Detection of Damage Caused by Some Vineyard Pests with the YOLOv8x Model Using Deep Learning and Object Detection Methods




Deep Learning, Object Detection, YOLOv8x, Vineyard Pests, Vineyard


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.


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How to Cite

Uygun, T., Özgüven, M. M., & Yanar, D. (2024). Detection of Damage Caused by Some Vineyard Pests with the YOLOv8x Model Using Deep Learning and Object Detection Methods. Turkish Journal of Agriculture - Food Science and Technology, 12(3), 386–396.



Research Paper