使用集成的光谱反射率和RGB图像指数与人工智能的干豆评估的水状态和植物特征
Mohamed S Abd El-Baki1, M M Ibrahim1, Salah Elsayed2,3
1Agricultural Engineering Department, Faculty of Agriculture, Mansoura University, Mansoura, 35516, Egypt.
Scientific reports
|May 14, 2025
概括
这项研究表明,遥感指数和人工神经网络 (ANN) 可以准确地评估干豆水压力. 新的光谱反射率指数 (SRIs) 改善了相关性,提供了实用的作物管理工具.
科学领域:
- 农业科学 农业科学
- 遥感 遥感 遥感 遥感
- 植物生理学 植物生理学
背景情况:
- 水压对干豆产量和生长有重大影响.
- 精确监测植物水的状态对于优化灌策略至关重要.
- 评估植物水压力的传统方法可能是劳动密集型和耗时的.
研究的目的:
- 调查远程传感指数与人工神经网络 (ANN) 结合的有效性,以量化干豆对水压力的反应.
- 评估新开发的光谱反射率指数 (SRIs) 与现有指数的性能.
- 用各种遥感数据输入来评估ANN的预测准确性.
主要方法:
- 实地实验是在三种灌模式下进行的 (100%,75%,50%的全部需求).
- 测量包括生物质,树冠含水量,SPAD,土壤含水量和种子产量.
- 遥感指数 (RGB图像指数 - RGBI,光谱反射率指数 - SRI) 与测量参数相关.
- 人工神经网络 (ANN) 用RGBIs和SRIs来预测植物的反应.
主要成果:
- 在完全灌下观察到最高的生物质和种子产量 (B100).
- 新开发的SRI与植物参数和产量相关性比已公布的指数高5-40%.
- 对于各种参数,ANN表现出高预测准确度 (R2 0.79-0.99),RGBI和SRI组合产生最佳结果.
结论:
- 遥感指数,特别是新型的SRI,与干豆生理反应和水压下的产量有效相关.
- 人工神经网络提供了一个高度准确和实用的工具,用于监测和管理赤字灌下的干豆作物.
- 在干豆生产中,SRI和ANN的整合为精准农业和可持续水资源管理提供了有希望的方法.
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