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Chengyuan Li

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Review Open access Aug 2026

Heterogeneous agricultural robots: a review of collaborative sensing and control, challenges and opportunities

A critical shift towards improved efficiency is currently taking place in modern agriculture, sustainability, and intelligence, driven by population growth, resource constraints, labor shortages, and increasing environmental pressure. Agricultural robots, especially heterogeneous robotic systems, have emerged as key enablers of this transformation by enabling precise, automated, and large-scale operations. This paper presents a comprehensive review of collaborative sensing and control technologies for heterogeneous agricultural robots working in both structured agricultural environments (greenhouses, orchards, and facility cultivation) and representative field farming scenarios. Focusing on recent advances over the past three years, we systematically analyze five representative robotic platforms: sprinkler irrigation robots, unmanned ground vehicles (UGVs), unmanned aerial vehicles (UAVs), agricultural robotic arms, and underground robots, from the perspectives of sensing, control, and cooperative operation. The review highlights key control methodologies, including intelligent path planning, adaptive and learning-based control, visual navigation, multi-sensor fusion, and energy-aware optimization. Furthermore, air–ground–underground integrated systems are discussed as an emerging paradigm for full-space agricultural coverage. Beyond technological progress, this paper identifies critical challenges in heterogeneous agricultural robot systems, including cooperative communication reliability, real-time safe control under dynamic environments, energy and endurance constraints, and system-level security. Finally, future research directions are outlined, emphasizing integrated communication and sensing, dual-arm and multi-arm collaborative manipulation, and heterogeneous multi-robot cooperative control frameworks.

Rong-Rong Gu, Songda Luan, Yunming Zhao et al. · 0 citations
Conference Jul 2026

Lightweight global-local dual-branch fusion for representative multicrop disease recognition

Multi-crop disease recognition becomes difficult when visually similar lesion patterns must be identified under a tight parameter budget. This paper reports a compact conference-scale study for the computer-vision and machine-learning track of MLES 2026. A representative 14-class subset covering tomato, cucumber, grape, and apple was constructed from 13,205 images, including 10,272 training images and 2,933 held-out evaluation images. The proposed network couples a lightweight global branch, implemented by a shallow CNN stem followed by a Transformer encoder, with a MobileNetV3- Small local branch for texture-sensitive feature extraction. A learned gating head projects and adaptively fuses global and local evidence before classification. On a single RTX 3060 GPU, the model achieved 99.35% Top-1 accuracy and 100.00% Top-5 accuracy, with macro precision, recall, F1-score, and specificity of 99.36%, 99.39%, 99.37%, and 99.95%, respectively. The model uses only 2.17M parameters, indicating that accurate and deployable visual recognition is possible with a compact dual-branch design. To address reviewer concerns on robustness and component attribution, the revised manuscript additionally reports five-fold cross-validation statistics, single-branch baselines, augmentation ablations, and a freezing-strategy study.

Yang Zhang, Rongrong Gu, Chengyuan Li et al. · 0 citations

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