Reducing soil and leaf shadow interference in UAV imagery for cotton nitrogen monitoring.

ABSTRACT:

Introduction

Individual leaves in the image are partly veiled by other leaves, which create shadows on another leaf. To eliminate the interference of soil and leaf shadows on cotton spectra and create reliable monitoring of cotton nitrogen content, one classification method to unmanned aerial vehicle (UAV) image pixels is proposed.

Methods

In this work, green light (550 nm) is divided into 10 levels to limit soil and leaf shadows (LS) on cotton spectrum. How many shadow has an influence on cotton spectra may be determined by the strong correlation between the vegetation index (VI) and leaf nitrogen content (LNC). Several machine learning methods were utilized to predict LNC using less disturbed VI. R-Square (R ²), root mean square error (RMSE), and mean abs

SUBMITTER: Yin C 

PROVIDER: S-EPMC11362076 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature