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A Comparative Study for Detection of Plant Leaf Diseases Based on Transfer Learning

Year 2025, , 154 - 170, 12.02.2025

Sevde Sazak , Selin Ceren Balsak , Hasan Badem

https://doi.org/10.18016/ksutarimdoga.vi.1571202

Abstract

Early diagnosis of diseases is critical for growing plants in a healthy manner and obtaining productive products. Plant diseases are generally difficult to visually identify by a farmer. However, by using machine learning methods, the process of detecting plant diseases can be realized more quickly and precisely. Hence, it can reduce product losses, reduce costs, and increase overall economic efficiency by increasing agricultural productivity. In this study, classifying plant diseases with artificial intelligence has been aimed by using images obtained from 12 different images of healthy plants and plant leaves infected with 30 different diseases. In the developed system, 5 different Convolutional neural networks (CNN) models including VGG16, VGG19, AlexNet, MobileNetV1, and MobileNetV2, have been used as artificial intelligence models. All models have been trained and compared based on their accuracies. The highest accuracy value of 99.20% has been obtained by The MobileNetV1. The proposed method has been validated through various performance analyses. An artificial intelligence-based web-based application has also been developed for the end-user.

Keywords

Plant Leaf Disease Deep Learning Transfer Learning Convolutional Neural Network Image Classification

