The fusion of vegetation indices increases the accuracy of cotton leaf area prediction.

ABSTRACT:

Introduction

Rapid and accurate estimation of leaf area index (LAI) is of great significance for the precision agriculture because LAI is an important parameter to evaluate crop canopy structure and growth status.

Methods

In this study, 20 vegetation indices were constructed by using cotton canopy spectra. Then, cotton LAI estimation models were constructed based on multiple machine learning (ML) methods extreme learning machine (ELM), random forest (RF), back propagation (BP), multivariable linear regression (MLR), support vector machine (SVM)], and the optimal modeling strategy (RF) was selected. Finally, the vegetation indices with a high correlation with LAI were fused to construct the VI-fusion RF model, to explore the potential of multi-vegetation index fusion in the e

SUBMITTER: Fan X 

PROVIDER: S-EPMC11298913 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature