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, Scaphoideus titanus, serving as the primary vector. FD is responsible for yield losses and high production costs due to mandatory insecticide treatments, infected plant uprooting, and repla...
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2024-12-01
|
| Series: | Frontiers in Plant Science |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/articles/10.3389/fpls.2024.1485216/full |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850060965601607680 |
|---|---|
| author | Giorgio Checola Paolo Sonego Roberto Zorer Valerio Mazzoni Franca Ghidoni Alberto Gelmetti Pietro Franceschi |
| author_facet | Giorgio Checola Paolo Sonego Roberto Zorer Valerio Mazzoni Franca Ghidoni Alberto Gelmetti Pietro Franceschi |
| author_sort | Giorgio Checola |
| collection | DOAJ |
| description | Flavescence dorée (FD) poses a significant threat to grapevine health, with the American grapevine leafhopper, Scaphoideus titanus, 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, Orientus ishidae, 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 S. titanus and O. ishidae 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. |
| format | Article |
| id | doaj-art-7ddeec16cfb044abb9336b2f30bdd5b2 |
| institution | DOAJ |
| issn | 1664-462X |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Plant Science |
| spelling | doaj-art-7ddeec16cfb044abb9336b2f30bdd5b22025-08-20T02:50:25ZengFrontiers Media S.A.Frontiers in Plant Science1664-462X2024-12-011510.3389/fpls.2024.14852161485216A novel dataset and deep learning object detection benchmark for grapevine pest surveillanceGiorgio Checola0Paolo Sonego1Roberto Zorer2Valerio Mazzoni3Franca Ghidoni4Alberto Gelmetti5Pietro Franceschi6Research and Innovation Centre, Fondazione Edmund Mach, San Michele all’Adige, TN, ItalyResearch and Innovation Centre, Fondazione Edmund Mach, San Michele all’Adige, TN, ItalyResearch and Innovation Centre, Fondazione Edmund Mach, San Michele all’Adige, TN, ItalyResearch and Innovation Centre, Fondazione Edmund Mach, San Michele all’Adige, TN, ItalyTechnology Transfer Centre, Fondazione Edmund Mach, San Michele all’Adige, TN, ItalyTechnology Transfer Centre, Fondazione Edmund Mach, San Michele all’Adige, TN, ItalyResearch and Innovation Centre, Fondazione Edmund Mach, San Michele all’Adige, TN, ItalyFlavescence dorée (FD) poses a significant threat to grapevine health, with the American grapevine leafhopper, Scaphoideus titanus, 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, Orientus ishidae, 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 S. titanus and O. ishidae 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.https://www.frontiersin.org/articles/10.3389/fpls.2024.1485216/fullScaphoideus titanusinsect detectionyellow sticky trapsdeep learningmachine visionprecision agriculture |
| spellingShingle | Giorgio Checola Paolo Sonego Roberto Zorer Valerio Mazzoni Franca Ghidoni Alberto Gelmetti Pietro Franceschi A novel dataset and deep learning object detection benchmark for grapevine pest surveillance Frontiers in Plant Science Scaphoideus titanus insect detection yellow sticky traps deep learning machine vision precision agriculture |
| title | A novel dataset and deep learning object detection benchmark for grapevine pest surveillance |
| title_full | A novel dataset and deep learning object detection benchmark for grapevine pest surveillance |
| title_fullStr | A novel dataset and deep learning object detection benchmark for grapevine pest surveillance |
| title_full_unstemmed | A novel dataset and deep learning object detection benchmark for grapevine pest surveillance |
| title_short | A novel dataset and deep learning object detection benchmark for grapevine pest surveillance |
| title_sort | novel dataset and deep learning object detection benchmark for grapevine pest surveillance |
| topic | Scaphoideus titanus insect detection yellow sticky traps deep learning machine vision precision agriculture |
| url | https://www.frontiersin.org/articles/10.3389/fpls.2024.1485216/full |
| work_keys_str_mv | AT giorgiochecola anoveldatasetanddeeplearningobjectdetectionbenchmarkforgrapevinepestsurveillance AT paolosonego anoveldatasetanddeeplearningobjectdetectionbenchmarkforgrapevinepestsurveillance AT robertozorer anoveldatasetanddeeplearningobjectdetectionbenchmarkforgrapevinepestsurveillance AT valeriomazzoni anoveldatasetanddeeplearningobjectdetectionbenchmarkforgrapevinepestsurveillance AT francaghidoni anoveldatasetanddeeplearningobjectdetectionbenchmarkforgrapevinepestsurveillance AT albertogelmetti anoveldatasetanddeeplearningobjectdetectionbenchmarkforgrapevinepestsurveillance AT pietrofranceschi anoveldatasetanddeeplearningobjectdetectionbenchmarkforgrapevinepestsurveillance AT giorgiochecola noveldatasetanddeeplearningobjectdetectionbenchmarkforgrapevinepestsurveillance AT paolosonego noveldatasetanddeeplearningobjectdetectionbenchmarkforgrapevinepestsurveillance AT robertozorer noveldatasetanddeeplearningobjectdetectionbenchmarkforgrapevinepestsurveillance AT valeriomazzoni noveldatasetanddeeplearningobjectdetectionbenchmarkforgrapevinepestsurveillance AT francaghidoni noveldatasetanddeeplearningobjectdetectionbenchmarkforgrapevinepestsurveillance AT albertogelmetti noveldatasetanddeeplearningobjectdetectionbenchmarkforgrapevinepestsurveillance AT pietrofranceschi noveldatasetanddeeplearningobjectdetectionbenchmarkforgrapevinepestsurveillance |