References

  • Abade, A., Ferreira, P. A. & Vidal, F. de B. (2021). Plant disease recognition on images using convolutional Neural networks: A systematic review. Comput. Electron. Agric., 185, 106125. DOI: 10.1016/j.compag.2021.106125
  • Bastiaans, L. (1991). The ratio between virtual and visual lesion size as a measure to describe reduction in leaf photosynthesis of rice due to leaf blast. Phytopathology, 81, 611-615.
  • Chellapandi, B., Vijayalakshmi, M., & Chopra, S. (2021). "Comparison of Pre-Trained Models Using Transfer Learning for Detecting Plant Disease", 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India, 2021, pp. 383-387, DOI: 10.1109/ICCCIS51004.2021.9397098
  • Chen, J., Chen, J., Zhang, D., Sun, Y., & Nanehkaran, Y. A. (2020). Using deep transfer learning for image-based plant disease identification. Computers and Electronics in Agriculture, 173, 105393. DOI: 10.1016/j.compag.2020.105393
  • Chohan, M., Khan, A., Chohan, R., Katpar, S. H., & Mahar, M. S. (2020). Plant disease detection using deep learning. Int. J. Recent Technol. Eng., 9(1), 909–914. DOI: 10.35940/ijrte.A2139.059120
  • Chouhan, S.S., Singh, U.P., & Jain, S. (2019). Applications of Computer Vision in Plant Pathology: A Survey. Archives of Computational Methods in Engineering 27, 611–632. DOI: 10.1007/s11831-019-09324-0
  • Clark, A., & The Pillow Developers. Pillow Documentation. Python Imaging Library, 2024. https://python-pillow.org/
  • Cruz, A.C., Luvisi, A., De Bellis, L. & Ampatzidis, Y. (2017). Vision-based plant disease detection system using transfer and deep learning. Proceedings of the ASABE Annual International Meeting, Spokane, WA, USA, 16–19 July. DOI: 10.13031/aim.201700241
  • Dawei, W., Limiao, D., Jiangong, N., Jiyue, G., Hongfei, Z., & Zhongzhi, H. (2019). Recognition pest by image-based transfer learning. Journal of the Science of Food and Agriculture, 99, 4524–4531. DOI: 10.1002/jsfa.9689
  • Erdoğan, C. (2024). Türkiye’de ve Dünya’da Bitki Koruma Ürünlerinin Kullanımının Değerlendirilmesi ve Öneriler. KSU Tarım ve Doğa Dergisi, 27(2), 382-392. DOI: 10.18016/ksutarimdoga.vi.1402605
  • Espejo-Garcia, B., Mylonas, N., Athanasakos, L., Vali, E. & Fountas, S. (2021). Combining generative adversarial networks and agricultural transfer learning for weeds identification. Biosystems Engineering, 203, 79–89. DOI: 10.1016/j.biosystemseng.2021.01.014
  • Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318. DOI: 10.1016/j.compag.2018.01.009
  • Geetharamani, G., & Arun Pandian, J. (2019). Computers Electrical Engineering. 323–338. DOI: 10.1016/j.compeleceng.2019.04.011
  • Harakannanavar, S.S., Rudagi, J.M., Puranikmath, V.I., Siddiqua, A. ve Pramodhini, R. (2022). Plant leaf diseasedetection using computer vision and machine learning algorithms. Global Transitions Proceedings, 3.1, 305–310. DOI: 10.1016/j.gltp.2022.03.016
  • Heltin Genitha, C., Dhinesh, E., & Jagan, A. (2019). Detection of leaf disease using principal component analysisand linear support vector machine. In: Advances in Computing: Proceedings of the International Conference on Advanced Computing (ICoAC). DOI: 10.1109/ICoAC48765.2019.246866
  • Howard, A. G., Sandler, M., Chu, G., Chen, L. H., Chen, W., & Tan, M. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.
  • Ibarra-Pérez, T., Jaramillo-Martínez, R., Correa-Aguado, H. C., Ndjatchi, C., Martínez-Blanco, M. del R., Guerrero-Osuna, H. A., Mirelez-Delgado, F. D., Casas-Flores, J. I. & Reveles-Martínez, R. (2024). A performance comparison of CNN models for bean phenology classification using transfer learning techniques. AgriEngineering, 6(1), 841-857. DOI: 10.3390/agriengineering6010048
  • Jiang, H., Xue, Z.P. &Yan Guo (2020). Research on Plant Leaf Disease Identification Based on Transfer Learning Algorithm. Journal of Physics: Conference Series, 1576 012023. DOI: 10.1088/1742-6596/1576/1/012023
  • Kaggle (2020). New Plant Diseases Dataset. Kaggle Dataset. https://www.kaggle.com/vipoooool/newplant-diseases-dataset
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). “ImageNet Classification with Deep Convolutional Neural Networks.” In: Advances in Neural Information Processing Systems. DOI: 10.1145/3065386
  • Lopes, D.B. & Berger, R.D. (2001). The effects of rust and anthracnose on the photosynthetic competence of diseased bean leaves. Phytopathology, 91, 212-220. DOI: 10.1094/PHYTO.2001.91.2.212
  • Luckey, A. (2012). Assessing youth perceptions and knowledge of agriculture: The impact of participating in an agventure program.
  • Marzougui, M. E., Elleuch, M., & Kherallah, M. (2020). A deep CNN approach for plant disease detection. In: 2020 21st International Arab Conference on Information Technology (ACIT), 1–6. DOI: 10.1109/ACIT50332.2020.9300072
  • Mehedi, M.H.K., Salman Hosain, A.K.M., Ahmed, S., Promita, S.T., Muna, R.K. & Hasan, M. (2022). Plant Leaf Disease Detection using Transfer Learning and Explainable AI. IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada, 2022, pp. 0166-0170, DOI: 10.1109/IEMCON56893.2022.9946513
