arXiv cs.AI· Lyes Saad Saoud, Oualid Doukhi, Reza Ghorbani, Moussa Ayyash·· 1 天前AI 评分22
AgriSense：虚拟温室中的视觉语言决策支持与机器人干预框架
AgriSense: Vision-Language Decision Support and Robotic Intervention in a Virtual Greenhouse Copy title
AI 导读
AgriSense 是一个结合 Isaac Sim 虚拟温室、四足移动机械臂与视觉语言决策支持的框架，可联合预测十二项环境参考值、目标区域和干预标志。在五组随机种子的测试中，完整模型、去图像和去语言配置均取得 43/44 正确区域决策，说明多模态并未带来区域准确率提升；温度、湿度、土壤湿度的平均绝对误差分别为 0.249 摄氏度、2.030 个百分点和 0.997 个百分点。
正文 · 原文
Abstract:Autonomous greenhouse management increasingly requires robotic systems that integrate perception, navigation, environmental regulation, and plant-level intervention. This paper presents AgriSense, a framework combining an Isaac Sim virtual greenhouse, a quadruped mobile manipulator, RGB and LiDAR-inertial sensing, environmental control, and supervised vision-language decision support. Frozen DINOv2 and BERT encoders, together with structured environmental inputs, support joint prediction of twelve environmental references, a target zone, and an intervention flag. Checkpoint replay reproduces 41/43 zone decisions. Across five seeds on a source-grouped test set, the full, image-removed, and language-removed configurations each achieve 43/44 correct zone decisions, providing no evidence of incremental multimodal zone-accuracy improvement. The full model achieves mean absolute errors of 0.249 degrees C for temperature, 2.030 percentage points for humidity, and 0.997 percentage points for soil moisture. Sensor-only inputs match full-model zone accuracy, while language-only inputs achieve 68.64%. External real-image benchmarks reveal distribution-shift limitations. Five offline Point-LIO routes yield rigidly aligned trajectory errors of 5.22-17.32 mm. Of 40 virtual-arm targets, 32 satisfy geometric acceptance criteria and eight are rejected at boundaries. Six long-horizon climate cases and fault protocols characterize integration limits. Navigation and integrated treatment rely on simulator-state inputs; physical greenhouse performance and agronomic treatment efficacy remain unvalidated. The contribution is an evaluated decision-support and robotic-intervention framework for a virtual greenhouse.
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI); Computer Vision and Pattern Recognition (cs.CV) |
| Cite as: | arXiv:2611.16752 [cs.RO] |
| (or arXiv:2611.16752v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2611.16752 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Lyes Saad Saoud Dr [view email]
[v1]
Thu, 12 Nov 2026 13:04:57 UTC (37,405 KB)
来源：arXiv cs.AI · arxiv.org