YOLOv11-based real-time weed detection and autonomous precision spraying for hilly agriculture
5 affiliations
- 1School of Engineering and Computing, Dev Bhoomi Uttarakhand University
- 2School of Design, University of Petroleum & Energy Studies
- 3School of Computer Science Engineering, Chandigarh University Uttar Pradesh
- 4Department of Computer Science, Christ University
- 5Cooperative Bank of Oromia
Abstract
Weeds are a major hazard to agricultural production and ecology in the hilly areas of Uttarakhand, India. Traditional weed management methods use blanket spraying of herbicides, which is very expensive, polluting, and unrealistic in steep slope areas where machines cannot reach. This study addresses this issue by recommending a portable robotic platform (robot) with the capability of detecting weeds using a deep learning system to apply precision herbicides to uneven and undulating surfaces. The system utilizes YOLOv11, the most recent version of the YOLO algorithm, which is renowned for its high accuracy and capability to detect objects in real time. It can also distinguish between crops, weeds, and background objects so that they can be sprayed using site-specific spraying. It was compared to Faster R-CNN on a performance basis using conventional detection metrics. On the independent test set, YOLOv11 achieved precision 0.7516, recall 0.8846, F1 0.8127, and [email protected] 0.8894, compared with Faster R-CNN at precision 0.3483, recall 0.9538, F1 0.5103, and [email protected] 0.8873; five-fold cross-validation confirmed that YOLOv11’s performance was stable across data partitions ([email protected] 0.889 ± 0.005, precision 0.750 ± 0.005, recall 0.881 ± 0.006). Although Faster R-CNN had a higher recall rate, YOLOv11 had a higher precision and F1 score, indicating that it is more reliable with fewer false positives. In end-to-end field trials over 0.7 ha of terraced fields, the integrated robot achieved a 92.4% spray hit-rate, a 64.8 ± 3.1% reduction in herbicide volume versus blanket application (mean ± SD; n = 3 independent paired field trials), and 7.1% off-target drift, demonstrating that the title’s claim of autonomous precision spraying for hilly agriculture is operationally validated under the tested field conditions. These results indicate that the proposed system offers a viable pathway toward reduced herbicide consumption, lower operating cost, and reduced environmental impact under hillside field conditions, warranting further multi-season validation.
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BibTeX
@article{mehra2026yolov11based,
title = {{YOLOv11-based real-time weed detection and autonomous precision spraying for hilly agriculture}},
author = {Ritika Mehra and Vikas Thapa and Rituraj Jain and Kamal Upreti and Govind Panwar and Simon Kasahun Bekele},
journal = {Scientific Reports},
year = {2026},
month = sep,
doi = {10.1038/s41598-026-71788-5},
url = {https://doi.org/10.1038/s41598-026-71788-5},
note = {CC BY},
}
RIS
TY - JOUR
TI - YOLOv11-based real-time weed detection and autonomous precision spraying for hilly agriculture
AU - Ritika Mehra
AU - Vikas Thapa
AU - Rituraj Jain
AU - Kamal Upreti
AU - Govind Panwar
AU - Simon Kasahun Bekele
PY - 2026
DA - 2026/09/01
JO - Scientific Reports
DO - 10.1038/s41598-026-71788-5
UR - https://doi.org/10.1038/s41598-026-71788-5
AB - Weeds are a major hazard to agricultural production and ecology in the hilly areas of Uttarakhand, India. Traditional weed management methods use blanket spraying of herbicides, which is very expensive, polluting, and unrealistic in steep slope areas where machines cannot reach. This study addresses this issue by recommending a portable robotic platform (robot) with the capability of detecting weeds using a deep learning system to apply precision herbicides to uneven and undulating surfaces. The system utilizes YOLOv11, the most recent version of the YOLO algorithm, which is renowned for its high accuracy and capability to detect objects in real time. It can also distinguish between crops, weeds, and background objects so that they can be sprayed using site-specific spraying. It was compared to Faster R-CNN on a performance basis using conventional detection metrics. On the independent test set, YOLOv11 achieved precision 0.7516, recall 0.8846, F1 0.8127, and [email protected] 0.8894, compared with Faster R-CNN at precision 0.3483, recall 0.9538, F1 0.5103, and [email protected] 0.8873; five-fold cross-validation confirmed that YOLOv11’s performance was stable across data partitions ([email protected] 0.889 ± 0.005, precision 0.750 ± 0.005, recall 0.881 ± 0.006). Although Faster R-CNN had a higher recall rate, YOLOv11 had a higher precision and F1 score, indicating that it is more reliable with fewer false positives. In end-to-end field trials over 0.7 ha of terraced fields, the integrated robot achieved a 92.4% spray hit-rate, a 64.8 ± 3.1% reduction in herbicide volume versus blanket application (mean ± SD; n = 3 independent paired field trials), and 7.1% off-target drift, demonstrating that the title’s claim of autonomous precision spraying for hilly agriculture is operationally validated under the tested field conditions. These results indicate that the proposed system offers a viable pathway toward reduced herbicide consumption, lower operating cost, and reduced environmental impact under hillside field conditions, warranting further multi-season validation.
SN - 2045-2322
ER -