalıklarını tespitte derin öğrenme: ResNet modelinin etkinliği,” Anadolu Bil. Meslek Yüksekokulu Derg., vol. 19, no. 69, pp. 31–65, 2024.
  • M. V. Applalanaidu and G. Kumaravelan, “A review of machine learning approaches in plant leaf disease detection and classification,” in Proc. 2021 3rd Int. Conf. Intell. Commun. Technol. Virtual Mobile Network. (ICICV), Feb. 2021, pp. 716–724.
  • Y. Zhao, Z. Zhang, N. Wu, Z. Zhang, and X. Xu, “MAFDE-DN4: Improved few-shot plant disease classification method based on deep nearest neighbor neural network,” Comput. Electron. Agric., vol. 226, p. 109373, 2024.
  • C. K. Sunil, C. D. Jaidhar, and N. Patil, “Tomato plant disease classification using multilevel feature fusion with adaptive channel spatial and pixel attention mechanism,” Expert Syst. Appl., vol. 228, p. 120381, 2023.
  • G. Iglesias, E. Talavera, and A. Díaz-Álvarez, “A survey on GANs for computer vision: Recent research, analysis and taxonomy,” Comput. Sci. Rev., vol. 48, p. 100553, 2023.
  • B. T. Weldemikael, G. Woldetinsae, and G. Neshir, “Generating land gravity anomalies from satellite gravity observations using PIX2PIX GAN image translation,” Appl. Comput. Geosci., vol. 100205, 2024.
  • E. Kardal and V. Nabiyev, “İnsan yüzü modifikasyonu için farklı bir GAN modeli,” Karadeniz Fen Bilimleri Derg., vol. 14, no. 2, pp. 403–418, 2024.
  • A. Abdollahi, B. Pradhan, S. Gite, and A. Alamri, “Building footprint extraction from high resolution aerial images using generative adversarial network (GAN) architecture,” IEEE Access, vol. 8, pp. 209517–209527, 2020.
  • M. Arun, M. Deenadhayalan, R. Deepak, S. Balaraman, and D. Deepak, “Lung cancer detection using ResNet-50 CNN architecture,” in Proc. 2024 2nd Int. Conf. Adv. Inf. Technol. (ICAIT), vol. 1, Jul. 2024, pp. 1–6.
  • Z. Nie, W. Gao, H. Jiang, J. Lu, Z. Lu, and X. Jiang, “Predicting critical flame quenching thickness using machine learning approach with ResNet and ANN,” J. Loss Prev. Process Ind., vol. 92, p. 105448, 2024.
  • C. Guo, Y. Chen, and J. Li, “Radiographic imaging and diagnosis of spinal bone tumors: AlexNet and ResNet for the classification of tumor malignancy,” J. Bone Oncol., vol. 48, p. 100629, 2024.
  • B. Koonce, Convolutional Neural Networks with Swift for TensorFlow: Image Recognition and Dataset Categorization, Berkeley, CA, USA: Apress, 2021. [Online]. Available: https://link.springer.com/content/pdf/10.1007/978-1-4842-6168-2.pdf. Accessed: December 15, 2024.
  • M. R. Hasan, M. I. Fatemi, M. Monirujjaman Khan, M. Kaur, and A. Zaguia, “Comparative analysis of skin cancer (benign vs. malignant) detection using convolutional neural networks,” J. Healthc. Eng., vol. 2021, no. 1, p. 5895156, 2021.
  • S. Toraman and B. Daş, “Evrişimsel sinir ağları kullanılarak normal ve göğüs kanseri hücreleri içeren genomların sınıflandırılması,” Dicle Univ. Muhendis. Fak. Muhendis. Derg., vol. 11, no. 1, pp. 81–90, 2020.
  • Y. Lu, Y. Qiu, Q. Gao, and D. Sun, “Infrared and visible image fusion based on tight frame learning via VGG19 network,” Digit. Signal Process., vol. 131, p. 103745, 2022.
  • S. Gaba, I. Budhiraja, V. Kumar, S. Garg, G. Kaddoum, and M. M. Hassan, “A federated calibration scheme for convolutional neural networks: Models, applications and challenges,” Comput. Commun., vol. 192, pp. 144–162, 2022.
  • K. Kılıç and U. Özcan, “AlexNet architecture optimized for wood defect detection,” Bozok J. Eng. Archit., vol. 2, no. 2, pp. 20–28, 2023.
  • W. Tang, J. Sun, S. Wang, and Y. Zhang, “Review of AlexNet for medical image classification,” EAI Endorsed Trans. e-Learning, vol. 9, Dec. 2023. doi: 10.4108/eetel.4389.
  • B. Taşcı, “An overview on COVID-19 detection algorithm using deep learning,” in Proc. 2021 Innovations in Power and Advanced Computing Technologies (i-PACT), 2021, pp. 1–5.

