Estimation of Plant Height and Biomass of Rice Using Unmanned Aerial Vehicle

Abstract

Plant height and biomass are important indicators of rice yield. Here we combined measured plant physiological traits with a crop growth model driven by unmanned aerial vehicle spectral data to quantify the changes in rice plant height and biomass under different irrigation and fertilizer treatments. The study included two treatments: I—water availability factor (i.e., three drought objects, optimal, and excess water); and II—two levels of deep percolation and five nitrogen fertilization doses. The introduced model is extreme learning machine (ELM), back propagation neural network (BPNN), and particle swarm optimization-ELM (PSO-ELM), respectively. The results showed that: (1) Proper water level regulation (3~5 cm) significantly increased the accumulation of spike biomass, which was about 6% higher compared to that under flooded conditions. (2) For plant height inversion, the ELM model was optimal with a mean coefficient of determination of 0.78, a mean root mean square error of 0.26 cm, and a mean performance deviation rate of 2.08. For biomass inversion, the PSO-ELM model was optimal with a mean coefficient of determination of 0.88, a mean root mean square error of 3.8 g, and a mean performance deviation rate of 3.29. This study provided the possible opportunity for large-scale estimations of rice yield under environmental disturbances.

Plant phenotyping relevance

UAVスペクトルデータと機械学習モデルによりイネの草丈・バイオマスを推定し、複数モデルの性能を評価しており、形質取得手法が研究の中心である。

Code and data availability

The supplied blocks describe UAV multispectral imagery, ground-measured plant height/biomass, and ELM/BPNN/PSO-ELM models, but contain no data availability statement, public dataset deposit, or author code repository with a public URL. No paper-specific public asset is identified.

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