Research Article
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Year 2025, Volume: 8 Issue: 4, 592 - 605

Alican Doğan , Cemal Yüksel

https://doi.org/10.35377/saucis...1626178

Abstract

References

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  • S. Kumar, R. Kumar, M. Gupta, and A. J. Obaid, “EEDL-based detection and classification of apple foliar leaf disease,” Proc. - 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1–6, 2024.
  • S. Huo, N. Duan, and Z. Xu, “An improved multi-scale YOLOv8 for apple leaf dense lesion detection and recognition,” IET Image Processing, 2024.
  • J. C. Semenza, J. Rocklöv, and K. Ebi, “Climate Change and Cascading Risks from Infectious Disease,” Infectious Diseases and Therapy, vol. 11, 2021.
  • J. K. Chavda and B. D. Vaniya, “Plant disease identification and classification using machine learning models: A survey,” Proceedings of IEEE Smart Technologies, pp. 281–289, 2023.
  • S. Sharma, A. Kapoor, and A. Jain, “Analysis of plant diseases using image processing techniques,” International Journal of Computer Vision, vol. 128, no. 3, pp. 343–362, 2023.
  • M. Chowdhury, M. O. Rahman, and S. Alam, Proprietor: “A farmer assistance smartphone application with crop planner, crop disease help, agri-expert search, and crop suggestion features,” 15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024, pp. 1–6, 2024.
  • P. Kambale, D. R. B. M., D. Patil, and R. G. N., “Mobile technology for farmers: An overview of agricultural apps,” Asian Journal of Agricultural Extension, Economics & Sociology, vol. 42, no. 9, pp. 75–81, 2024.
  • I. Kunduracioglu, “CNN models approaches for robust classification of apple diseases,” CNN Models Approaches for Robust Classification of Apple Disease, vol. 1, pp. 235–251, 2024.
  • P. Matov, S. Filipova-Petrakieva, M. Lazarova, and I. Taralova, “Apple trees leaves pathologies detection using deep learning convolutional neural network,” 2024 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2024, pp. 1–4, 2024.
  • N. Dembla and R. Yadav, “Advancing foliar disease detection in apple orchards: Evaluating the efficacy of the EfficientNetB3 model in machine learning-based classification,” 2024 International Conference on Advances in Computing Research on Science, Engineering and Technology, ACROSET 2024, pp. 1–4, 2024.
  • Z. Qiu, Y. Xu, C. Chen, W. Zhou, and G. Yu, “Enhanced disease detection for apple leaves with rotating feature extraction,” Agronomy, vol. 14, no. 11, pp. 2602–2602, 2024.
  • R. W. Mwangi, M. Mustafa, K. Charles, I. W. Wagara, and N. Kappel, “Selected emerging and reemerging plant pathogens affecting the food basket: A threat to food security,” Journal of Agriculture and Food Research, vol. 14, 2023.
  • A. Awari, V. Bhokare, H. Daundkar, S. Dhas, and Prof. A. Take, “Plant disease detection and classification,” Indian Scientific Journal of Research in Engineering and Management, vol. 08, no. 10, pp. 1–7, 2024.
  • R. K. Ramya and C. Meenakshi, “A review on ML and DL techniques in detecting plant diseases,” International Journal of Advanced Research in Science, Communication and Technology, pp. 34–40, 2024.
  • D. Navaneetha, T. Maringanti, S. Repaka, V. Basalingwar, Y. Vasavi, and N. V. Manasa, “Analysis of automatic health monitoring in agricultural crops using artificial intelligence methods,” 2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024, pp. 1–4, 2024.
  • S. Arulmurugan, V. Bharathkumar, S. Gokulachandru, and M. Mohamad Yusuf, “Plant guard: AI-enhanced plant diseases detection for sustainable agriculture,” 7th International Conference on Inventive Computation Technologies, ICICT 2024, pp. 726–730, 2024.
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  • P. S. Soltis, L. Teixeira‐Costa, P. Bonnet, and R. G. Nelson, “Advances in plant imaging across scales,” Applications in Plant Sciences, vol. 11, no. 5, 2023.
  • A. Sulaiman, V. Anand, S. Gupta, H. Alshahrani, M. S. Al Reshan, A. Rajab, A. Shaikh, and A. T. Azar, “Sustainable apple disease management using an intelligent fine-tuned transfer learning-based model,” Sustainability, vol. 15, no. 17, 2023.
  • K. Sujatha, K. Gayatri, M. S. Yadav, N. C. Sekhara Rao, and B. S. Rao, “Customized deep CNN for foliar disease prediction based on features extracted from apple tree leaves images,” International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022 - Proceedings, pp. 193–197, 2022.
  • P. Matov, S. Filipova-Petrakieva, M. Lazarova, and I. Taralova, “Apple trees leaves pathologies detection using deep learning convolutional neural network,” 2024 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2024, pp. 1–4, 2024.
  • S. Alqethami, B. Almtanni, W. Alzhrani, and M. Alghamdi, “Disease detection in apple leaves using image processing techniques,” Engineering, Technology & Applied Science Research, vol. 12, no. 2, pp. 8335–8341, 2022.
  • T. Biswas, S. K. Pati, and S. Sarangi, “Plant disease detection,” Prospects of Science, Technology and Applications, pp. 250–256, 2024.
  • S. Gupta, S. Jain, and K. C. Bandhu, “Revolutionizing plant disease detection: A comprehensive review of machine learning techniques and applications,” 2024 International Conference on Advances in Computing Research on Science Engineering and Technology, ACROSET 2024, pp. 1–7, 2024.
  • P. Patwal, R. Chauhan, C. Bhatt, and S. Devliyal, “Automated tomato disease detection and classification using image processing and machine learning for precision agriculture,” Challenges in Information, Communication and Computing Technology, pp. 481–486, 2024.
  • V. V. N. Vinaykumar, A. Babu, P. Bhargavi, and S. Aluvala, “An Inception Architecture with Transfer Learning for Apple Plant Leaf Disease Detection,” in Proceedings of the 2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), SR University, Nov. 2023. DOI: 10.1109/AIKIIE60097.2023.10390058.
  • Khan, A., & Samantaray, S. D. Comparative Analysis of Pre-Trained CNN Architectures for Apple Foliar Disease Classification. Indian Scientific Journal Of Research In Engineering And Management, 2023. https://doi.org/10.55041/ijsrem25943
  • Rawat, P., & Singh, S. K. Apple Leaf Disease Detection Using Transfer Learning. 1–6, 2024. https://doi.org/10.1109/icicacs60521.2024.10498746
  • Babalola, F. O., Kpai̇, N. I., & Toygar, Ö. Elma Yaprağı Hastalıklarının AlexNet Kullanılarak Derin Öğrenme Tabanlı Sınıflandırılması. Computer Science, 2023. https://doi.org/10.53070/bbd.1349566
  • Ashmafee, Md. H., Ahmed, T., Ahmed, S., Hasan, Md. B., Jahan, Mst. N., & Rahman, A. An Efficient Transfer Learning-based Approach for Apple Leaf Disease Classification. European Conference on Cognitive Ergonomics, 2023, 1–6. https://doi.org/10.1109/ECCE57851.2023.10101542
  • Saharan, M. S., & Singh, G.. Leaf Disease Detection Using Transfer Learning (pp. 44–58). Springer Science+Business Media, 2023. https://doi.org/10.1007/978-3-031-47997-7_4
  • M. H. Rabbi, M. J. Hossain, A. Masud, and A. Rahman, “An image recognition approach for crop disease detection in agro-field from infected plant area,” Global Mainstream Journal of Innovation, Engineering & Emerging Technology, pp. 11–18, 2024.
  • I. Ahmed and P. K. Yadav, “A systematic analysis of machine learning and deep learning based approaches for identifying and diagnosing plant diseases,” Sustainable Operations and Computers, vol. 4, pp. 96–104, 2023.
  • T. T. Moharekar and U. Pol, “Detection and classification of plant leaf diseases using convolution neural networks and Streamlit,” www.irjmets.com, 2022.
  • V. Swetha and R. Jayaram, “A novel method for plant leaf malady recognition using machine learning classifiers,” Proceedings of the 3rd International Conference on Electronics and Communication and Aerospace Technology, ICECA 2019, pp. 1360–1365, 2019.
  • Y. Wang, Y. Wei, X. Yu, J. Wang, Y. Zhang, L. Zhang, Y. Wan, and Z. Chen, “A segmentation network for generalized lesion extraction with semantic fusion of transformer with value vector enhancement,” Expert Systems with Applications, vol. 266, p. 126098, 2025.
  • N. Shinde and A. Ambhaikar, “Comprehensive survey on datasets, models, and future directions in plant disease prediction,” International Journal of Image and Graphics, 2024.
  • Y. He, Q. Gao, and Z. Ma, “A crop leaf disease image recognition method based on bilinear residual networks,” Mathematical Problems in Engineering, vol. 2022, p. 2948506, 2022.
  • Y. A. Nanehkaran, D. Zhang, J. Chen, Y. Tian, and N. Al-Nabhan, “Recognition of plant leaf diseases based on computer vision,” Journal of Ambient Intelligence and Humanized Computing, 2020.
  • M. Bhagat and D. Kumar, “Efficient feature selection using BoWs and SURF method for leaf disease identification,” Multimedia Tools and Applications, vol. 82, no. 18, pp. 28187–28211, 2023.
  • A. Saygılı, “The efficiency of transfer learning and data augmentation in lemon leaf image classification,” European Journal of Engineering and Applied Sciences, vol. 6, no. 1, pp. 32–40, 2023.
  • B. Dikici, M. F. Bekçioğullari, H. Açikgöz, And D. Korkmaz, “Performance investigation of pre-trained convolutional neural networks in olive leaf disease classification,” Konya Journal of Engineering Sciences, vol. 10, no. 3, pp. 535–547, 2022.
  • Y. Kurmi and S. Gangwar, “A leaf image localization based algorithm for different crops disease classification,” IPAgr, vol. 9, no. 3, pp. 456–474, 2022.
  • M. Türkoğlu, K. Hanbay, I. Sivrikaya, and D. Hanbay, “Derin evrişimsel sinir ağı kullanılarak kayısı hastalıklarının sınıflandırılması,” Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 9, no. 1, pp. 334–345, 2020.
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  • Rose, A. How do Artificial Neural Networks Work. Journal of Advances in Science and Technology, 20(1), 172–177, 2024. https://doi.org/10.29070/ttrkmm98
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  • Z. Chen, H. Qin, W. Xiang, and Y. Liu, “Reshaping the feature distribution to aid in building extraction,” Computer Vision and Pattern Recognition, p. 27, 2024.
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  • M. Zoric, M. Stula, I. Markic, and M. Braovic, “Transfer learning in building neural network model case study,” 2024 32nd International Conference on Software, Telecommunications and Computer Networks, SoftCOM 2024, pp. 1–6, 2024.
  • V. Gunturu, N. Maiti, B. Toure, P. Kunekar, S. B. Banu, and D. Sahaya Lenin, “Transfer learning in biomedical image classification,” in Proceedings - 3rd International Conference on Advances in Computing, Communication and Applied Informatics, ACCAI 2024, 2024.
  • N. Pateriya, P. Jain, K. P. Niveditha, V. Tiwari, and S. Vishwakarma, “Deep residual networks for image recognition,” International Journal of Innovative Research in Computer and Communication Engineering, vol. 11, no. 09, pp. 10742–10747, 2023.
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Enchancing Apple Plant Leaf Disease Detection Performance with Transfer Learning Methods

Year 2025, Volume: 8 Issue: 4, 592 - 605

Alican Doğan , Cemal Yüksel

https://doi.org/10.35377/saucis...1626178

Abstract

It is very important in agriculture to detect diseases in plants and recovery solutions to produce more crop and to improve efficiency. Enhancements in automated disease detection and analysis can offer significant advantages for taking prompt action, enabling interventions at earlier stages to treat the disease and prevent its spread. This proactive approach could help minimize damage to crop yields. This research is aimed at improving classification performance for apple plant leaf disease detection using transfer learning approaches. The goal is to take necessary precautions for unhealthy apple plants for productive agriculture and healthy food. It discriminates sick apple plants from healthy counterparts by implementing image processing with apple leaf photographs. In this study, traditional machine learning methods are applied for apple plant disease detection task and the classification achievement scores are maximized with transfer learning techniques. The experiments are conducted on a real-world data set including 3164 apple leaf images. As a result, those experiments reveal that transfer learning methods especially EfficientNetB0 has made a significant improvement on classification accuracy for this task. Accuracy and F-score values obtained by transfer learning methods are over 99% which states that they can be considered reliable for plant disease detection tasks.

Keywords

Machine learning Transfer learning Plant disease detection Agriculture

References

  • H. S. Harshitha, J. Nagaraja, and D. Pruthiraja, “Plant disease detection using image processing,” Proc. - 2024 Second International Conference on Advances in Information Technology (ICAIT), pp. 1–6, 2024.
  • M. P. Taranukho, Y. M. Kovalyshyna, and Y. V. Zaika, “Effect of viral infection on the ultrastructural organization of blackcurrant leaf tissue cells,” Mikrobiolohichnyi Zhurnal, vol. 84, no. 5, pp. 38, 2022.
  • R. Yakkundimath, G. Saunshi, and S. Palaiah, “Automatic methods for classification of visual-based viral and bacterial disease symptoms in plants,” Springer, 2021.
  • A. A. Yatoo and A. Sharma, “An indigenous dataset for the detection and classification of apple leaf diseases,” Data in Brief, vol. 53, p. 110165, 2024.
  • S. Kumar, R. Kumar, M. Gupta, and A. J. Obaid, “EEDL-based detection and classification of apple foliar leaf disease,” Proc. - 2024 15th International Conference on Computing Communication and Networking Technologies (ICCCNT), pp. 1–6, 2024.
  • S. Huo, N. Duan, and Z. Xu, “An improved multi-scale YOLOv8 for apple leaf dense lesion detection and recognition,” IET Image Processing, 2024.
  • J. C. Semenza, J. Rocklöv, and K. Ebi, “Climate Change and Cascading Risks from Infectious Disease,” Infectious Diseases and Therapy, vol. 11, 2021.
  • J. K. Chavda and B. D. Vaniya, “Plant disease identification and classification using machine learning models: A survey,” Proceedings of IEEE Smart Technologies, pp. 281–289, 2023.
  • S. Sharma, A. Kapoor, and A. Jain, “Analysis of plant diseases using image processing techniques,” International Journal of Computer Vision, vol. 128, no. 3, pp. 343–362, 2023.
  • M. Chowdhury, M. O. Rahman, and S. Alam, Proprietor: “A farmer assistance smartphone application with crop planner, crop disease help, agri-expert search, and crop suggestion features,” 15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024, pp. 1–6, 2024.
  • P. Kambale, D. R. B. M., D. Patil, and R. G. N., “Mobile technology for farmers: An overview of agricultural apps,” Asian Journal of Agricultural Extension, Economics & Sociology, vol. 42, no. 9, pp. 75–81, 2024.
  • I. Kunduracioglu, “CNN models approaches for robust classification of apple diseases,” CNN Models Approaches for Robust Classification of Apple Disease, vol. 1, pp. 235–251, 2024.
  • P. Matov, S. Filipova-Petrakieva, M. Lazarova, and I. Taralova, “Apple trees leaves pathologies detection using deep learning convolutional neural network,” 2024 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2024, pp. 1–4, 2024.
