Balkan Journal of Electrical and Computer Engineering 2147-284X 2147-284X MUSA YILMAZ 10.17694/bajece.1649068 Software Testing, Verification and Validation Electrical Engineering (Other) Yazılım Testi, Doğrulama ve Validasyon Elektrik Mühendisliği (Diğer) Patates Yaprağı Görüntülerinden Derin Öğrenme Tabanlı Hastalık Tespiti Deep Learning based Disease Detection from Potato Leaf Images https://orcid.org/0000-0002-0698-3525 Öztekin Abdulkerim BATMAN UNIVERSITY https://orcid.org/0000-0002-3234-8728 Almas Kenan BATMAN ÜNİVERSİTESİ 03 30 2025 13 1 19 26 02 28 2025 03 24 2025 Copyright © 2013, Balkan Journal of Electrical and Computer Engineering 2013 Balkan Journal of Electrical and Computer Engineering

Bu tez çalışması, derin öğrenme yöntemleri kullanılarak patates görüntülerinden hastalık tespiti yapmayı amaçlamaktadır. Çalışmada, sağlıklı ve çeşitli patates hastalıklarına ait geniş ve kapsamlı bir görüntü veri seti kullanılmıştır. Farklı Evrişimli Sinir Ağı (CNN) mimarileri ve hibrit modelleri kullanılarak patates hastalıklarını tespit etmek için modeller geliştirilmiştir. Geliştirilen modeller farklı parametreler ve veri kümeleri kullanılarak eğitilmiş ve doğruluk, kesinlik gibi metrikler kullanılarak değerlendirilmiştir. Patates bitkilerinde görülen yaygın hastalıklar (geç yanıklık, erken yanıklık) tespit edilmiş ve görüntü ön işleme teknikleri kullanılarak modellerin performansı artırılmıştır. Bu çalışma, derin öğrenme yöntemlerinin patates hastalıklarının tespitinde etkili bir şekilde kullanılabileceğini göstermeyi ve bu alanda daha önce yapılan çalışmalara katkıda bulunmayı amaçlamaktadır. Çalışmada, dört farklı ResNet modeli ile görüntüler test edilmiş ve çeşitli performans metrikleriyle değerlendirilmiştir. Elde edilen bulguların, patates yetiştiriciliğinde hastalık yönetimi ve verimlilik artışı için önemli bilgiler sağlayabileceği düşünülmektedir. Yapay zeka ile görüntülerden hastalık tespiti yapılması tarım alanında yenilikler yapılmasına önayak olabileceği gibi makine-insan etkileşimine de artırıcı katkı sağlayabilir. Çalışmamız ResNet derin öğrenme modellerinin, görüntü çıkarımı alanında derin öğrenme modellerinin başarısını ve önemini vurgulamaktadır.

This study aims to detect diseases from potato images using deep learning methods. In the study, a large and comprehensive image dataset of healthy and various potato diseases was used. Models were developed to detect potato diseases using different Convolutional Neural Network (CNN) architectures and hybrid models. The developed models were trained using different parameters and datasets and evaluated using metrics such as accuracy and precision. Common diseases seen in potato plants (late blight, early blight) were detected and the performance of the models was increased using image preprocessing techniques. This study aims to show that deep learning methods can be used effectively in the detection of potato diseases and to contribute to previous studies in this field. In the study, images were tested with four different ResNet models and evaluated with various performance metrics. It is thought that the findings obtained can provide important information for disease management and productivity increase in potato cultivation. Disease detection from images with artificial intelligence can lead to innovations in the field of agriculture and can also contribute to machine-human interaction. Our work highlights the success and importance of ResNet deep learning models in the field of image extraction.

