发表机构
Delhi Public School Vasant Kunj; University of Delhi; On My Own Technology Pvt. Ltd（瓦桑特昆吉德里公立学校; 德里大学; On My Own Technology私人有限公司）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本文提出CropSentry，一种低成本多机器人系统，通过多模态叶片感知逐行监测作物胁迫，实现84.12%分类准确率，为中小农户提供可负担的早期胁迫检测方案。
AI 中文摘要
早期作物胁迫检测在当今对于提高效率、减少时间、金钱和精力浪费至关重要。然而，大多数现代技术，如高光谱成像和基于人工智能的系统，对于中小规模农户而言过于昂贵和复杂，难以实施。本文展示了CropSentry，一个低成本、基于地面的多机器人系统，利用多模态叶片感知通过跟踪胁迫水平来持续监测作物健康。该系统由两个自主机器人组成，它们逐行持续检测叶片颜色和环境数据。观测结果被空间映射并发送至主机器人，主机器人利用颜色编码的行段生成实时基于网页的仪表板，显示作物健康状况。在实验期间收集了63次观测后，结果显示整体作物健康分类准确率为84.12%，其中健康植物为82.60%，营养缺乏植物为88%，患病植物为80%。此外，在10次从属机器人观测中实现了100%的无线通信成功率。跨多个机器人的近距离叶片检查可以在保持经济实惠、可访问和可扩展的同时检测作物的早期胁迫。它为农民提供及时信息，以改善资源利用和作物管理。
英文摘要
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.