  • N. Dembla and R. Yadav, “Advancing foliar disease detection in apple orchards: Evaluating the efficacy of the EfficientNetB3 model in machine learning-based classification,” 2024 International Conference on Advances in Computing Research on Science, Engineering and Technology, ACROSET 2024, pp. 1–4, 2024.
  • Z. Qiu, Y. Xu, C. Chen, W. Zhou, and G. Yu, “Enhanced disease detection for apple leaves with rotating feature extraction,” Agronomy, vol. 14, no. 11, pp. 2602–2602, 2024.
  • R. W. Mwangi, M. Mustafa, K. Charles, I. W. Wagara, and N. Kappel, “Selected emerging and reemerging plant pathogens affecting the food basket: A threat to food security,” Journal of Agriculture and Food Research, vol. 14, 2023.
  • A. Awari, V. Bhokare, H. Daundkar, S. Dhas, and Prof. A. Take, “Plant disease detection and classification,” Indian Scientific Journal of Research in Engineering and Management, vol. 08, no. 10, pp. 1–7, 2024.
  • R. K. Ramya and C. Meenakshi, “A review on ML and DL techniques in detecting plant diseases,” International Journal of Advanced Research in Science, Communication and Technology, pp. 34–40, 2024.
  • D. Navaneetha, T. Maringanti, S. Repaka, V. Basalingwar, Y. Vasavi, and N. V. Manasa, “Analysis of automatic health monitoring in agricultural crops using artificial intelligence methods,” 2nd IEEE International Conference on Data Science and Network Security, ICDSNS 2024, pp. 1–4, 2024.
  • S. Arulmurugan, V. Bharathkumar, S. Gokulachandru, and M. Mohamad Yusuf, “Plant guard: AI-enhanced plant diseases detection for sustainable agriculture,” 7th International Conference on Inventive Computation Technologies, ICICT 2024, pp. 726–730, 2024.
  • A. G. Khan, R. Javed, A. M. Malik, A. F. Mirza, S. Aslam, A. Shabbir, and H. Tauseef, “Early detection and classification of apple leaf diseases using deep learning technique,” Deleted Journal, vol. 4, no. 3, 2024.
  • P. S. Soltis, L. Teixeira‐Costa, P. Bonnet, and R. G. Nelson, “Advances in plant imaging across scales,” Applications in Plant Sciences, vol. 11, no. 5, 2023.
  • A. Sulaiman, V. Anand, S. Gupta, H. Alshahrani, M. S. Al Reshan, A. Rajab, A. Shaikh, and A. T. Azar, “Sustainable apple disease management using an intelligent fine-tuned transfer learning-based model,” Sustainability, vol. 15, no. 17, 2023.
  • K. Sujatha, K. Gayatri, M. S. Yadav, N. C. Sekhara Rao, and B. S. Rao, “Customized deep CNN for foliar disease prediction based on features extracted from apple tree leaves images,” International Interdisciplinary Humanitarian Conference for Sustainability, IIHC 2022 - Proceedings, pp. 193–197, 2022.
  • P. Matov, S. Filipova-Petrakieva, M. Lazarova, and I. Taralova, “Apple trees leaves pathologies detection using deep learning convolutional neural network,” 2024 IEEE International Conference on Omni-Layer Intelligent Systems, COINS 2024, pp. 1–4, 2024.
  • S. Alqethami, B. Almtanni, W. Alzhrani, and M. Alghamdi, “Disease detection in apple leaves using image processing techniques,” Engineering, Technology & Applied Science Research, vol. 12, no. 2, pp. 8335–8341, 2022.
  • T. Biswas, S. K. Pati, and S. Sarangi, “Plant disease detection,” Prospects of Science, Technology and Applications, pp. 250–256, 2024.
  • S. Gupta, S. Jain, and K. C. Bandhu, “Revolutionizing plant disease detection: A comprehensive review of machine learning techniques and applications,” 2024 International Conference on Advances in Computing Research on Science Engineering and Technology, ACROSET 2024, pp. 1–7, 2024.
  • P. Patwal, R. Chauhan, C. Bhatt, and S. Devliyal, “Automated tomato disease detection and classification using image processing and machine learning for precision agriculture,” Challenges in Information, Communication and Computing Technology, pp. 481–486, 2024.
  • V. V. N. Vinaykumar, A. Babu, P. Bhargavi, and S. Aluvala, “An Inception Architecture with Transfer Learning for Apple Plant Leaf Disease Detection,” in Proceedings of the 2023 International Conference on Ambient Intelligence, Knowledge Informatics and Industrial Electronics (AIKIIE), SR University, Nov. 2023. DOI: 10.1109/AIKIIE60097.2023.10390058.
  • Khan, A., & Samantaray, S. D. Comparative Analysis of Pre-Trained CNN Architectures for Apple Foliar Disease Classification. Indian Scientific Journal Of Research In Engineering And Management, 2023. https://doi.org/10.55041/ijsrem25943