  • Microsoft. (2024). Visual Studio Code. Retrieved from https://code.visualstudio.com/
  • Mohanty, S.P., Hughes, D.P. & Salathe, M. (2016). Using Deep Learning for Image-Based Plant Disease Detection. Frontiers in Plant Science, 7, 1419-1419. DOI: 10.3389/fpls.2016.01419
  • Murk, C., Khan, A., Katper, S. H., Mahar, M. S. & Bhutto, B. N. (2020). Plant Disease Detection using Deep Learning. International Journal of Recent Technology and Engineering (IJRTE), 8(4), 1621-1625. DOI: 10.35940/ijrte.A2139.059120
  • Nachtigall, L. G., Araujo, R. M. & Nachtigall, G. R. (2016). Classification of apple tree disorders using convolutional neural networks. In: Proceedings of the 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), San Jose, CA, USA, 6–8 November 2016; pp. 472–476. DOI: 10.1109/ICTAI.2016.0078
  • Nigam, S., Jain, R., Marwaha, S., Arora, A., Haque, M. A., Dheeraj, A. & Singh, V. K. (2023). Deep transfer learning model for disease identification in wheat crop. Ecological Informatics, 75, 102068. DOI: 10.1016/j.ecoinf.2023.102068
  • Picon, A., Alvarez-Gila, A., Seitz, M., Ortiz-Barredo, A., Echazarra, J., & Johannes, A. (2019). Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Computers and Electronics in Agriculture, 161, 280–290. DOI: 10.1016/j.compag.2018.04.002
  • Rajasekaran, C., Arul, S., Devi, S., Gowtham, G. & Jeyaram, S. (2020). Turmeric plant diseases detection and classification using artificial intelligence. International Conference on Signal Processing and Communication. DOI: 10.1109/ICCSP48568.2020.9182255
  • Rao, D. S., Ch, R. B., Kiran, V. S., Rajasekhar, N., Srinivas, K., Akshay, P. S., Mohan, G. S., & Bharadwaj, B. L. (2022). Plant Disease Classification Using Deep Bilinear CNN. Intelligent Automation & Soft Computing, 31(1), 161–176. DOI: 10.32604/iasc.2022.017706
  • Ristaino, J.B., Anderson, P.K., Bebber, D.P., Brauman, K.A., Cunniffe, N.J., Fedoroff, N.V., Finegold, C., Garrett,K.A., Gilligan, C.A., Jones, C.M., Martin, M.D., MacDonald, G.K., Neenan, P., Records, A., Schmale, D.G., Tateosian, L. &
  • Wei, Q. (2021). The persistent threat of emerging plant disease pandemics to global food security. Proceedings of the National Academy of Sciences of the United States of America, 118(23). DOI: 10.1073/pnas.2022239118
  • Sagar, A., & Jacob, D. (2021). On Using Transfer Learning For Plant Disease Detection. DOI: 10.1101/2020.05.22.110957
  • Sandler, M., Howard, A., Zhu, M., Zhmoginov, A. & Chen, L.C. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 4510–4520. DOI: 10.1109/CVPR.2018.00474
  • Shahoveisi, F., Taheri Gorji, H., Shahabi, S.M., Hosseinirad, S.A., Markell, S. & Vasef, F. (2023). Application of image processing and transfer learning for the detection of rust disease. Scientific Reports, 13, Article 31942. DOI: 10.1038/s41598-023-31942-9
  • Shrivastava, V.K., Pradhan, M.K., Minz, S. & Thakur, M.P. (2019). Rice Plant Disease Classification Using Transfer Learning of Deep Convolution Neural Network. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42(3/W6). DOI: 10.5194/isprs-archives-XLII-3-W6-631-2019
  • Sibiya, M. & Sumbwanyambe, M. (2019). A computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks. AgriEngineering, 1(1), 119-131. DOI: 10.3390/agriengineering1010009
  • Simonyan, K. & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv Preprint. DOI: 10.48550/arXiv.1409.1556
  • Vallabhajosyula, S., Sistla, V., & Kolli, V. K. K. (2024). A novel hierarchical framework for plant leaf disease detection using residual vision transformer. Heliyon, 10(5), e29912. DOI: 10.1016/j.heliyon.2024.e29912
  • Vangala Rama Vyshnavi ve ark. (2019). Efficient of web development using Python and Flask. International Journal of Recent Research way Aspects, 6(2), 16–19.
  • Wallelign, S., Polceanu, M., & Buche, C. (2018). Soybean plant disease identification using convolutional neural networks. In: The Thirty-First International FLAIRS Conference.
  • Wang, G., Sun, Y. & Wang, J. (2017). Automatic image-based plant disease severity estimation using deep Learning. Computational Intelligence and Neuroscience. DOI: 10.1155/2017/2917536
  • Wasswa, Ş., Tufail, A., De Silva Liyanage, C. ve Awg Haji Mohd Apong, R. A. (2024). Using transfer learning-based plant disease classification and detection for sustainable agriculture. BMC Plant Biology, 24, Article 136. DOI: 10.1186/s12870-024-04825-y
  • Xie, W., Wei, S., Zheng, Z., Jiang, Y. & Yang, D. (2021). Recognition of defective carrots based on deep learning and transfer learning. Food and Bioprocess Technology, 14(7), 1-14. DOI: 10.1007/s11947-021-02653-8
  • Xu, M., Yoon, S., Jeong, Y. ve Park, D. S. (2022). Transfer learning for versatile plant disease recognition with limited data. Frontiers in Plant Science, 13. DOI: 10.3389/fpls.2022.1010981
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Transfer Öğrenme Temelli Bitki Yaprak Hastalıklarının Tespiti İçin Karşılaştırmalı Bir Çalışma

Year 2025, , 154 - 170, 12.02.2025

Sevde Sazak , Selin Ceren Balsak , Hasan Badem

https://doi.org/10.18016/ksutarimdoga.vi.1571202

Abstract

Bitkilerin sağlıklı bir şekilde yetiştirilmesi ve verimli ürün alınması için hastalıkların erken teşhisi kritik öneme sahiptir. Bitki hastalıklarının bir çiftçi tarafından görsel olarak tanımlanması genellikle zordur. Ancak, makine öğrenmesi yöntemleri kullanılarak, bitki hastalıkları tespiti sürecini daha hızlı ve hassas bir şekilde gerçekleştirilebilir. Bu sayede, ürün kayıplarını azaltarak, maliyetlerinin düşürülmesi ve tarımsal üretkenliğin artırılmasıyla genel ekonomik verimliliği yükseltebilmek mümkündür. Bu çalışmada, 12 farklı sağlıklı bitki ve 30 farklı hastalıkla bulaşık bitki yaprağı görüntüleri kullanılarak bitki hastalıklarının yapay zeka ile sınıflandırması amaçlanmıştır. Geliştirilen sistemde yapay zeka modeli olarak VGG16, VGG19, AlexNet, MobileNetV1 ve MobileNetV2 olmak üzere 5 farklı Evrişimli sinir ağı modeli kullanılmıştır. Tüm modeller eğitilmiş ve doğruluk değerleri üzerinden karşılaştırılmıştır. MobileNetV1 üzerinden %99,20 ile en yüksek doğruluk değeri elde edilmiştir. Önerilen yöntem, çeşitli performans analizlerinden geçirilerek doğrulanmıştır. Yapay zeka tabanlı bir web uygulama da son kullanıcı için geliştirilmiştir.

Keywords

Bitki Yaprak Hastalığı Derin Öğrenme Transfer Öğrenme Evrişimli Sinir Ağı Görüntü Sınıflandırma

