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Cited by 41Smart Agriculture and AIAdvanced Neural Network ApplicationsRemote Sensing in Agriculture

Ag-YOLO: A Real-Time Low-Cost Detector for Precise Spraying With Case Study of Palms

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Qin, Zhenwang, Wang, Wensheng, Dammer, Karl-Heinz, Guo, Leifeng, Cao, Zhen·2021·Frontiers in Plant Science
DOI 10.3389/fpls.2021.753603PMC PMC8739968
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Abstract

To date, unmanned aerial vehicles (UAVs), commonly known as drones, have been widely used in precision agriculture (PA) for crop monitoring and crop spraying, allowing farmers to increase the efficiency of the farming process, meanwhile reducing environmental impact. However, to spray pesticides effectively and safely to the trees in small fields or rugged environments, such as mountain areas, is still an open question. To bridge this gap, in this study, an onboard computer vision (CV) component for UAVs is developed. The system is low-cost, flexible, and energy-effective. It consists of two parts, the hardware part is an Intel Neural Compute Stick 2 (NCS2), and the software part is an object detection algorithm named the Ag-YOLO. The NCS2 is 18 grams in weight, 1.5 watts in energy consumption, and costs about $66. The proposed model Ag-YOLO is inspired by You Only Look Once (YOLO), trained and tested with aerial images of areca plantations, and shows high accuracy (F1 score = 0.9205) and high speed [36.5 frames per second (fps)] on the target hardware. Compared to YOLOv3-Tiny, Ag-YOLO is 2× faster while using 12× fewer parameters. Based on this study, crop monitoring and crop spraying can be synchronized into one process, so that smart and precise spraying can be performed.

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  • Ag-YOLO is a lightweight version of YOLO developed for Intel Neural Compute Stick 2 (NCS2) hardware, designed for crop and spray monitoring.

    “A notable example is Ag-YOLO, a lightweight version of YOLO developed for Intel Neural Compute Stick 2 (NCS2) hardware, which was designed for crop and spray monitoring.”

    From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [101]

    The Introduction (§1.2) describes Ag-YOLO as an agricultural object detector tested on the NCS2, and the Conclusion says it improves YOLOv3-Tiny. The Abstract says the system aims to synchronize crop monitoring with crop spraying; it does not describe monitoring the spraying itself. Thus the hardware and lightweight-YOLO characterization are supported, but “spray monitoring” is not. (LLM, conf=0.84)

    Experimental — accuracy not yet measured

  • Ag-YOLO proved to be an efficient, cost-effective solution for farmers operating in resource-constrained environments.

    “Ag-YOLO achieved an F1-score of 0.9205, proving to be an efficient, cost-effective solution for farmers operating in resource-constrained environments [101].”

    From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [101]

    The Abstract reports an F1 score of 0.9205, 36.5 fps on low-cost hardware, and an energy-efficient system. The Discussion says selective spraying could reduce farmers’ costs and should be affordable for small farmers, but Future Work says integration into a practical spraying UAV is still planned. The paper therefore supports the detector’s measured performance and low-cost design, not a proven cost-effective solution in farmers’ use. (LLM, conf=0.86)

    Experimental — accuracy not yet measured

  • Ag-YOLO for crop monitoring and spraying achieved F1-score of 0.9205 on UAV imagery of palm trees.

    “| Ag-YOLO [101] | F1-Score | Ag-YOLO for crop monitoring and spraying, F1-score of 0.9205. | UAV imagery of palm trees |”

    From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [101]

  • Ag-YOLO achieved an F1-score of 0.9205.

    “Ag-YOLO achieved an F1-score of 0.9205, proving to be an efficient, cost-effective solution for farmers operating in resource-constrained environments [101].”

    From The YOLO Framework: A Comprehensive Review of Evolution, Applications, and Benchmarks in Object Detection (2024) · cites this paper as [101]

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Cited by

Sorted by citations

Authors
Qin, Z., Wang, W., Dammer, K.-H., Guo, L., & Cao, Z.
Affiliations
  • Agricultural Information Institute (CN)
  • Leibniz Institute for Agricultural Engineering and Bioeconomy (DE)
Corresponding author
  • Wang, Wensheng
  • Guo, Leifeng
ORCID
Venue
Frontiers in Plant Science
Publisher
Frontiers Media SA
Published
2021-12-24
Volume
12
Pages
753603-753603
ISSN
1664-462X (ISSN-L)
DOI
10.3389/fpls.2021.753603
PMC
PMC8739968
OpenAlex
W4200251033
Cited by
41
References
4
Citations by year
2026: 6 · 2025: 14 · 2024: 10 · 2023: 7 · 2022: 4
FWCI
3.16
Type
journal article
Language
en
Open access
Licence
Locations
  • Frontiers in Plant Science (publishedVersion, cc-by)
  • PubMed Central (publishedVersion, cc-by)
  • PubMed (publishedVersion)
  • DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
Funders
  • National Natural Science Foundation of China (31860180)
  • Natural Science Foundation of Xinjiang
Topics
  • Life Sciences › Agricultural and Biological Sciences › Plant Science › Smart Agriculture and AI
  • Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Neural Network Applications
  • Physical Sciences › Environmental Science › Ecology › Remote Sensing in Agriculture
Keywords
YOLO, object detection, unmanned aerial vehicles, precision agriculture, pesticide application, areca palm, plant detection, low-cost hardware, real-time detection, energy-efficient computing, aerial imagery

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