Classification of Plant Diseases With ResNet-GAN Integration: Comparative Analysis of Machine Learning And Deep Learning Methods

Year 2025, Volume: 8 Issue: 4, 606 - 620

Buse Çalişir , Bihter Daş

https://doi.org/10.35377/saucis...1634387

Abstract

Accurate and effective classification of plant diseases is critical for increasing yield and quality in agricultural production, minimizing economic losses through early detection of diseases, and implementing sustainable agriculture approaches. In this study, an approach for the detection and classification of plant leaf diseases is presented. We compare the performance of machine learning and deep learning-based models, and we use GAN-based data synthesis methods on a dataset we created to improve the model performance. For machine learning methods, ResNet-based feature extraction is performed and XGBoost, Random Forest, SVM, and InceptionV3 models are evaluated. In contrast, AlexNet, VGG16, VGG19, DenseNet, and ResNet models are examined within the scope of deep learning. The study was analyzed in three classes Phytophthora Infestans, Potassium Deficiency, and Healthy, and tested on data obtained from 21 different plant species. According to the model performances obtained, the deep learning-based ResNet model showed the highest success in all performance metrics and achieved 99% accuracy, showing superior performance compared to other methods. In the study, a comprehensive evaluation of multiple classification, GAN-based data synthesis, machine learning, and deep learning models was carried out. A valuable contribution was made to the existing studies in the literature.

Keywords

Plant Disease Classification Leaf Deep Learning Machine Learning Image Processing