References

  • Abade, A., Ferreira, P. A. & Vidal, F. de B. (2021). Plant disease recognition on images using convolutional Neural networks: A systematic review. Comput. Electron. Agric., 185, 106125. DOI: 10.1016/j.compag.2021.106125
  • Bastiaans, L. (1991). The ratio between virtual and visual lesion size as a measure to describe reduction in leaf photosynthesis of rice due to leaf blast. Phytopathology, 81, 611-615.
  • Chellapandi, B., Vijayalakshmi, M., & Chopra, S. (2021). "Comparison of Pre-Trained Models Using Transfer Learning for Detecting Plant Disease", 2021 International Conference on Computing, Communication, and Intelligent Systems (ICCCIS), Greater Noida, India, 2021, pp. 383-387, DOI: 10.1109/ICCCIS51004.2021.9397098
  • Chen, J., Chen, J., Zhang, D., Sun, Y., & Nanehkaran, Y. A. (2020). Using deep transfer learning for image-based plant disease identification. Computers and Electronics in Agriculture, 173, 105393. DOI: 10.1016/j.compag.2020.105393
  • Chohan, M., Khan, A., Chohan, R., Katpar, S. H., & Mahar, M. S. (2020). Plant disease detection using deep learning. Int. J. Recent Technol. Eng., 9(1), 909–914. DOI: 10.35940/ijrte.A2139.059120
  • Chouhan, S.S., Singh, U.P., & Jain, S. (2019). Applications of Computer Vision in Plant Pathology: A Survey. Archives of Computational Methods in Engineering 27, 611–632. DOI: 10.1007/s11831-019-09324-0
  • Clark, A., & The Pillow Developers. Pillow Documentation. Python Imaging Library, 2024. https://python-pillow.org/
  • Cruz, A.C., Luvisi, A., De Bellis, L. & Ampatzidis, Y. (2017). Vision-based plant disease detection system using transfer and deep learning. Proceedings of the ASABE Annual International Meeting, Spokane, WA, USA, 16–19 July. DOI: 10.13031/aim.201700241
  • Dawei, W., Limiao, D., Jiangong, N., Jiyue, G., Hongfei, Z., & Zhongzhi, H. (2019). Recognition pest by image-based transfer learning. Journal of the Science of Food and Agriculture, 99, 4524–4531. DOI: 10.1002/jsfa.9689
  • Erdoğan, C. (2024). Türkiye’de ve Dünya’da Bitki Koruma Ürünlerinin Kullanımının Değerlendirilmesi ve Öneriler. KSU Tarım ve Doğa Dergisi, 27(2), 382-392. DOI: 10.18016/ksutarimdoga.vi.1402605
  • Espejo-Garcia, B., Mylonas, N., Athanasakos, L., Vali, E. & Fountas, S. (2021). Combining generative adversarial networks and agricultural transfer learning for weeds identification. Biosystems Engineering, 203, 79–89. DOI: 10.1016/j.biosystemseng.2021.01.014
  • Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318. DOI: 10.1016/j.compag.2018.01.009
  • Geetharamani, G., & Arun Pandian, J. (2019). Computers Electrical Engineering. 323–338. DOI: 10.1016/j.compeleceng.2019.04.011
  • Harakannanavar, S.S., Rudagi, J.M., Puranikmath, V.I., Siddiqua, A. ve Pramodhini, R. (2022). Plant leaf diseasedetection using computer vision and machine learning algorithms. Global Transitions Proceedings, 3.1, 305–310. DOI: 10.1016/j.gltp.2022.03.016
  • Heltin Genitha, C., Dhinesh, E., & Jagan, A. (2019). Detection of leaf disease using principal component analysisand linear support vector machine. In: Advances in Computing: Proceedings of the International Conference on Advanced Computing (ICoAC). DOI: 10.1109/ICoAC48765.2019.246866
  • Howard, A. G., Sandler, M., Chu, G., Chen, L. H., Chen, W., & Tan, M. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint arXiv:1704.04861.
  • Ibarra-Pérez, T., Jaramillo-Martínez, R., Correa-Aguado, H. C., Ndjatchi, C., Martínez-Blanco, M. del R., Guerrero-Osuna, H. A., Mirelez-Delgado, F. D., Casas-Flores, J. I. & Reveles-Martínez, R. (2024). A performance comparison of CNN models for bean phenology classification using transfer learning techniques. AgriEngineering, 6(1), 841-857. DOI: 10.3390/agriengineering6010048
