智能物联网驱动的精密农业:土地绘图,作物预测和灌系统
Gourab Saha1, Fariha Shahrin1, Farhan Hasin Khan1
1Electrical and Electronic Engineering Department, BRAC University , Dhaka, Bangladesh.
PloS one
|March 18, 2025
概括
这项研究将物联网,机器学习和模糊逻辑集成到智能农业中,优化土地选择和灌. 该系统增强了作物产量预测,并大大减少了用于可持续农业的用水量.
科学领域:
- 农业科学 农业科学
- 计算机科学 计算机科学
- 环境科学 环境科学
背景情况:
- 全球人口的增加需要先进的精准农业,以确保粮食安全.
- 现有的研究往往缺乏用于可持续农业系统的综合智能技术.
- 需要自动化系统来优化农业的资源管理.
研究的目的:
- 介绍基于物联网的智能农田和作物选择系统的综合设计.
- 开发一种自动化灌系统,利用机器学习和模糊逻辑,用于精准农业.
- 通过计算技术,加强资源管理和农业的可持续性.
主要方法:
- 使用Landsat-8卫星图像进行农业分析 (植被,水,盐度指数) 通过K-means集群.
- 采用机器学习算法 (线性回归,随机森林) 进行作物产量预测和作物适应性分析.
- 集成了一个物联网网络与土壤传感器和基于模糊逻辑的太阳能灌系统.
主要成果:
- 在作物产量预测 (高达95.87%的随机森林) 和作物适应性预测 (97.35%) 中取得了高准确性.
- LSTM模型有效地预测了随着时间的推移健康植被面积的变化.
- 与传统方法相比,模糊逻辑灌系统显示了显著的节水 (约61%) 和更快的校准 (66.23%).
结论:
- 综合系统有效地支持可持续农业的智能土地和作物选择.
- 先进的计算技术,包括机器学习和模糊逻辑,显著提高农业效率和资源管理.
- 开发的系统为精密农业提供了可扩展的解决方案,解决了全球粮食需求和水资源短缺问题.
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