arXiv cs.AI· Rishi Gupta, Astha Goyal, Vinay Vishwakarma·· 10 天前AI 评分22
CropSentry：群体协同多机器人系统利用多模态叶片传感实现农作物行早期胁迫检测
A Swarm-Coordinated Multi-Robot System for Early Stress Detection in Agricultural Rows Using Multimodal Leaf Sensing
AI 导读
CropSentry 是一个低成本地面多机器人系统，通过两台自主机器人逐行检测叶片颜色与环境数据，经空间映射后传至主机器人，生成实时网页仪表盘展示作物健康状态。在 63 次观测实验中，作物健康分类总体准确率为 84.12%，其中健康植株 82.60%、营养缺乏植株 88%、患病植株 80%，10 次从机观测的无线通信成功率达 100%。
正文 · 原文
Abstract:Early stress detection in crops is a necessity today to improve efficiency and reduce waste of time, money, and effort. However, most modern techniques, such as hyperspectral imaging and AI-based systems, are too costly and complex for medium and small-scale farmers to implement. This paper showcases CropSentry, a low-cost, ground-based multi-robot system that uses multimodal leaf sensing to continuously monitor crop health by tracking stress levels. The system comprises two autonomous bots that continuously detect leaf color and environmental data row by row. The observations are spatially mapped and sent over to the master bot, which uses color-coded row segments to generate a real-time web-based dashboard displaying crop health. After 63 observations were collected during the experiments, the results showed an overall crop health classification accuracy of 84.12%, with 82.60% for healthy plants, 88% for nutrient-deficient plants, and 80% for diseased plants. Also, 100% wireless communication success rate across 10 slave observations was achieved. Close-range leaf inspection across multiple bots can detect early stress in crops while remaining affordable, accessible, and scalable. It provides farmers with timely information to improve resource utilization and crop management.
| Subjects: | Robotics (cs.RO); Artificial Intelligence (cs.AI) |
| Cite as: | arXiv:2610.08603 [cs.RO] |
| (or arXiv:2610.08603v1 [cs.RO] for this version) | |
| https://doi.org/10.48550/arXiv.2610.08603 arXiv-issued DOI via DataCite (pending registration) |
Submission history
From: Astha Goyal [view email]
[v1]
Tue, 6 Oct 2026 16:07:15 UTC (671 KB)
来源：arXiv cs.AI · arxiv.org