DOMATES HASTALIĞI TAHMINI IÇIN GERÇEK ZAMANLI UYGULAMA

Yıl 2022, , 90 - 95, 15.04.2022

Doğukan Demirci Esmanur Saraçbaşı Emre Emrah İsmail Uzun Yakup Genç , Kemal Özkan

https://doi.org/10.31796/ogummf.969487

Öz

Hem ülkesel hem de dünyanın önemli bir besin kaynağı olan domates bitkisinin hastalıklarının önceden belirlenmesi önemlidir. Bu çalışmada literatürdeki standart veri setlerine ilaveten toplanan saha verileri kullanarak yaygın olan alternaria ve mildiyö hastalıkların tespiti için bir yöntem önerilmiştir. Derin öğrenmede sıklıkla kullanılan Resnet50 mimarisi ile %97 oranında hastalıklar doğru olarak belirlenmiştir. Geliştirilen mimari mobil cihaza uygulanmış sonuçları çiftçilerle paylaşılmıştır.

Anahtar Kelimeler

Domates hastalığı, Resnet50, Mobil uygulama

Destekleyen Kurum

Tübitak

Proje Numarası

3191179

Kaynakça

• Alfarisy, A.A., Chen, Q., Guo, M., 2018. Deep Learning based classification for paddy pests & diseases recognition. In: Proceedings of 2018 International Conference on Mathematics and Artificial Intelligence, ICMAI ’18, ACM, New York, NY, USA, 2018,pp. 21–25. doi:10.1145/3208788.3208795.
• Brahimi, M., Arsenovic, M., Laraba, S., Sladojevic, S., Boukhalfa, K., Moussaoui, A., 2018. Deep Learning for Plant Diseases: Detection and Saliency Map Visualisation. Springer International Publishing, Cham, pp. 93–117. doi:10.1007/978-3-319-90403-0_6.
• Cui, Y.L., Cheng, P.F., Dong, X.Z., Liu, Z.H., Wang, S.X., 2005. Image processing and extracting color features of greenhouse diseased leaf. Trans. Chinese Soc. Agric. Eng. 21 (S2), 32–35 DOI:10.4236/ojapps.2013.31B006
• Durmus, H., Kirci, M., Gunes, E.O., 2017. Disease detection on the leaves of the tomato plants by using deep learning. In: 2017 6th International Conference on Agro-Geoinformatics, pp. 1-5. DOI: 10.1109/Agro-Geoinformatics.2017.8047016.
• Hughes, D.P., Salathe, M., 2015. An open access repository of images on plant health to enable the development of mobile disease diagnostics, arXiv:1511.08060
• Kumar, A., Vani, M., 2019. Image based tomato leaf disease detection. In: 2019 10th International Conference on Computing, Communication and NetworkingTechnologies (ICCCNT), pp. 1–6.
• Mohanty, S.P., Hughes, D.P., Salathé, M., 2016. Using deep learning for image-based plant disease detection. Front. Plant Sci. 7. doi:10.3389/fpls.2016.01419
• Jameel, S.M., Rehman Gilal, A., Hussain Rizvi, S.S., Rehman, M., Hashmani, M.A., 2020. Practical implications and challenges of multispectral image analysis. In: 2020 3rd International Conference on Computing, Mathematics and Engineering Technologies (iCoMET), pp. 1–5.
• PlantVillage Dataset, 2018, https://www.kaggle.com/emmarex/plantdisease
• Rangarajan, A.K., Purushothaman, R., Ramesh, A., 2018. Tomato crop disease classification using pre-trained deep learning algorithm. Procedia Comput. Sci. 133, 1040–1047. https://doi.org/10.1016/j.procs.2018.07.070
• 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. Agric. Eng. 1, 119–131. doi.org/10.20944/preprints201902.0203.v1
• Srdjan, S., Marko, A., Andras, A., Dubravko, C., Darko, S., 2016. Deep neural networks based recognition of plant diseases by leaf image classification. Computat. Intell. Neurosci. 1–11. https://doi.org/10.1155/2016/3289801.
• Tao, H.W., Zhao, L., Xi, J., Yu, L., Wang, T., 2014. Fruits and vegetables recognition based on color and texture features. Trans. Chinese Soc. Agric. Eng. 30 (16), 305–311.
• Zhou, G.X., Zhang, W.Z., Chen, A.B., He, M.F., 2019. Rapid detection of rice disease based on FCM-KM and Faster R-CNN fusion. IEEE Access 7, 143190–143206. https://doi. org/10.1109/ACCESS.2019.2943454.
• Zhang, J.H., Kong, F.T., Wu, J., Zhai, Z., Wu, S., Cao, S., 2018. Cotton disease identification model based on improved VGG convolutional neural network. J. China Agric. Univ. 23 (11), 161–171.
• Zhang, Y.L., Lai, Z.Y., Jing, X., Lv, J., 2015. Soybean disease detection based on improved BP neural network. J. Agric. Mech. Res. 2, 79–82.