Development of Prediction Models for Estimating Key Rice Growth Variables Using Visible and NIR Images from Unmanned Aerial Systems

Zhengchao Qiu,Xuebin Xu,Changwen Du,Fei Ma,Zhenwang Li

doi:10.3390/rs14061384

Abstract

The rapid and accurate acquisition of rice growth variables using unmanned aerial system (UAS) is useful for assessing rice growth and variable fertilization in precision agriculture. In this study, rice plant height (PH), leaf area index (LAI), aboveground biomass (AGB), and nitrogen nutrient index (NNI) were obtained for different growth periods in field experiments with different nitrogen (N) treatments from 2019–2020. Known spectral indices derived from the visible and NIR images and key rice growth variables measured in the field at different growth periods were used to build a prediction model using the random forest (RF) algorithm. The results showed that the different N fertilizer applications resulted in significant differences in rice growth variables; the correlation coefficients of PH and LAI with visible-near infrared (V-NIR) images at different growth periods were larger than those with visible (V) images while the reverse was true for AGB and NNI. RF models for estimating key rice growth variables were established using V-NIR images and V images, and the results were validated with an R2 value greater than 0.8 for all growth stages. The accuracy of the RF model established from V images was slightly higher than that established from V-NIR images. The RF models were further tested using V images from 2019: R2 values of 0.75, 0.75, 0.72, and 0.68 and RMSE values of 11.68, 1.58, 3.74, and 0.13 were achieved for PH, LAI, AGB, and NNI, respectively, demonstrating that RGB UAS achieved the same performance as multispectral UAS for monitoring rice growth.

Highlights

IntroductionPublisher’s Note: MDPI stays neutralRice is one of the most important crops worldwide and plays an important role in food security [1]
The objectives of this study are (1) to analyze the effects of different nitrogen fertilizer applications on rice growth variables at different growth periods; and (2) to find the vegetation indexes (VIs) from the visible-near infrared (V-NIR) images and the V images that better correlate with the studied rice growth variables
Figure treatments at different periods in the. These results reflect treatments at different periods in the Pukou and Luhe experiments. These results reflect the status of each growth variable influenced by fertilization factors and changes in the status of each growth variable influenced by N fertilization factors and changes in rice rice growth

Summary

Introduction

Publisher’s Note: MDPI stays neutralRice is one of the most important crops worldwide and plays an important role in food security [1]. Rapid and accurate monitoring of key rice growth variables is important for rice fertilizer management [2,3]. Precision fertilization improves the fertilizer utilization rate and rice yield and reduces environmental pollution [4]. Rice growth variables, such as plant height (PH), leaf area index (LAI), and aboveground biomass (AGB), are often used to diagnose crop growth in agricultural management practices [5]. The nitrogen nutrient index (NNI) is a key indicator that can accurately reflect the N nutrition status of different crops and is relatively stable in diagnosing N nutrition status [6,7]. Handheld instruments for obtaining crop growth variables have been developed in recent years. Handheld instruments started to be used for in situ measuring crop growth variables. A common example are chlorophyll meters, which was used to predict leaf nitrogen content from reflectance measurements in the with regard to jurisdictional claims in published maps and institutional affiliations

Objectives

Results

Discussion

Conclusion