Detection of Damage Caused by Some Vineyard Pests with the YOLOv8x Model Using Deep Learning and Object Detection Methods
DOI:
https://doi.org/10.24925/turjaf.v12i3.386-396.6510Keywords:
Deep Learning, Object Detection, YOLOv8x, Vineyard Pests, VineyardAbstract
It is very important to control, monitor and maintain the vineyards correctly and on time. Excessive use of pesticides in combating vineyard pests endangers human health and causes environmental pollution. In addition, excessive use of pesticides causes an increase in operating expenses when considered from an economic perspective. Therefore, timely diagnosis of pests and their damage in the vineyard is very important. One of the methods that helps ensure timely detection is deep learning. This study was carried out to detect the damage caused by some vineyard pests (Grapevine moth, thrips, vineyard leaf scab and two-spotted spider mite) on leaves and fruit parts with the YOLOv8x model, which is a deep learning algorithm. A dataset consisting of 7 different classes and 3500 images was generated. The generated dataset; Trained with YOLOv8(n/s/m/l/x) models. As a result of the training, YOLOv8x model performance values are as follows; It gave results as mAP0.5, mAP0.5-0.95, Precision, Recall, 0.926, 0.648, 0.892 and 0.903. The same dataset was trained with YOLOv7, DETR and RTMDet models and performance comparisons were made with the YOLOv8x model. As a result of the comparison, the YOLOv8x model was the one that best detected the damage caused by the mentioned pests in the vineyards.
References
Altaş, Z., Özgüven, M. M., & Dilmaç, M. (2021). Görüntü işleme teknikleri ile bağ yaprak uyuzu hasarının belirlenmesi. Gaziosmanpaşa Bilimsel Araştırma Dergisi (GBAD), Volume 10:3:77-87
Altaş, Z., Özgüven, M. M., & Adem, K. (2023). Automatic detection and classification of some vineyard diseases with Faster R-CNN model. Turkish Journal of Agriculture - Food Science and Technology, 11(1), 97–103. https://doi.org/10.24925/turjaf.v11i1.97-103.5665
Bai, R., Shen, F., Wang, M., Lu, J., & Zhang, Z. (2023). Improving detection capabilities of YOLOv8-n for small objects in remote sensing imagery: Towards Better Precision with Simplified Model Complexity. (2023) DOI: https://doi.org/10.21203/rs.3.rs-3085871/v1
Barbedo, J. G. A. (2019). Plant disease identification from individual lesions and spots using deep learning. Biosystems Engineering, 180:96-107
Cai, H., & Jiang, J. (2023). An improved plant disease detection method based on YOLOv5. 2023 15th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC) | 979-8-3503-2617-8/23/$31.00 ©2023 IEEE | DOI: 10.1109/IHMSC58761.2023.00062
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., & Zagoruyko, S. (2020). End-to-end object detection with transformers. Computer Vision and Pattern Recognition (cs.CV) 2020. https://doi.org/10.48550/arXiv.2005.12872
Feng, C., Zhong, Y., Gao, Y., Scott, M. R., & Huang, W. (2021). TOOD: Task-Aligned one-stage object detection. In Proceedings of the 2021 IEEE International Conference on Computer Vision (ICCV), Montreal, QC, Canada, 10–17 October 2021; pp. 3490–3499
Geetharamani, G., & Arun, P. J. (2019). Identification of plant leaf diseases using a nine-layer deep convolutional neural network. Computers and Electrical Engineering, 76:323-338
Jocher, G., Chaurasia, A., & Qiu, J. (2023). “YOLO by Ultralytics.” https://github.com/ultralytics/ultralytics. (Erişim tarihi: 25.10.2023)
Kang, J., Zhao, L., Wang, K., & Zhang, K. (2023). Research on an improved YOLOv8 image segmentation model for crop pest. Advances in Computer, Signals and Systems Clausius Scientific Press, Canada. DOI: 10.23977/acss.2023.070301. ISSN 2371-8838 Vol. 7 Num. 3
Kaplan, C., Zeki, C., Çakırbay, F., Çetin, G., Öztürk, N., Altındişli, Ö., & Kahveci, Y. (2008). Meyve ve bağ zararlıları. Zirai Mücadele Teknik Talimatları Cilt IV. Tarımsal Araştırmalar ve Politikalar Genel Müdürlüğü Bitki Sağlığı Araştırmaları Daire Başkanlığı
Karabat, S. (2014). Türkiye ve dünya bağcılığı. Apelasyon, ISSN:2149-4908. http://apelasyon.com/Yazi/33-dunya-ve-turkiye-bagciligi (Erişim Tarihi: 08.12.2023)
Leng, S., Musha, Y., Yang, Y., & Feng, G. (2023). CEMLB-YOLO: Efficient detection model of maize leaf blight in complex field environments. Appl. Sci. 2023, 13, 9285. https:// doi.org/10.3390/app13169285
Li, X., Wang, W., Wu, L., Chen, S., Hu, X., Li, J., Tang, J., & Yang, J. (2020). Generalized Focal Loss: Learning qualified and distributed bounding boxes for dense object detection. arXiv 2020, arXiv:2006.04388