References

  • T. Saranya, C. Deisy, S. Sridevi, and K. S. M. Anbananthen, “A comparative study of deep learning and Internet of Things for precision agriculture,” Eng. Appl. Artif. Intell., vol. 122, p. 106034, 2023.
  • S. N. Benli, Derin evrişimli yapay sinir ağı kullanarak meyve yaprağı hastalık tespiti, M.S. thesis, Fen Bilimleri Enstitüsü, Bilecik Şeyh Edebali Üniversitesi, Bilecik, Turkey, 2021.
  • R. Thangaraj, S. Anandamurugan, P. Pandiyan, and V. K. Kaliappan, “Artificial intelligence in tomato leaf disease detection: A comprehensive review and discussion,” J. Plant Dis. Prot., vol. 129, no. 3, pp. 469–488, 2022.
  • S. K. Upadhyay and A. Kumar, “A novel approach for rice plant diseases classification with a deep convolutional neural network,” Int. J. Inf. Technol., vol. 14, no. 1, pp. 185–199, 2022.
  • Z. Yao and M. Huang, “Deep learning in tropical leaf disease detection: Advantages and applications,” Trop. Plants, vol. tp-0024-0018, pp. 1–11, 2024.
  • L. Li, S. Zhang, and B. Wang, “Plant disease detection and classification by deep learning—A review,” IEEE Access, vol. 9, pp. 56683–56698, 2021.
  • J. Lu, L. Tan, and H. Jiang, “Review on convolutional neural network (CNN) applied to plant leaf disease classification,” Agriculture, vol. 11, no. 8, p. 707, 2021.
  • M. H. Saleem, J. Potgieter, and K. M. Arif, “Plant disease classification: A comparative evaluation of convolutional neural networks and deep learning optimizers,” Plants, vol. 9, no. 10, p. 1319, 2020.
  • V. K. Shrivastava and M. K. Pradhan, “Rice plant disease classification using color features: A machine learning paradigm,” J. Plant Pathol., vol. 103, no. 1, pp. 17–26, 2021.
  • Y. Borhani, J. Khoramdel, and E. Najafi, “A deep learning-based approach for automated plant disease classification using vision transformer,” Sci. Rep., vol. 12, no. 1, p. 11554, 2022.
  • D. Argüeso, A. Picon, U. Irusta, A. Medela, M. G. San-Emeterio, A. Bereciartua, and A. Alvarez-Gila, “Few-shot learning approach for plant disease classification using images taken in the field,” Comput. Electron. Agric., vol. 175, p. 105542, 2020.
  • Y. Zhao, C. Sun, X. Xu, and J. Chen, “RIC-Net: A plant disease classification model based on the fusion of Inception and residual structure and embedded attention mechanism,” Comput. Electron. Agric., vol. 193, p. 106644, 2022.
  • A. Haridasan, J. Thomas, and E. D. Raj, “Deep learning system for paddy plant disease detection and classification,” Environ. Monit. Assess., vol. 195, no. 1, p. 120, 2023.
  • S. U. M. Rao, K. Sreekala, P. S. Rao, N. Shirisha, G. Srinivas, and E. Sreedevi, “Plant disease classification using novel integration of deep learning CNN and graph convolutional networks,” Indones. J. Electr. Eng. Comput. Sci., vol. 36, no. 3, pp. 1721–1730, 2024.
  • C. Topçu and P. Güneş, “Bitki hastalıklarını tespitte derin öğrenme: ResNet modelinin etkinliği,” Anadolu Bil. Meslek Yüksekokulu Derg., vol. 19, no. 69, pp. 31–65, 2024.
  • M. V. Applalanaidu and G. Kumaravelan, “A review of machine learning approaches in plant leaf disease detection and classification,” in Proc. 2021 3rd Int. Conf. Intell. Commun. Technol. Virtual Mobile Network. (ICICV), Feb. 2021, pp. 716–724.
  • Y. Zhao, Z. Zhang, N. Wu, Z. Zhang, and X. Xu, “MAFDE-DN4: Improved few-shot plant disease classification method based on deep nearest neighbor neural network,” Comput. Electron. Agric., vol. 226, p. 109373, 2024.
  • C. K. Sunil, C. D. Jaidhar, and N. Patil, “Tomato plant disease classification using multilevel feature fusion with adaptive channel spatial and pixel attention mechanism,” Expert Syst. Appl., vol. 228, p. 120381, 2023.
  • G. Iglesias, E. Talavera, and A. Díaz-Álvarez, “A survey on GANs for computer vision: Recent research, analysis and taxonomy,” Comput. Sci. Rev., vol. 48, p. 100553, 2023.
  • B. T. Weldemikael, G. Woldetinsae, and G. Neshir, “Generating land gravity anomalies from satellite gravity observations using PIX2PIX GAN image translation,” Appl. Comput. Geosci., vol. 100205, 2024.
  • E. Kardal and V. Nabiyev, “İnsan yüzü modifikasyonu için farklı bir GAN modeli,” Karadeniz Fen Bilimleri Derg., vol. 14, no. 2, pp. 403–418, 2024.
  • A. Abdollahi, B. Pradhan, S. Gite, and A. Alamri, “Building footprint extraction from high resolution aerial images using generative adversarial network (GAN) architecture,” IEEE Access, vol. 8, pp. 209517–209527, 2020.
  • M. Arun, M. Deenadhayalan, R. Deepak, S. Balaraman, and D. Deepak, “Lung cancer detection using ResNet-50 CNN architecture,” in Proc. 2024 2nd Int. Conf. Adv. Inf. Technol. (ICAIT), vol. 1, Jul. 2024, pp. 1–6.
  • Z. Nie, W. Gao, H. Jiang, J. Lu, Z. Lu, and X. Jiang, “Predicting critical flame quenching thickness using machine learning approach with ResNet and ANN,” J. Loss Prev. Process Ind., vol. 92, p. 105448, 2024.
  • C. Guo, Y. Chen, and J. Li, “Radiographic imaging and diagnosis of spinal bone tumors: AlexNet and ResNet for the classification of tumor malignancy,” J. Bone Oncol., vol. 48, p. 100629, 2024.
  • B. Koonce, Convolutional Neural Networks with Swift for TensorFlow: Image Recognition and Dataset Categorization, Berkeley, CA, USA: Apress, 2021. [Online]. Available: https://link.springer.com/content/pdf/10.1007/978-1-4842-6168-2.pdf. Accessed: December 15, 2024.
  • M. R. Hasan, M. I. Fatemi, M. Monirujjaman Khan, M. Kaur, and A. Zaguia, “Comparative analysis of skin cancer (benign vs. malignant) detection using convolutional neural networks,” J. Healthc. Eng., vol. 2021, no. 1, p. 5895156, 2021.
  • S. Toraman and B. Daş, “Evrişimsel sinir ağları kullanılarak normal ve göğüs kanseri hücreleri içeren genomların sınıflandırılması,” Dicle Univ. Muhendis. Fak. Muhendis. Derg., vol. 11, no. 1, pp. 81–90, 2020.
  • Y. Lu, Y. Qiu, Q. Gao, and D. Sun, “Infrared and visible image fusion based on tight frame learning via VGG19 network,” Digit. Signal Process., vol. 131, p. 103745, 2022.
  • S. Gaba, I. Budhiraja, V. Kumar, S. Garg, G. Kaddoum, and M. M. Hassan, “A federated calibration scheme for convolutional neural networks: Models, applications and challenges,” Comput. Commun., vol. 192, pp. 144–162, 2022.
  • K. Kılıç and U. Özcan, “AlexNet architecture optimized for wood defect detection,” Bozok J. Eng. Archit., vol. 2, no. 2, pp. 20–28, 2023.
  • W. Tang, J. Sun, S. Wang, and Y. Zhang, “Review of AlexNet for medical image classification,” EAI Endorsed Trans. e-Learning, vol. 9, Dec. 2023. doi: 10.4108/eetel.4389.
  • B. Taşcı, “An overview on COVID-19 detection algorithm using deep learning,” in Proc. 2021 Innovations in Power and Advanced Computing Technologies (i-PACT), 2021, pp. 1–5.
There are 33 citations in total.

