智能和可持续的精准农业的代理人工智能
Parvathaneni Naga Srinivasu1, Aruna Pavate2, G JayaLakshmi3
1Amrita School of Computing, Amrita Vishwa Vidyapeetham, Amaravati, Andhra Pradesh, India.
Frontiers in plant science
|January 30, 2026
概括
代理人工智能 (AAI) 与联合学习 (FL) 结合,增强精密农业,实现更智能的农业. 这一框架改善了实时监测和决策,导致了更可持续的农业实践.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 人工智能的人工智能
背景情况:
- 现代农业在实现可持续和高效实践方面面临着挑战.
- 精准农业 (PA) 和联合学习 (FL) 为优化资源使用和决策提供了潜在的解决方案.
研究的目的:
- 为精准农业提出和评估一个代理人工智能 (AAI) 框架.
- 整合分布式传感,智能代理和FL进行实时农场级监测和决策支持.
主要方法:
- 开发了一个AAI框架,将PA和FL与本地模型 (DenseNet121,MobileNetV2,EfficientDet-D0,YOLOv8) 集成在一起.
- 评估了番茄疾病分类和杂草检测数据集的框架.
- 概述了用于设备间通信和本地化情报的实际部署架构.
主要成果:
- 联合全球模型实现了96.4%的准确性,超过了单个客户端模型.
- 在杂草检测方面,EfficientDet-D0实现了高性能 (mAP@0.5:0.978,F1得分:0.961).
- 在分类任务中,DenseNet121和MobileNetV2表现强 (准确率分别为95.0%和93.9%).
结论:
- 对智能精准农业而言,AAI和FL的整合是可行的,也是有效的.
- 该研究为开发可持续的智能农业系统提供了路线图.
- SWOT分析确定了优势和部署挑战,指导了未来的研究.
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