Li, S., Liu, S., Cai, Z., Liu, Y., Chen, G., & Guoqing, G. (2023). TC-YOLOv5: rapid detection of floating debris on raspberry Pi 4B. Journal of Real-Time Image Processing (2023) 20:17. https://doi.org/10.1007/s11554-023-01265-z
Lyu, C., Zhang, W., Huang, H., Zhou, Y., Wang, Y., Liu, Y., Zhang, S., & Che, K. (2022). RTMDet: An empirical study of designing real-time object detectors. Computer Vision and Pattern Recognition. https://doi.org/10.48550/arXiv.2212.07784
Mahesh, T. Y., & Mathew, M. P. (2023). Detection of bacterial spot disease in bell pepper plant using YOLOv3, IETE Journal of Research, DOI: 10.1080/03772063.2023.2176367
Orchi, H., Sadik, M., Khaldoun, M., & Sabir, E. (2023). Real-time detection of crop leaf diseases using enhanced YOLOv8 algorithm. International Wireless Communications and Mobile Computing (IWCMC) 2023 | 979-8-3503-3339-8/23/$31.00 ©2023 IEEE | DOI: 10.1109/IWCMC58020.2023.10182573
Ozguven, M., & Adem, K. (2019). Automatic detection and classification of leaf spot disease in sugar beet using deep learning algorithms. Physica A: Statistical Mechanics and its Applications, 535(2019), 122537. doi: 10.1016/j.physa.2019.122537
Ozguven, M. M., & Altas, Z. (2022). A new approach to detect mildew disease on cucumber (Pseudoperonospora cubensis) leaves with ımage processing. J Plant Pathol. https://doi.org/10.1007/s42161-022-01178-z
Ozguven, M. M., & Yanar, Y. (2022). The technology uses in the determination of sugar beet diseases. In: Misra, V., Srivastava, S., Mall, A.K. (eds) Sugar Beet Cultivation, Management and Processing. Springer, Singapore. https://doi.org/10.1007/978-981-19-2730-0_30
Ozguven, M. M. (2023). The digital age in agriculture. CRC Press Taylor & Francis Group LLC. ISBN 978-103-23-8577-8
Rashid, J., Khan, I., Ali, G., Rehman, S. U., Alturise, F., & Alkhalifah, T. (2023). Real-time multiple guava leaf disease detection from a single leaf using hybrid deep learning technique. Computers, Materials & Continua, 74(1), 1235-1257. https://doi.org/10.32604/cm c.2023.032005
Redmon, J., & Farhadi, A. (2018). YOLOv3: An Incremental Improvement. arXiv 2018, arXiv:1804.02767
Roy, A. M., & Bhaduri, J. (2021). A deep learning-enabled multi-class plant disease detection model based on computer vision. AI 2021, 2, 413–428. https://doi.org/10.3390/ ai2030026
Terven, J. R., & Cordova-Esparza, D. M. (2023). A comprehensive review of yolo: from yolov1 and beyond. arXiv:2304.00501v4 [cs.CV]
Terzi, İ., Özgüven, M. M., Altaş, Z., & Uygun, T. (2019). Tarımda yapay zeka kullanımı. International Erciyes Agriculture, Animal Food Sciences Conference 24-27 April 2019- Erciyes University- Kayseri/Turkey.
Uygun, T., Ozguven, M. M., & Yanar, D. (2020). A new approach to monitor and assess the damage caused by two-spotted spider mite. Experimental and Applied Acarology, 82(3), 335-346. https://doi.org/10.1007/s10493-020-00561-8
Vaidya, S., Kavthekar, S., & Joshi, A. (2023). Leveraging YOLOv7 for plant disease detection. 2023 4th International Conference on Innovative Trends in Information Technology (ICITIIT) | 978-1-6654-9414-4/23/$31.00 ©2023 IEEE | DOI: 10.1109/ICITIIT57246.2023.10068590
Wang, J., Yu, L., Yang, J., & Dong, H. (2021). DBA_SSD: A novel end-to-end object detection algorithm applied to plant disease detection. Information 2021, 12, 474. https://doi.org/10.3390/info12110474
Wang, C. Y., Bochkovskiy, A., & Liao, H. Y. M. (2022). YOLOv7: Trainable bag-of-freebies sets new state-of-the-art for real-time object detectors. Institute of Information Science, Academia Sinica, Taiwan. https://doi.org/10.48550/arXiv.2207.02696
Wang, G., Chen, Y., An, P., Hong, H., Hu, J., & Huang, T. (2023). UAV- YOLOv8: A small-object-detection model based on improved YOLOv8 for UAV aerial photography scenarios. Sensors 2023, 23, 7190. https:// doi.org/10.3390/s23167190
Zhang, Z., Qiao, Y., Guo, Y., & He, D. (2022). Deep learning based automatic grape downy mildew detection. Front. Plant Sci. 13:872107. doi: 10.3389/fpls.2022.872107
Zhang, L., Ding, G., Li, C., & Li, D. (2023). DCF-Yolov8: An improved algorithm for aggregating low-level features to detect agricultural pests and diseases. Agronomy 2023, 13, 2012. https://doi.org/10.3390/agronomy13082012
Zheng, Wang, P., Liu, W., Li, J., Ye, R., & Ren, D. (2020). Distance-IoU loss: Faster and better learning for bounding box regression. In Proceedings of the AAAI Conference on Artificial Intelligence, New York, NY, USA, 7–12 February 2020; pp. 12993–13000
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