Derin Öğrenme Tabanlı Mobil Uygulama ile Bitki Hastalığı Tespiti

Yıl 2025, Cilt: 6 Sayı: 1, 20 - 27

Murat Akman , Osman Çam , Bilal Cinar , Durmuş Özdemir

https://doi.org/10.53608/estudambilisim.1531231

Öz

Gelişen teknolojilerle birlikte tarım, daha verimli ve sürdürülebilir hale gelmektedir. Bu doğrultuda, Android platformu için geliştirilen “Bitki Hastalıkları Teşhis Uygulaması”, yapay zeka teknolojilerini kullanarak bitkilerdeki hastalıkları gerçek zamanlı olarak tespit etmeyi hedeflemektedir. Kullanıcılar, akıllı telefonlarının kamerasıyla bitkilerinin fotoğrafını çekerek ya da önceden çekilmiş görüntüleri kullanarak uygulama içerisinde yer alan “görüntü üzerinden tespit” butonu ile hastalık teşhisi gerçekleştirebilirler. Alternatif olarak, “gerçek zamanlı tespit” seçeneği sayesinde canlı kamera görüntüsü üzerinden de teşhis yapılabilmektedir. TensorFlow Lite kütüphanesiyle desteklenen makine öğrenimi modeli, bitki yapraklarında görülen görsel anormallikleri analiz ederek olası hastalıkları tahmin etmektedir. Tarımsal üretimde hastalıkların erken teşhisine katkı sunmayı amaçlayan bu mobil çözüm; tarım sektörü çalışanları, hobi amaçlı bahçecilikle ilgilenenler ve evde bitki yetiştiren bireyler gibi geniş bir kullanıcı kitlesine hitap etmektedir. Böylece uygulama, bitki sağlığının korunmasına ve tarımsal verimliliğin artırılmasına yönelik teknolojik bir katkı sunmaktadır.

Anahtar Kelimeler

Bitki hastalığı, görüntü işleme, makine öğrenmesi, yapay zeka, mobil uygulama

Deep Learning‑Based Mobile Application for Plant Disease Detection

Öz

With the advancement of technology, agriculture has become more efficient and sustainable. In this context, the “Plant Disease Diagnosis Application,” developed for the Android platform, aims to detect plant diseases in real time by utilizing artificial intelligence technologies. Users can take photos of their plants using their smartphone cameras or upload previously captured images to perform disease diagnosis via the “image-based detection” button within the application. Alternatively, the “real-time detection” feature allows users to perform diagnoses directly through the live camera feed. Supported by the TensorFlow Lite library, the machine learning model analyzes visual abnormalities on plant leaves and predicts possible diseases.
This mobile solution, designed to support early diagnosis of plant diseases in agricultural production, appeals to a wide range of users including agricultural professionals, hobbyist gardeners, and individuals who grow plants at home. In doing so, the application offers a technological contribution to protecting plant health and enhancing agricultural productivity.

Anahtar Kelimeler

Plant disease, image processing, machine learning, artifical intelligence, mobile application

Etik Beyan

Bu çalışmanın, özgün bir çalışma olduğunu; çalışmanın hazırlık, veri toplama, eğitim ve bilgilerin sunumu üzere tüm aşamalarından bilimsel etik ilke ve kurallara uygun davrandığımızı beyan ederiz.

Destekleyen Kurum

Yazarlar, bu çalışmayla ilgili herhangi bir çıkar çatışması olmadığını beyan ederler.

