在智能农业设备上使用轻量级模型进行智能作物产量预测
Rajesh Kumar Dhanaraj1, M Maragatharajan2, Aanjankumar Sureshkumar2
1Symbiosis Institute of Computer Studies and Research (SICSR), Symbiosis International (Deemed University), Pune, India. sangeraje@gmail.com.
Scientific reports
|August 25, 2025
概括
这项研究引入了使用随机森林 (RF) 的智能作物产量预测系统,以优化可持续农业的用水量. 轻量级人工智能模型达到90.1%的准确性,提高了水资源管理,促进了对气候适应的农业.
科学领域:
- 农业技术
- 农业中的人工智能
- 可持续的农业
背景情况:
- 人工智能应用越来越多地集成到农业消费电子产品中,提高了工艺智能,效率和可持续性.
- 优化用水对于可持续农业,气候适应性和减少环境影响至关重要.
研究的目的:
- 开发一个智能作物产量预测系统以优化农业用水.
- 整合轻量级机器学习模型与消费电子产品,以改善水资源管理.
- 通过有效的灌计划促进可持续的农业实践.
主要方法:
- 使用随机森林 (RF) 分类器预测作物产量和优化用水量.
- 集成轻量级机器学习与消费电子设备,包括传感器和智能显示设备.
- 在实时农业数据上训练模型,使用最小的内存资源进行可持续性预测.
主要成果:
- 在预测作物产量适用于农田方面达到90.1%的准确性.
- 超越现有方法,例如支持AI的物联网 (89%),基于LoRa的系统 (87.2%) 和自适应性AI (88%).
- 证明了计算效率高的机器学习模型在不依赖云的实时决策的有效性.
结论:
- 通过精确的作物产量预测, 提议的智能系统有效地优化了用水量.
- 与消费电子相结合的轻量级机器学习模型为可持续农业提供了可行的解决方案.
- 该系统有助于改善水资源管理,减少对环境的影响,并支持适应气候变化的农业实践.
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