.
Özet Görüntüleme: 257
/ 
PDF İndirme: 130
DOI:
https://doi.org/10.5281/zenodo.8320097Anahtar Kelimeler:
Persimmon, YOLOv8, classificationÖzet
.
Referanslar
Afonso, M., Fonteijn, H., Fiorentin, F.S., Lensink, D., Mooij, M., Faber, N., Wehrens, R., 2020. Tomato fruit detection and counting in greenhouses using deep learning. Frontiers in Plant Science, 11: 571299.
Aktaş, H., 2022. Classification of pistachios based on outer shell color using deep learning. Niğde Ömer Halisdemir University Journal of Engineering Sciences, 11(3): 461–466.
Arserim, M.A., Usta, A., 2023. Object detection by deep learning approach using images taken from unmanned aerial vehicle. Dicle University Journal of Engineering, 14(1): 9-15.
Aydın, S., Taşyürek, M., Öztürk, C., 2021. Air pollution prediction for Central Anatolia Region and its surroundings with deep learning method. European Journal of Science and Technology, Special Issue 29: 168-173.
Bengio, Y., 2009. Learning deep architectures for AI. Foundations and Trends in Machine Learning, 2(1): 1–27.
Çetiner, H., Çetiner, I., 2022. Classification of citrus diseases with convolutional neural network based deep learning model. Bitlis Eren University Journal of Science, 11(1): 352–368.
DeLuna, R.G., Dadios, E.P., Bandala, A.A., Vicerra, R.R.P., 2019. Size classification of tomato fruit using thresholding, machine learning and deep learning techniques. Agrivita, 41(3): 586–596.
Deng, L., Yu, D., 2014. Deep learning: methods and applications. Foundations And Trends® In Signal Processing, 7(3–4): 197-387.
Eren, H.A., Okyay, S., Adar, N., 2021. Adoken: Deep learning based decision support software for MRI. Journal of Engineering Sciences and Design, 9(2): 406–413.
He, K., Zhang, X., Ren, S., Sun, J., 2016. Deep residual learning for image recognition. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition, Conference Proceedings Book, December, pp. 770–778.
Karasulu, B., Yücalar, F., Borandaǧ, E., 2022. A hybrid approach based on deep learning for gender recognition using human ear images. Journal of the Faculty of Engineering and Architecture of Gazi University, 37(3): 1579–1594.
Kirişoğlu, S., Kotan, B., Kotan, K., 2022. Network traffic classification analysis with multi-layer sensor. Düzce University Journal of Science Technology, 10(2): 837–846.
Kuzucu F.C., Kaynaş, K., 2004. Chemical and Physiological changes in persimmons (Diospyros kaki L.) harvested in different time periods. Bahçe, 33(1-2): 17 – 25.
Liakos, K.G., Busato, P., Moshou, D., Pearson, S., Bochtis, D., 2018. Machine learning in agriculture: A review. Sensors (Basel), 18(8):2674.
Mu, Y., Chen, T.S., Ninomiya, S., Guo, W., 2020. Intact detection of highly occluded immature tomatoes on plants using deep learning techniques. Sensors, 20(10): 2984.
Mutha, S.A., Shah, A.M., Ahmed, M.Z., 2021. Maturity detection of tomatoes using deep learning. SN Computer Science, 2(6): 441.
Onur, S., 1990. Trabzon hurması. Derim, 7(1): 4-46.
Özcan, M., 2018. The problems and future of persimmon (Diospyros Kaki L.) cultivation in Turkey. Black Sea Journal of Agriculture, 1(2): 38-43.
Özkan, H.U., Can, H.Z., 2013. Research on the quality properties of persimmon (Diospyros Kaki L.) fruits at different harvest stages. Journal of Agriculture Faculty of Ege University, 50(2): 137-144.
Sa, I., Ge, Z., Dayoub, F., Upcroft, B., Perez, T., Mccool, C., 2016. Deepfruits: A fruit detection system using deep neural networks. Sensors, 16(8).
Seo, D., Cho, B.H., Kim, K., 2012. Development of monitoring robot system for tomato fruits in hydroponic greenhouses. Agronomy, 11(11): 2211.
Simonyan, K., Zisserman, A., 2014. Very Deep Convolutional Networks for Large-Scale Image Recognition. (http://arxiv.org/abs/1409.1556).
Şentürk, T., Latifoğlu, F., 2023. Deep learning based methods for biomedical image segmentation: A review. Dicle University Journal of the Institute of Natural and Applied Science, 12(1): 161–187.
Tan, Z., Karaköse, M., 2022. Comparative analysis for autonomous path planning approaches based on deep reinforcement learning in dynamic environments. Journal of Engineering Science of Adıyaman University, 9(16): 248–262.
Tuzcu, Ö., Yıldırım, B., 2000. Trabzon hurması (Diospyros kaki) ve Yetiştiriciliği. Tübitak Tarp Yayınları, Adana.
Yaman, O., Tuncer, T., 2022. Deep feature extraction and machine learning method for leaf disease detection in plants. Fırat University Journal of Engineering Science, 34(1): 123–132.
Zhang, L., Jia, J., Gui, G., Hao, X., Gao, W., Wang, M., 2018. Deep learning based improved classification system for designing tomato harvesting robot. IEEE Access, 6: 67940-67950
İndir
Yayınlanmış
Nasıl Atıf Yapılır
Sayı
Bölüm
Lisans
Telif Hakkı (c) 2023 ISPEC Tarım Bilimleri Dergisi
Bu çalışma Creative Commons Attribution-NonCommercial 4.0 International License ile lisanslanmıştır.