  • Rawat, P., & Singh, S. K. Apple Leaf Disease Detection Using Transfer Learning. 1–6, 2024. https://doi.org/10.1109/icicacs60521.2024.10498746
  • Babalola, F. O., Kpai̇, N. I., & Toygar, Ö. Elma Yaprağı Hastalıklarının AlexNet Kullanılarak Derin Öğrenme Tabanlı Sınıflandırılması. Computer Science, 2023. https://doi.org/10.53070/bbd.1349566
  • Ashmafee, Md. H., Ahmed, T., Ahmed, S., Hasan, Md. B., Jahan, Mst. N., & Rahman, A. An Efficient Transfer Learning-based Approach for Apple Leaf Disease Classification. European Conference on Cognitive Ergonomics, 2023, 1–6. https://doi.org/10.1109/ECCE57851.2023.10101542
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  • M. H. Rabbi, M. J. Hossain, A. Masud, and A. Rahman, “An image recognition approach for crop disease detection in agro-field from infected plant area,” Global Mainstream Journal of Innovation, Engineering & Emerging Technology, pp. 11–18, 2024.
  • I. Ahmed and P. K. Yadav, “A systematic analysis of machine learning and deep learning based approaches for identifying and diagnosing plant diseases,” Sustainable Operations and Computers, vol. 4, pp. 96–104, 2023.
  • T. T. Moharekar and U. Pol, “Detection and classification of plant leaf diseases using convolution neural networks and Streamlit,” www.irjmets.com, 2022.
  • V. Swetha and R. Jayaram, “A novel method for plant leaf malady recognition using machine learning classifiers,” Proceedings of the 3rd International Conference on Electronics and Communication and Aerospace Technology, ICECA 2019, pp. 1360–1365, 2019.
  • Y. Wang, Y. Wei, X. Yu, J. Wang, Y. Zhang, L. Zhang, Y. Wan, and Z. Chen, “A segmentation network for generalized lesion extraction with semantic fusion of transformer with value vector enhancement,” Expert Systems with Applications, vol. 266, p. 126098, 2025.
  • N. Shinde and A. Ambhaikar, “Comprehensive survey on datasets, models, and future directions in plant disease prediction,” International Journal of Image and Graphics, 2024.
  • Y. He, Q. Gao, and Z. Ma, “A crop leaf disease image recognition method based on bilinear residual networks,” Mathematical Problems in Engineering, vol. 2022, p. 2948506, 2022.
  • Y. A. Nanehkaran, D. Zhang, J. Chen, Y. Tian, and N. Al-Nabhan, “Recognition of plant leaf diseases based on computer vision,” Journal of Ambient Intelligence and Humanized Computing, 2020.
  • M. Bhagat and D. Kumar, “Efficient feature selection using BoWs and SURF method for leaf disease identification,” Multimedia Tools and Applications, vol. 82, no. 18, pp. 28187–28211, 2023.
  • A. Saygılı, “The efficiency of transfer learning and data augmentation in lemon leaf image classification,” European Journal of Engineering and Applied Sciences, vol. 6, no. 1, pp. 32–40, 2023.
  • B. Dikici, M. F. Bekçioğullari, H. Açikgöz, And D. Korkmaz, “Performance investigation of pre-trained convolutional neural networks in olive leaf disease classification,” Konya Journal of Engineering Sciences, vol. 10, no. 3, pp. 535–547, 2022.
  • Y. Kurmi and S. Gangwar, “A leaf image localization based algorithm for different crops disease classification,” IPAgr, vol. 9, no. 3, pp. 456–474, 2022.
  • M. Türkoğlu, K. Hanbay, I. Sivrikaya, and D. Hanbay, “Derin evrişimsel sinir ağı kullanılarak kayısı hastalıklarının sınıflandırılması,” Bitlis Eren Üniversitesi Fen Bilimleri Dergisi, vol. 9, no. 1, pp. 334–345, 2020.
  • Cybenko, G. Neural networks in computational science and engineering. 3(1), 36–42, 1996. https://doi.org/10.1109/99.486759
  • Rose, A. How do Artificial Neural Networks Work. Journal of Advances in Science and Technology, 20(1), 172–177, 2024. https://doi.org/10.29070/ttrkmm98
  • Haug, C. Convolutional Neural Networks (pp. 139–176). Apress eBooks, 2022. https://doi.org/10.1007/978-1-4842-8835-1_3
  • S. A. Wagle and R. Harikrishnan, “Comparison of plant leaf classification using modified alexnet and support vector machine,” Traitement Du Signal, vol. 38, no. 1, pp. 79–87, 2021.
  • Z. Chen, H. Qin, W. Xiang, and Y. Liu, “Reshaping the feature distribution to aid in building extraction,” Computer Vision and Pattern Recognition, p. 27, 2024.
  • L. Torrey and J. Shavlik, “Transfer learning,” in 978-1-60566-766-9.CH011, pp. 242–264, 2010.
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There are 63 citations in total.