  • Jiang, H., Xue, Z.P. &Yan Guo (2020). Research on Plant Leaf Disease Identification Based on Transfer Learning Algorithm. Journal of Physics: Conference Series, 1576 012023. DOI: 10.1088/1742-6596/1576/1/012023
  • Kaggle (2020). New Plant Diseases Dataset. Kaggle Dataset. https://www.kaggle.com/vipoooool/newplant-diseases-dataset
  • Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). “ImageNet Classification with Deep Convolutional Neural Networks.” In: Advances in Neural Information Processing Systems. DOI: 10.1145/3065386
  • Lopes, D.B. & Berger, R.D. (2001). The effects of rust and anthracnose on the photosynthetic competence of diseased bean leaves. Phytopathology, 91, 212-220. DOI: 10.1094/PHYTO.2001.91.2.212
  • Luckey, A. (2012). Assessing youth perceptions and knowledge of agriculture: The impact of participating in an agventure program.
  • Marzougui, M. E., Elleuch, M., & Kherallah, M. (2020). A deep CNN approach for plant disease detection. In: 2020 21st International Arab Conference on Information Technology (ACIT), 1–6. DOI: 10.1109/ACIT50332.2020.9300072
  • Mehedi, M.H.K., Salman Hosain, A.K.M., Ahmed, S., Promita, S.T., Muna, R.K. & Hasan, M. (2022). Plant Leaf Disease Detection using Transfer Learning and Explainable AI. IEEE 13th Annual Information Technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada, 2022, pp. 0166-0170, DOI: 10.1109/IEMCON56893.2022.9946513
  • Microsoft. (2024). Visual Studio Code. Retrieved from https://code.visualstudio.com/
  • Mohanty, S.P., Hughes, D.P. & Salathe, M. (2016). Using Deep Learning for Image-Based Plant Disease Detection. Frontiers in Plant Science, 7, 1419-1419. DOI: 10.3389/fpls.2016.01419
  • Murk, C., Khan, A., Katper, S. H., Mahar, M. S. & Bhutto, B. N. (2020). Plant Disease Detection using Deep Learning. International Journal of Recent Technology and Engineering (IJRTE), 8(4), 1621-1625. DOI: 10.35940/ijrte.A2139.059120
  • Nachtigall, L. G., Araujo, R. M. & Nachtigall, G. R. (2016). Classification of apple tree disorders using convolutional neural networks. In: Proceedings of the 2016 IEEE 28th International Conference on Tools with Artificial Intelligence (ICTAI), San Jose, CA, USA, 6–8 November 2016; pp. 472–476. DOI: 10.1109/ICTAI.2016.0078
  • Nigam, S., Jain, R., Marwaha, S., Arora, A., Haque, M. A., Dheeraj, A. & Singh, V. K. (2023). Deep transfer learning model for disease identification in wheat crop. Ecological Informatics, 75, 102068. DOI: 10.1016/j.ecoinf.2023.102068
  • Picon, A., Alvarez-Gila, A., Seitz, M., Ortiz-Barredo, A., Echazarra, J., & Johannes, A. (2019). Deep convolutional neural networks for mobile capture device-based crop disease classification in the wild. Computers and Electronics in Agriculture, 161, 280–290. DOI: 10.1016/j.compag.2018.04.002
  • Rajasekaran, C., Arul, S., Devi, S., Gowtham, G. & Jeyaram, S. (2020). Turmeric plant diseases detection and classification using artificial intelligence. International Conference on Signal Processing and Communication. DOI: 10.1109/ICCSP48568.2020.9182255
  • Rao, D. S., Ch, R. B., Kiran, V. S., Rajasekhar, N., Srinivas, K., Akshay, P. S., Mohan, G. S., & Bharadwaj, B. L. (2022). Plant Disease Classification Using Deep Bilinear CNN. Intelligent Automation & Soft Computing, 31(1), 161–176. DOI: 10.32604/iasc.2022.017706
  • Ristaino, J.B., Anderson, P.K., Bebber, D.P., Brauman, K.A., Cunniffe, N.J., Fedoroff, N.V., Finegold, C., Garrett,K.A., Gilligan, C.A., Jones, C.M., Martin, M.D., MacDonald, G.K., Neenan, P., Records, A., Schmale, D.G., Tateosian, L. &
