A Resource Optimization Approach Based on Deep Integration of Vision-Motion System for Sustainable Agriculture
The integration of vision and motion systems represents a critical phase in the intelligent transformation of consumer electronics aimed at enhancing productivity. However, owing to the operational modes and distributional characteristics inherent in existing systems, achieving large-scale, stable, and consistent agricultural applications on consumer electronics remains a significant challenge. To address this problem, this paper proposes a unified resource optimization approach for different configurations of agricultural consumer electronics to achieve deep integration of vision and motion systems. The optimization is mainly at two levels: on the one hand, we design a velocity observer for the vision-motion integrated system represented by the visual servoing system, which makes native changes to the characteristics of the visual servoing’s non-real-time instruction issuance. By converting long-time commands with uncertain period to short-time commands with certain period, the design difficulty of real-time trajectory planning of the real underlying motion controller is simplified. On the other hand, in image-based visual servoing (IBVS) system, the mixture parameter of the image Jacobian matrix also affect the control performance of the visual servoing system. For most IBVS-based agricultural applications, there is a lack of a systematic approach to ensure that the mixture parameter is adaptively and continuously varied. To solve this problem, this paper proposes a fuzzy logic-based method to adaptively adjust this parameter and ensure its continuity by introducing a suitable membership function. The experimental results of visual servoing based on the consumer electronics show that our proposed method can significantly improve the integrated vision-motion controllability, and can trade-off the convergence efficiency and feature retention constraints to effectively improve the overall efficiency of the system operation.
Authors
- Areej Alasiry (ORCID: https://orcid.org/0000-0003-4088-5942)
- Mehrez Marzougui (ORCID: https://orcid.org/0000-0003-0445-5371)
- Minghao Cheng (ORCID: https://orcid.org/0000-0002-2487-9078)
- Mughair Aslam Bhatti (ORCID: https://orcid.org/0009-0009-8409-3745)
- Di Li (ORCID: https://orcid.org/0000-0001-5065-9565)
- Nan Zhou (ORCID: https://orcid.org/0009-0008-8429-550X)
- Shiyong Wang (ORCID: https://orcid.org/0000-0002-0297-4525)
- Zeeshan Zeeshan (ORCID: https://orcid.org/0000-0003-4745-6892)
Institutions
- Shaheed Zulfiqar Ali Bhutto Institute of Science and Technology (PK)
- Robotics Research (United States) (US)
- Peng Cheng Laboratory (CN)
- King Khalid University (SA)
- South China University of Technology (CN)
Publication Details
- Journal
- IEEE Transactions on Consumer Electronics
- Published
- 2025-04-03
- DOI
- https://doi.org/10.1109/tce.2025.3552586
- Citations
- 1
- Primary Topic
- Smart Agriculture and AI
- Type
- article
- Field-Weighted Citation Impact
- 1.75