智能番茄种植的深度学习驱动的物联网解决方案
Akshit Saxena1, Aayushi Agarwal1, Bhavya Nagrath1
1School of Electronics Engineering, Vellore Institute of Technology, Vellore, India.
Scientific reports
|August 24, 2025
概括
这项研究介绍了用于番茄种植的物联网智能温室,使用传感器和人工智能监测作物条件并对成熟度进行分类. 该系统提供实时数据和优化的深度学习,
科学领域:
- 农业工程
- 计算机科学
- 环境科学
背景情况:
- 增加全球粮食需求和气候变化需要可持续的农业实践.
- 精准农业为优化作物生产效率提供了解决方案.
研究的目的:
- 开发基于物联网 (IoT) 的智能温室平台,用于番茄种植.
- 整合环境传感和深度学习以实时监测和成熟度分类.
主要方法:
- 使用ESP32无线传感器实时收集土壤湿度,温度和湿度数据.
- 使用带有Pi摄像头的Raspberry Pi和YOLOv8模型进行番茄成熟度分类 (绿色,半成熟,完全成熟).
- 实施模型优化技术 (量化,修剪,TensorRT) 以提高推断速度.
主要成果:
- 在初始阶段获得了35%的推断速度改进,52.8%的分类准确度.
- 测量每日能耗:ESP32传感器为8.91Wh,Raspberry Pi为78Wh.
- 展示了实时监测和人工智能驱动成熟度评估的功能原型.
结论:
- 开发的平台为智能农业提供了对环境监测和基于人工智能的作物评估的实用见解.
- 该研究为可扩展的多节点系统和温室边缘人工智能集成奠定了基础.
- 未来的改进包括Edge TPU,LoRa和全自动化温室的自动控制系统.
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