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由人工智能和物联网驱动的边缘设备优化用于作物害虫和疾病检测

Jean Pierre Nyakuri¹, Celestin Nkundineza^2,3, Omar Gatera²

¹African Centre of Excellence in Internet of Things (ACEIoT), College of Science and Technology, University of Rwanda, Kigali, Rwanda. njpindian@yahoo.fr.

Scientific reports

|July 1, 2025

概括

这项研究介绍了一种便携式智能物联网设备,用于在作物中早期检测害虫和疾病. 该系统使用轻量级的人工智能模型 (Tiny-LiteNet) 进行高效的实时分析,帮助可持续农业.

科学领域:

农业技术农业技术
计算机科学计算机科学
环境科学环境科学

背景情况:

气候变化加剧了谷物作物中的害虫和疾病爆发,导致大量的收获损失.
现有的监测技术往往缺乏可移植性,成本效益和能源效率,用于资源有限的农业环境.
对于实时监测作物健康状况的边缘兼容解决方案有着至关重要的需求.

研究的目的:

开发一款便携式,具有成本效益和能源效率的智能物联网设备,用于在作物中早期检测害虫和疾病.
整合一个轻量级,可解释的卷积神经网络 (CNN),优化为边缘应用.
为农民提供一个实用的工具,以加强作物健康管理和粮食安全.

主要方法:

开发一款便携式智能物联网设备,配备高清摄像头和Raspberry Pi 5.
整合了一个新的轻量级CNN,Tiny-LiteNet,用于基于边缘的图像处理和分析.
使用GSM/GPRS模块进行无云通信和数据传输.

主要成果:

Tiny-LiteNet实现了高精度 (98.6%),F1得分 (98.4%) 和回忆 (98.2%) 的快速推断时间 (80毫秒).
该模型拥有紧的尺寸 (1.2 MB,148万个参数),在边缘计算的效率方面超过传统的CNN.
智能设备显示了低功耗和易于使用的设计,适合农业领域使用.

结论:

开发的智能物联网设备为农业中实时检测害虫和疾病提供了实用和高效的解决方案.
Tiny-LiteNet的性能和效率使其成为资源有限的环境中边缘AI应用的理想选择.
这项技术支持可持续农业实践,增强粮食安全,并为农民提供可操作的见解.

关键词:

卷积神经网络是一种卷积神经网络.深度学习是一种深度学习.边缘计算是一种边缘计算.病虫害和疾病检测检测检测这是一个小小的LiteNet.

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