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.

RICE-YOLO: In-Field Rice Spike Detection Based on Improved YOLOv5 and Drone Images

“…In contrast, drone-captured field rice spike images present challenges such as a larger capture range, minor spikes, and severe occlusion issues at the image edges. Research on drone-based detection has provided valuable insights [29][30][31]. Lyu et al [32] used a drone equipped with a high-resolution camera to capture rice spike images at a height of 1.2 m, and then employed Mask R-CNN for rice spike detection.…”