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基于LAI估计的UAV产量预测,基于冬季小麦的LAI估计 (Triticum aestivum L.) 在不同的肥类型和率下

Jinjin Guo^1,2,3,4, Xiangtong Zeng^1,2,3,4, Qichang Ma^1,2,3,4

¹Yunnan Key Laboratory of Efficient Utilization and Intelligent Control of Agricultural Water Resources, Faculty of Modern Agricultural Engineering, Kunming University of Science and Technology, Kunming 650500, China.

Plants (Basel, Switzerland)

|July 12, 2025

概括

通过无人机遥感估计的叶面积指数 (LAI) 可以实现准确的冬季小麦产量预测,特别是在解剖时的CI_红边指数. 慢释放化肥 (SRF) 在220公斤ha-1时优化了产量.

科学领域:

农业科学农业科学
遥感遥感遥感遥感
农业学是一种农业学.

背景情况:

精准农业依赖于准确的作物产量预测,以有效管理.
叶面积指数 (LAI) 是作物生长和产量潜力的关键指标.
无人机 (UAV) 多谱传感器为作物监测提供了先进的功能.

研究的目的:

使用LAI和光谱数据建立最佳冬季小麦产量预测模型.
为了确定最佳的植被指数和生长阶段,以估计产量.
确定最佳的缓释化肥战略,以实现可持续的产量增加.

主要方法:

在四个生长阶段使用无人机收集冬季小麦LAI和树冠光谱数据.
从光谱数据中提取了植被指数,包括CI_红边.
使用随机森林 (RF),支持矢量机 (SVM) 和反向传播神经网络 (BPNN) 开发和评估了产量预测模型.

主要成果:

劳动力投资显示出与收益的显著正相关性,在合成阶段达到顶峰 (R2=0.96).
CI_红边植被指数在估计LAI和预测产量方面表现出卓越的准确性.
随机森林 (RF) 模型实现了最高的预测准确度.
慢释放化肥 (SRF) 在220公斤/ha-1时,产生了冬季小麦的最高产量.

结论:

在合成阶段基于无人机的LAI遥感对于冬季小麦产量预测是有效的.
CI_红边是冬季小麦产量估计的最佳光谱指数.
在220公斤公-1上应用的SRF是最大限度地提高冬季小麦产量的最有效的施肥策略.

关键词:

N受精的类型和速度.叶面积指数叶面积指数远程传感是一种遥感技术.冬季小麦冬季小麦收益率预测收益率预测

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