 Convolutional Neural Network (CNN) Deep Learning Potato Disease Detection ResNet Evrişimsel Sinir Ağı (CNN) Derin Öğrenme Patates Hastalığı Tespiti ResNet. [1] Saleem, M. H., Potgieter, J., & Arif, K. M. (2019). Plant disease detection and classification by deep learning. Plants, 8(11), 468. [2] Ferentinos, K. P. (2018). Deep learning models for plant disease detection and diagnosis. Computers and electronics in agriculture, 145, 311-318. [3] Shoaib, M., Shah, B., Ei-Sappagh, S., Ali, A., Ullah, A., Alenezi, F., ... & Ali, F. (2023). An advanced deep learning models-based plant disease detection: A review of recent research. Frontiers in Plant Science, 14, 1158933. [4] Ramesh, S., Hebbar, R., Niveditha, M., Pooja, R., Shashank, N., & Vinod, P. V. (2018, April). Plant disease detection using machine learning. In 2018 International conference on design innovations for 3Cs compute communicate control (ICDI3C) (pp. 41-45). IEEE. [5] Shruthi, U., Nagaveni, V., & Raghavendra, B. K. (2019, March). A review on machine learning classification techniques for plant disease detection. In 2019 5th International conference on advanced computing & communication systems (ICACCS) (pp. 281-284). IEEE. [6] Ahmed, I., & Yadav, P. K. (2023). Plant disease detection using machine learning approaches. Expert Systems, 40(5), e13136. [7] Omid, M., Khojastehnazhand, M., & Tabatabaeefar, A. (2010). Estimating volume and mass of citrus fruits by image processing technique. Journal of food Engineering, 100(2), 315-321. [8] Chauhan, K., Jani, S., Thakkar, D., Dave, R., Bhatia, J., Tanwar, S., & Obaidat, M. S. (2020, March). Automated machine learning: The new wave of machine learning. In 2020 2nd International Conference on Innovative Mechanisms for Industry Applications (ICIMIA) (pp. 205-212). IEEE. [9] Blasco, J., Aleixos, N., & Moltó, E. (2003). Machine vision system for automatic quality grading of fruit. Biosystems engineering, 85(4), 415-423. [10] Gómez, D., Salvador, P., Sanz, J., & Casanova, J. L. (2019). Potato yield prediction using machine learning techniques and sentinel 2 data. Remote Sensing, 11(15), 1745. [11] Kurek, J., Niedbała, G., Wojciechowski, T., Świderski, B., Antoniuk, I., Piekutowska, M., ... & Bobran, K. (2023). Prediction of potato (solanum tuberosum l.) yield based on machine learning methods. Agriculture, 13(12), 2259. [12] Gold, K. M., Townsend, P. A., Herrmann, I., & Gevens, A. J. (2020). Investigating potato late blight physiological differences across potato cultivars with spectroscopy and machine learning. Plant Science, 295, 110316. [13] Kadam, S. U., Dhede, V. M., Khan, V. N., Raj, A., & Takale, D. G. (2022). Machine learning methode for automatic potato disease detection. NeuroQuantology, 20(16), 2102-2106. [14] Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and electronics in agriculture, 147, 70-90. [15] Lee, H. S., & Shin, B. S. (2020). Potato detection and segmentation based on mask R-CNN. Journal of Biosystems Engineering, 45, 233-238. [16] Bangari, S., Rachana, P., Gupta, N., Sudi, P. S., & Baniya, K. K. (2022, February). A survey on disease detection of a potato leaf using cnn. In 2022 Second International Conference on Artificial Intelligence and Smart Energy (ICAIS) (pp. 144-149). IEEE. [17] Rozaqi, A. J., & Sunyoto, A. (2020, November). Identification of disease in potato leaves using Convolutional Neural Network (CNN) algorithm. In 2020 3rd International Conference on Information and Communications Technology (ICOIACT) (pp. 72-76). IEEE. [18] Xi, R., Hou, J., & Lou, W. (2020). Potato bud detection with improved faster R-CNN. Transactions of the ASABE, 63(3), 557-569. [19] Gao, W., Xiao, Z., & Bao, T. (2023). Detection and identification of potato-typical diseases based on multidimensional fusion Atrous-CNN and hyperspectral data. Applied Sciences, 13(8), 5023. [20] Singh, M. K., Kumar, J., Vishwakarma, S., Gupta, V. K., Raghuwanshi, G., Joshi, K., ... & Mishra, K. (2024, March). Identification of Potato Leaves Diseases Using CNN with Transfer Learning. In 2024 2nd International Conference on Disruptive Technologies (ICDT) (pp. 6-10). IEEE. [21] Khobragade, P., Shriwas, A., Shinde, S., Mane, A., & Padole, A. (2022, December). Potato leaf disease detection using cnn. In 2022 International Conference on Smart Generation Computing, Communication and Networking (SMART GENCON) (pp. 1-5). IEEE. [22] Griffel, L. M., Delparte, D., & Edwards, J. (2018). Using Support Vector Machines classification to differentiate spectral signatures of potato plants infected with Potato Virus Y. Computers and electronics in agriculture, 153, 318-324. [23] Islam, M., Dinh, A., Wahid, K., & Bhowmik, P. (2017, April). Detection of potato diseases using image segmentation and multiclass support vector machine. In 2017 IEEE 30th canadian conference on electrical and computer engineering (CCECE) (pp. 1-4). IEEE. [24] Ji, Y., Sun, L., Li, Y., Li, J., Liu, S., Xie, X., & Xu, Y. (2019). Non-destructive classification of defective potatoes based on hyperspectral imaging and support vector machine. Infrared Physics & Technology, 99, 71-79. [25] Shen, D., Zhang, S., Ming, W., He, W., Zhang, G., & Xie, Z. (2022). Development of a new machine vision algorithm to estimate potato's shape and size based on support vector machine. Journal of Food Process Engineering, 45(3), e13974. [26] Weiss, K., Khoshgoftaar, T. M., & Wang, D. (2016). A survey of transfer learning. Journal of Big data, 3, 1-40. [27] Kulkarni, U., Meena, S. M., Gurlahosur, S. V., & Mudengudi, U. (2019, September). Classification of cultural heritage sites using transfer learning. In 2019 IEEE fifth international conference on multimedia big data (BigMM) (pp. 391-397). IEEE. [28] Venkatesan, R., & Li, B. (2017). Convolutional neural networks in visual computing: a concise guide. CRC Press. [29] Zhang, A., Lipton, Z. C., Li, M., & Smola, A. J. (2023). Dive into deep learning. Cambridge University Press. [30] LeCun, Y., Bengio, Y., & Hinton, G. (2015). Deep learning. nature, 521(7553), 436-444. [31] Scherer, D., Müller, A., & Behnke, S. (2010, September). Evaluation of pooling operations in convolutional architectures for object recognition. In International conference on artificial neural networks (pp. 92-101). Berlin, Heidelberg: Springer Berlin Heidelberg. [32] He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep residual learning for image recognition. In Proceedings of the IEEE conference on computer vision and pattern recognition (pp. 770-778). [33] Narkhede, S. (2018). Understanding confusion matrix. Towards Data Science, 180(1), 1-12. [34] Powers, D. M. (2020). Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. arXiv preprint arXiv:2010.16061. [35] Bento, C. (2022). ROC analysis and the AUC—Area under the curve. Towards Data Science. [36] Cook, N. R. (2007). Use and misuse of the receiver operating characteristic curve in risk prediction. Circulation, 115(7), 928-935.