  • Wei, Q. (2021). The persistent threat of emerging plant disease pandemics to global food security. Proceedings of the National Academy of Sciences of the United States of America, 118(23). DOI: 10.1073/pnas.2022239118
  • Sagar, A., & Jacob, D. (2021). On Using Transfer Learning For Plant Disease Detection. DOI: 10.1101/2020.05.22.110957
  • Sandler, M., Howard, A., Zhu, M., Zhmoginov, A. & Chen, L.C. (2018). MobileNetV2: Inverted Residuals and Linear Bottlenecks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 4510–4520. DOI: 10.1109/CVPR.2018.00474
  • Shahoveisi, F., Taheri Gorji, H., Shahabi, S.M., Hosseinirad, S.A., Markell, S. & Vasef, F. (2023). Application of image processing and transfer learning for the detection of rust disease. Scientific Reports, 13, Article 31942. DOI: 10.1038/s41598-023-31942-9
  • Shrivastava, V.K., Pradhan, M.K., Minz, S. & Thakur, M.P. (2019). Rice Plant Disease Classification Using Transfer Learning of Deep Convolution Neural Network. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, 42(3/W6). DOI: 10.5194/isprs-archives-XLII-3-W6-631-2019
  • Sibiya, M. & Sumbwanyambe, M. (2019). A computational procedure for the recognition and classification of maize leaf diseases out of healthy leaves using convolutional neural networks. AgriEngineering, 1(1), 119-131. DOI: 10.3390/agriengineering1010009
  • Simonyan, K. & Zisserman, A. (2014). Very Deep Convolutional Networks for Large-Scale Image Recognition. arXiv Preprint. DOI: 10.48550/arXiv.1409.1556
  • Vallabhajosyula, S., Sistla, V., & Kolli, V. K. K. (2024). A novel hierarchical framework for plant leaf disease detection using residual vision transformer. Heliyon, 10(5), e29912. DOI: 10.1016/j.heliyon.2024.e29912
  • Vangala Rama Vyshnavi ve ark. (2019). Efficient of web development using Python and Flask. International Journal of Recent Research way Aspects, 6(2), 16–19.
  • Wallelign, S., Polceanu, M., & Buche, C. (2018). Soybean plant disease identification using convolutional neural networks. In: The Thirty-First International FLAIRS Conference.
  • Wang, G., Sun, Y. & Wang, J. (2017). Automatic image-based plant disease severity estimation using deep Learning. Computational Intelligence and Neuroscience. DOI: 10.1155/2017/2917536
  • Wasswa, Ş., Tufail, A., De Silva Liyanage, C. ve Awg Haji Mohd Apong, R. A. (2024). Using transfer learning-based plant disease classification and detection for sustainable agriculture. BMC Plant Biology, 24, Article 136. DOI: 10.1186/s12870-024-04825-y
  • Xie, W., Wei, S., Zheng, Z., Jiang, Y. & Yang, D. (2021). Recognition of defective carrots based on deep learning and transfer learning. Food and Bioprocess Technology, 14(7), 1-14. DOI: 10.1007/s11947-021-02653-8
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There are 50 citations in total.

Details

Primary Language Turkish
Subjects Plant Protection (Other)
Journal Section RESEARCH ARTICLE
Authors

Sevde Sazak 0009-0000-9035-3261

Selin Ceren Balsak 0000-0002-2326-7520

Hasan Badem 0000-0002-4262-8774

Early Pub Date January 30, 2025
Publication Date February 12, 2025
Submission Date October 21, 2024
Acceptance Date December 4, 2024
Published in Issue Year 2025

Cite

APA Sazak, S., Balsak, S. C., & Badem, H. (2025). Transfer Öğrenme Temelli Bitki Yaprak Hastalıklarının Tespiti İçin Karşılaştırmalı Bir Çalışma. Kahramanmaraş Sütçü İmam Üniversitesi Tarım Ve Doğa Dergisi, 28(1), 154-170. https://doi.org/10.18016/ksutarimdoga.vi.1571202

21082



2022-JIF = 0.500

2022-JCI = 0.170

Uluslararası Hakemli Dergi (International Peer Reviewed Journal)

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      Yılda 6 sayı yayınlanır. (Published 6 times a year)


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