Visual Perception-Driven Control Strategies for Agricultural Machinery Automation
Year: 2025
Type: article
Abstract: This study addresses the increasing demand for intelligent and context-aware automation in complex environments by introducing a perception-driven control framework designed to tackle operational uncertainties, heterogeneous terrain conditions, and spatiotemporal variability. Conventional autonomous control systems in dynamic and semi-structured domains often rely on rigid architectures or static policy representations, which restrict their adaptability to environmental changes and multi-phase task requirements. Furthermore, these systems frequently exhibit limitations in handling sensor noise, occlusion, and terrain-dependent constraints due to insufficient modularity and inadequate multi-scale reasoning. To address these challenges, we propose an integrated framework combining the Field-Adaptive Perception-Control Encoder (FAPCE) and the Contextually Guided Adaptive Modulation (CGAM) system. FAPCE utilizes a hierarchical encoder-decoder architecture to integrate multi-resolution sensory data, spatial field maps, and temporal sequences, enabling precise low-level control commands. CGAM incorporates semantic field classification, phase-aware control adaptation, and uncertainty-aware decision modulation to dynamically adjust control outputs based on contextual information. This approach facilitates real-time policy switching, smooth control transitions, and effective coordination among multiple agents, enhancing robustness in variable and unpredictable conditions. Extensive experiments across four benchmark datasets show that our method achieves an average accuracy of 92.17% and an F1 score of 90.90%, outperforming state-of-the-art baselines such as Swin Transformer and EfficientNet by 2.3% and 4.2%, respectively. Under sensor perturbations and partial modality loss, the proposed system maintains over 89% F1 score, confirming its robustness. On embedded platforms such as NVIDIA Jetson Xavier, real-time inference is sustained at 17.8 FPS with a 23W power envelope, supporting practical deployment. These results demonstrate that our system significantly improves coverage efficiency, safety compliance, and task execution continuity across diverse agricultural and semi-structured environments. The proposed method contributes to advancements in task-aware and semantically grounded control systems, offering strong potential for adaptive automation in sensor-rich, safety-critical real-world domains.
Source: IEEE Access
Authors: Runkun Guo, Liang Shang
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $2,075
OpenAlex ID: W4417201599