Details

Primary Language English
Subjects Computer Software
Journal Section Research Article
Authors

Buse Çalişir 0009-0003-9948-1755

Bihter Daş 0000-0002-2498-3297

Early Pub Date October 13, 2025
Publication Date October 16, 2025
Submission Date February 6, 2025
Acceptance Date August 31, 2025
Published in Issue Year 2025 Volume: 8 Issue: 4

Cite

APA Çalişir, B., & Daş, B. (2025). Classification of Plant Diseases With ResNet-GAN Integration: Comparative Analysis of Machine Learning And Deep Learning Methods. Sakarya University Journal of Computer and Information Sciences, 8(4), 606-620. https://doi.org/10.35377/saucis...1634387
AMA Çalişir B, Daş B. Classification of Plant Diseases With ResNet-GAN Integration: Comparative Analysis of Machine Learning And Deep Learning Methods. SAUCIS. October 2025;8(4):606-620. doi:10.35377/saucis.1634387
Chicago Çalişir, Buse, and Bihter Daş. “Classification of Plant Diseases With ResNet-GAN Integration: Comparative Analysis of Machine Learning And Deep Learning Methods”. Sakarya University Journal of Computer and Information Sciences 8, no. 4 (October 2025): 606-20. https://doi.org/10.35377/saucis. 1634387.
EndNote Çalişir B, Daş B (October 1, 2025) Classification of Plant Diseases With ResNet-GAN Integration: Comparative Analysis of Machine Learning And Deep Learning Methods. Sakarya University Journal of Computer and Information Sciences 8 4 606–620.
IEEE B. Çalişir and B. Daş, “Classification of Plant Diseases With ResNet-GAN Integration: Comparative Analysis of Machine Learning And Deep Learning Methods”, SAUCIS, vol. 8, no. 4, pp. 606–620, 2025, doi: 10.35377/saucis...1634387.
ISNAD Çalişir, Buse - Daş, Bihter. “Classification of Plant Diseases With ResNet-GAN Integration: Comparative Analysis of Machine Learning And Deep Learning Methods”. Sakarya University Journal of Computer and Information Sciences 8/4 (October2025), 606-620. https://doi.org/10.35377/saucis. 1634387.
JAMA Çalişir B, Daş B. Classification of Plant Diseases With ResNet-GAN Integration: Comparative Analysis of Machine Learning And Deep Learning Methods. SAUCIS. 2025;8:606–620.
MLA Çalişir, Buse and Bihter Daş. “Classification of Plant Diseases With ResNet-GAN Integration: Comparative Analysis of Machine Learning And Deep Learning Methods”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 4, 2025, pp. 606-20, doi:10.35377/saucis. 1634387.
Vancouver Çalişir B, Daş B. Classification of Plant Diseases With ResNet-GAN Integration: Comparative Analysis of Machine Learning And Deep Learning Methods. SAUCIS. 2025;8(4):606-20.
 Download Cover Image


INDEXING & ABSTRACTING & ARCHIVING


 31045 31044   ResimLink - Resim Yükle  31047 

31043 28939 28938 34240


29070    The papers in this journal are licensed under a Creative Commons Attribution-NonCommercial 4.0 International License