Time Series Field Estimation of Rice Canopy Height Using an Unmanned Aerial Vehicle-Based RGB/Multispectral Platform

• Published in: Agronomy (Basel) 2024-05, Vol.14 (5), p.883
• Main Authors: Li, Ziqiu, Feng, Xiangqian, Li, Juan, Wang, Danying, Hong, Weiyuan, Qin, Jinhua, Wang, Aidong, Ma, Hengyu, Yao, Qin, Chen, Song
• Format: Article
• Language: English
• Subjects: Canopies; Clusters; Color imagery; Crop yield; Crops; Curve fitting; Data collection; digital surface model; Drone aircraft; Estimation; Growing season; Growth stage; Infrared imagery; Monoculture; Performance indices; Plant breeding; Quantiles; Regression analysis; Research methodology; Rice; rice canopy height estimation; Sensors; two-stage linear regression; unmanned aerial vehicle; Unmanned aerial vehicles; Vegetation; Vegetation index
• ISSN: 2073-4395; 2073-4395
• DOI: 10.3390/agronomy14050883

Crop plant height is a critical parameter for assessing crop physiological properties, such as above-ground biomass and grain yield and crop health. Current dominant plant height estimation methods are based on digital surface model (DSM) and vegetation indexes (VIs). However, DSM-based methods usually estimate plant height by growth stages, which would result in some discontinuity between growth stages due to different fitting curves. Additionally, there has been limited research on the application of VI-based plant height estimation for multiple crop species. Thus, this study investigated the validity and challenges associated with these methods for estimating canopy heights of multi-variety rice throughout the entire growing season. A total of 474 rice varieties were cultivated in a single season, and RGB images including red, green, and blue bands, DSMs, multispectral images including near infrared and red edge bands, and manually measured plant heights were collected in 2022. DSMs and 26 commonly used VIs were employed to estimate rice canopy heights during the growing season. The plant height estimation using DSMs was performed using different quantiles (50th, 75th, and 95th), while two-stage linear regression (TLR) models based on each VI were developed. The DSM-based method at the 95th quantile showed high accuracy, with an R2 value of 0.94 and an RMSE value of 0.06 m. However, the plant height estimation at the early growth stage showed lower effectiveness, with an R2 < 0. For the VIs, height estimation with MTCI yielded the best results, with an R2 of 0.704. The first stage of the TLR model (maximum R2 = 0.664) was significantly better than the second stage (maximum R2 = 0.133), which indicated that the VIs were more suitable for estimating canopy height at the early growth stage. By grouping the 474 varieties into 15 clusters, the R2 values of the VI-based TLR models exhibited inconsistencies across clusters (maximum R2 = 0.984; minimum R2 = 0.042), which meant that the VIs were suitable for estimating canopy height in the cultivation of similar or specific rice varieties. However, the DSM-based method showed little difference in performance among the varieties, which meant that the DSM-based method was suitable for multi-variety rice breeding. But for specific clusters, the VI-based methods were better than the DSM-based methods for plant height estimation. In conclusion, the DSM-based method at the 95th quantile was suitable for plant height e