A novel dataset and deep learning object detection benchmark for grapevine pest surveillance.

Flavescence dorée (FD) poses a significant threat to grapevine health, with the American grapevine leafhopper, , serving as the primary vector. FD is responsible for yield losses and high production costs due to mandatory insecticide treatments, infected plant uprooting, and replanting. Another potential FD vector is the mosaic leafhopper, , commonly found in agroecosystems. The current monitoring approach, which involves periodic human identification of yellow sticky traps, is labor-intensive and time-consuming. Therefore, there is a compelling need to develop an automatic pest detection system leveraging recent advances in computer vision and deep learning techniques. However, progress in developing such a system has been hindered by the lack of effective datasets for training. To fill this gap, our study contributes a fully annotated dataset of and from yellow sticky traps, which includes more than 600 images, with approximately 1500 identifications per class. Assisted by entomologists, we performed the annotation process, trained, and compared the performance of two state-of-the-art object detection algorithms: YOLOv8 and Faster R-CNN. Pre-processing, including automatic cropping to eliminate irrelevant background information and image enhancements to improve the overall quality of the dataset, was employed. Additionally, we tested the impact of altering image resolution and data augmentation, while also addressing potential issues related to class detection. The results, evaluated through 10-fold cross validation, revealed promising detection accuracy, with YOLOv8 achieving an mAP@0.5 of 92%, and an F1-score above 90%, with an mAP@[0.5:0.95] of 66%. Meanwhile, Faster R-CNN reached an mAP@0.5 and mAP@[0.5:0.95] of 86% and 55%, respectively. This outcome offers encouraging prospects for developing more effective management strategies in the fight against Flavescence dorée.