在干旱地区使用机器学习和物联网集成使用人工智能支持的智能农业框架,用于在干旱地区可持续种植枣树
Marran Al Qwaid1, Md Tanjil Sarker2, Sarowar Morshed Shawon3
1Department of Computer Science, College of Computing and Information Technology, Shaqra University, Shaqra, 11961, Saudi Arabia.
Scientific reports
|January 12, 2026
概括
这项研究介绍了一种使用机器学习 (ML) 和物联网 (IoT) 的人工智能智能农业系统,以增强干旱地区的枣树种植. 该系统优化了灌,并预测了棕树的健康状况,通过随机森林模型实现了95.3%的准确性.
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:
- 干旱地区面临着重大的农业挑战,包括水资源短缺和高温.
- 在这些地区,传统的农业方法往往是低效和不可持续的.
- 枣树种植至关重要,但在干旱的环境中面临着障碍.
研究的目的:
- 开发一个支持人工智能的智能农业框架,以优化枣树种植.
- 整合机器学习 (ML) 和物联网 (IoT) 以改善作物管理.
- 为了提高水利用效率和预测诊断在枣树种植.
主要方法:
- 创建了一个多模式数据集,包括生物识别,环境和健康状况数据.
- 四个ML算法 (随机森林,GBM,ANN,SVM) 被训练并优化.
- 用五倍交叉验证的网格搜索用于模型优化.
主要成果:
- 随机森林模型在分类棕健康方面取得了最高的准确性 (95.3%).
- 土壤水分,湿度,树干直径和叶数被确定为关键预测特征.
- 人工智能-物联网框架证明了对异质农业数据的稳定性.
结论:
- 人工智能-物联网框架为可持续农业提供实时监控和自动决策支持.
- 这项技术支持与国家发展目标一致的资源高效农业实践.
- 使用人工智能的优化枣树种植有助于干旱地区的粮食安全.
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