Details

Primary Language English
Subjects Software Engineering (Other)
Journal Section Research Article
Authors

Alican Doğan 0000-0002-0553-2888

Cemal Yüksel 0000-0003-2722-5114

Early Pub Date October 13, 2025
Publication Date October 16, 2025
Submission Date January 24, 2025
Acceptance Date September 1, 2025
Published in Issue Year 2025 Volume: 8 Issue: 4

Cite

APA Doğan, A., & Yüksel, C. (2025). Enchancing Apple Plant Leaf Disease Detection Performance with Transfer Learning Methods. Sakarya University Journal of Computer and Information Sciences, 8(4), 592-605. https://doi.org/10.35377/saucis...1626178
AMA Doğan A, Yüksel C. Enchancing Apple Plant Leaf Disease Detection Performance with Transfer Learning Methods. SAUCIS. October 2025;8(4):592-605. doi:10.35377/saucis.1626178
Chicago Doğan, Alican, and Cemal Yüksel. “Enchancing Apple Plant Leaf Disease Detection Performance With Transfer Learning Methods”. Sakarya University Journal of Computer and Information Sciences 8, no. 4 (October 2025): 592-605. https://doi.org/10.35377/saucis. 1626178.
EndNote Doğan A, Yüksel C (October 1, 2025) Enchancing Apple Plant Leaf Disease Detection Performance with Transfer Learning Methods. Sakarya University Journal of Computer and Information Sciences 8 4 592–605.
IEEE A. Doğan and C. Yüksel, “Enchancing Apple Plant Leaf Disease Detection Performance with Transfer Learning Methods”, SAUCIS, vol. 8, no. 4, pp. 592–605, 2025, doi: 10.35377/saucis...1626178.
ISNAD Doğan, Alican - Yüksel, Cemal. “Enchancing Apple Plant Leaf Disease Detection Performance With Transfer Learning Methods”. Sakarya University Journal of Computer and Information Sciences 8/4 (October2025), 592-605. https://doi.org/10.35377/saucis. 1626178.
JAMA Doğan A, Yüksel C. Enchancing Apple Plant Leaf Disease Detection Performance with Transfer Learning Methods. SAUCIS. 2025;8:592–605.
MLA Doğan, Alican and Cemal Yüksel. “Enchancing Apple Plant Leaf Disease Detection Performance With Transfer Learning Methods”. Sakarya University Journal of Computer and Information Sciences, vol. 8, no. 4, 2025, pp. 592-05, doi:10.35377/saucis. 1626178.
Vancouver Doğan A, Yüksel C. Enchancing Apple Plant Leaf Disease Detection Performance with Transfer Learning Methods. SAUCIS. 2025;8(4):592-605.
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