ORIGINAL RESEARCH article
Front. Plant Sci.
Sec. Sustainable and Intelligent Phytoprotection
Volume 16 - 2025 | doi: 10.3389/fpls.2025.1665352
This article is part of the Research TopicHighlights of 1st International Conference on Sustainable and Intelligent Phytoprotection (ICSIP 2025)View all articles
Research on the Method of Shiitake Mushroom Picking Robot Based on CSO-ASTGCN Human Action Prediction Network
Provisionally accepted
- East China JiaoTong University School of Electrical and Automation Engineering, Nanchang, China
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Automating shiitake mushroom picking is critical for modern agriculture, yet its biological traits hinder automation via target recognition, path planning, and precision challenges. Traditional manual picking is inefficient, labor-heavy, and unsuitable for large-scale production. In human-robot collaboration, computer vision -based human motion prediction enables efficient picking coordination, yet methods like LSTM and static graph networks struggle with robust spatiotemporal correlation capture and long-term stability in complex agricultural settings. To address this, we propose the Chaos-Optimized Adaptive Spatiotemporal Graph Convolutional Network (CSO-ASTGCN). First, it integrates three core modules: the Adaptive Spatial Feature Graph Convolution Module (ASF-GCN) for dynamic joint correlation modeling (e.g., wrist-finger coupling during grasping). Second, the Dynamic Temporal Feature Graph Convolution Module (DT-GCN) captures multi-scale temporal dependencies. Third, Chaos Search Optimization (CSO) globally optimizes hyper parameters to avoid local optima common in traditional optimization methods. Additionally, a flexible control system fuses CSO-ASTGCN motion prediction with GRCNN grasp pose estimation to optimize grasping paths and operational forces. Experiments show our model reduces the Mean Per -Joint Position Error (MPJPE) by 15.2% on the CMU dataset and 12.7% on the 3DPW dataset compared to methods like STSGCN and Transformers. The human -robot collaborative system boosts picking efficiency by 31% and cuts mushroom damage by 26% relative to manual operations. These results validate CSO -ASTGCN's superiority in spatiotemporal modeling for fine -grained agricultural motions and its practical value in intelligent edible fungi harvesting.
Keywords: Shiitake mushroom picking robot, Human motion prediction, Spatiotemporal graph convolutional network, chaos search optimization, Human-robot collaboration
Received: 14 Jul 2025; Accepted: 19 Aug 2025.
Copyright: © 2025 Yao, Yang, Chen, Chen, Yin and Wang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Daojin Yao, East China JiaoTong University School of Electrical and Automation Engineering, Nanchang, China
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