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DOMATES HASTALIĞI TAHMINI IÇIN GERÇEK ZAMANLI UYGULAMA

Year 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

Abstract

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.

Keywords

Domates hastalığı Resnet50 Mobil uygulama

Supporting Institution

Tübitak

Project Number

3191179

References

  • 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.

Year 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

Abstract

Project Number

3191179

References

  • 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.
There are 16 citations in total.

Details

Primary Language Turkish
Subjects Computer Software
Journal Section Research Articles
Authors

Doğukan Demirci This is me 0000-0001-8422-4244

Esmanur Saraçbaşı This is me 0000-0001-9022-7476

Emre Emrah This is me 0000-0001-8422-4244

İsmail Uzun This is me 0000-0002-1261-0856

Yakup Genç 0000-0002-6952-6735

Kemal Özkan 0000-0003-2252-2128

Project Number 3191179
Publication Date April 15, 2022
Acceptance Date March 7, 2022
Published in Issue Year 2022

Cite

APA Demirci, D., Saraçbaşı, E., Emrah, E., Uzun, İ., et al. (2022). DOMATES HASTALIĞI TAHMINI IÇIN GERÇEK ZAMANLI UYGULAMA. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi, 30(1), 90-95. https://doi.org/10.31796/ogummf.969487
AMA Demirci D, Saraçbaşı E, Emrah E, Uzun İ, Genç Y, Özkan K. DOMATES HASTALIĞI TAHMINI IÇIN GERÇEK ZAMANLI UYGULAMA. ESOGÜ Müh Mim Fak Derg. April 2022;30(1):90-95. doi:10.31796/ogummf.969487
Chicago Demirci, Doğukan, Esmanur Saraçbaşı, Emre Emrah, İsmail Uzun, Yakup Genç, and Kemal Özkan. “DOMATES HASTALIĞI TAHMINI IÇIN GERÇEK ZAMANLI UYGULAMA”. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi 30, no. 1 (April 2022): 90-95. https://doi.org/10.31796/ogummf.969487.
EndNote Demirci D, Saraçbaşı E, Emrah E, Uzun İ, Genç Y, Özkan K (April 1, 2022) DOMATES HASTALIĞI TAHMINI IÇIN GERÇEK ZAMANLI UYGULAMA. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 30 1 90–95.
IEEE D. Demirci, E. Saraçbaşı, E. Emrah, İ. Uzun, Y. Genç, and K. Özkan, “DOMATES HASTALIĞI TAHMINI IÇIN GERÇEK ZAMANLI UYGULAMA”, ESOGÜ Müh Mim Fak Derg, vol. 30, no. 1, pp. 90–95, 2022, doi: 10.31796/ogummf.969487.
ISNAD Demirci, Doğukan et al. “DOMATES HASTALIĞI TAHMINI IÇIN GERÇEK ZAMANLI UYGULAMA”. Eskişehir Osmangazi Üniversitesi Mühendislik ve Mimarlık Fakültesi Dergisi 30/1 (April 2022), 90-95. https://doi.org/10.31796/ogummf.969487.
JAMA Demirci D, Saraçbaşı E, Emrah E, Uzun İ, Genç Y, Özkan K. DOMATES HASTALIĞI TAHMINI IÇIN GERÇEK ZAMANLI UYGULAMA. ESOGÜ Müh Mim Fak Derg. 2022;30:90–95.
MLA Demirci, Doğukan et al. “DOMATES HASTALIĞI TAHMINI IÇIN GERÇEK ZAMANLI UYGULAMA”. Eskişehir Osmangazi Üniversitesi Mühendislik Ve Mimarlık Fakültesi Dergisi, vol. 30, no. 1, 2022, pp. 90-95, doi:10.31796/ogummf.969487.
Vancouver Demirci D, Saraçbaşı E, Emrah E, Uzun İ, Genç Y, Özkan K. DOMATES HASTALIĞI TAHMINI IÇIN GERÇEK ZAMANLI UYGULAMA. ESOGÜ Müh Mim Fak Derg. 2022;30(1):90-5.

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