Kaynakça

• Albattah, W., Nawaz, M., Javed, A., Masood, M., &Albahli, S. (2022). A novel deep learning method for detection and classification of plant diseases. Complex & Intelligent Systems, 1-18.
• Al-Hiary, H., Bani-Ahmad, S., Reyalat, M., Braik, M., Alrahamneh, Z. (2011). Fast and accurate detection and classification of plant diseases. International Journal of Computer Applications, 17(1), 31-38.
• Sladojevic, S., Arsenovic, M., Anderla, A., Culibrk, D., Stefanovic, D. (2016). Deep neural networks based recognition of plant diseases by leaf image classification. Computational intelligence and neuroscience, 2016(1), 3289801.
• Iqbal, Z., Khan, M. A., Sharif, M., Shah, J. H., ur Rehman, M. H., Javed, K. (2018). An automated detection and classification of citrus plant diseases using image processing techniques: A review. Computers and electronics in agriculture, 153, 12-32.
• Argüeso, D., Picon, A., Irusta, U., Medela, A., San-Emeterio, M. G., Bereciartua, A., Alvarez-Gila, A. (2020). Few-Shot Learning approach for plant disease classification using images taken in the field. Computers and Electronics in Agriculture, 175, 105542
• Saleem, M. H., Potgieter, J., Arif, K. M. (2019). Plant disease detection and classification by deep learning. Plants, 8(11), 468.
• Li, L., Zhang, S., Wang, B. (2021). Plant disease detection and classification by deep learning—a review. IEEE Access, 9, 56683-56698.
• Brahimi, M., Mahmoudi, S., Boukhalfa, K., & Moussaoui, A. (2019, September). Deep interpretable architecture for plant diseases classification. In 2019 signal processing: Algorithms, architectures, arrangements, and applications (SPA) (pp. 111-116). IEEE.
• Xian, T. S., Ngadiran, R. (2021, July). Plant diseases classification using machine learning. In Journal of Physics: Conference Series (Vol. 1962, No. 1, p. 012024). IOP Publishing.
• Upadhyay, S. K., Kumar, A. (2022). A novel approach for rice plant diseases classification with deep convolutional neural network. International Journal of Information Technology, 14(1), 185-199.
• Rumpf, T., Mahlein, A. K., Steiner, U., Oerke, E. C., Dehne, H. W., Plümer, L. (2010). Early detection and classification of plant diseases with support vector machines based on hyperspectral reflectance. Computers and electronics in agriculture, 74(1), 91-99.
• Lee, S. H., Goëau, H., Bonnet, P., Joly, A. (2020). Attention-based recurrent neural network for plant disease classification. Frontiers in Plant Science, 11, 601250.
• Hlaing, C. S., & Zaw, S. M. M. (2018, June). Tomato plant diseases classification using statistical texture feature and color feature. In 2018 IEEE/ACIS 17th International Conference on Computer and Information Science (ICIS) (pp. 439-444). IEEE.
• Aqel, D., Al-Zubi, S., Mughaid, A., Jararweh, Y. (2022). Extreme learning machine for plant diseases classification: a sustainable approach for smart agriculture. Cluster Computing, 25(3), 2007-2020.
• Ali, A. A., Chramcov, B., Jasek, R., Katta, R., Krayem, S. (2021, April). Classification of plant diseases using convolutional neural networks. In Computer science on-line conference (pp. 268-275). Cham: Springer International Publishing.
• Vishwakarma, V. (2019). New Plant Diseases Dataset. Kaggle.
• Nguyen Dat-bit. MobilenetV2 [GitHub repository]. GitHub. First published Oct. 26, 2021; latest commit Nov. 2, 2021.
• Mohanty SP, Hughes DP, Salathé M. (2016). Using deep learning for image-based plant disease detection. Frontiers in Plant Science, 7:1419.
• Ferentinos KP. (2018). Deep learning models for plant disease detection and diagnosis. Computers and Electronics in Agriculture, 145, 311–318. Picon A, et al. (2019). MobileNet-based plant disease classification under field conditions. Computers and Electronics in Agriculture, 162, 125–132.
• Liu B, et al. (2019). Real-time plant disease diagnosis on mobile devices. IEEE Access, 7, 64234–64242.
• MobileNetV2: Inverted Residuals and Linear Bottlenecks Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen; Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018, pp. 4510-4520