Opening book details…
Can I read Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios on EtoBox?
Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios by Sportelli, Mino (author);Apolo-Apolo, Orly Enrique (author);Fontanelli, Marco (author);Frasconi, Christian (author);Raffaelli, Michele (author);Peruzzi, Andrea (author);Perez-Ruiz, Manuel (author) is a Engineering article available to read on EtoBox.
What is Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios about?
The advancement of computer vision technology has allowed for the easy detection of weeds and other stressors in turfgrasses and agriculture. This study aimed to evaluate the feasibility of single shot object detectors for weed detection in lawns, which represents a difficult task. In this study, four different YOLO (You Only Look Once) object detectors version, along with all their various scales, were trained on a public ‘Weeds’ dataset with 4203 digital images of weeds growing in lawns with a total of 11,385 annotations and tested for weed detection in turfgrasses. Different weed species were considered as one class (‘Weeds’). Trained models were tested on the test subset of the ‘Weeds’ dataset and three additional test datasets. Precision (P), recall (R), and mean average precision (mAP\_0.5 and mAP\_0.5:0.95) were used to evaluate the different model scales. YOLOv8l obtained the overall highest performance in the ‘Weeds’ test subset resulting in a P (0.9476), mAP\_0.5 (0.9795), and mAP\_0.5:0.95 (0.8123), while best R was obtained from YOLOv5m (0.9663). Despite YOLOv8l high performances, the outcomes obtained on the additional test datasets have underscored the necessity for f
Who reads Evaluation of YOLO Object Detectors for Weed Detection in Different Turfgrass Scenarios?
It is typically read by researchers, students, and practitioners in Engineering.
- Author
- Sportelli, Mino (author);Apolo-Apolo, Orly Enrique (author);Fontanelli, Marco (author);Frasconi, Christian (author);Raffaelli, Michele (author);Peruzzi, Andrea (author);Perez-Ruiz, Manuel (author)
- Publisher
- MDPI AG
- Published
- 2023
- Language
- EN
- Field
- Engineering (Physical Sciences)