Reconfigurable Parallel Platforms for Dependable Personalized Health Monitoring

Adeluyi, Olufemi; Lee, Jeong-A

doi:10.1201/b17104-5

Cited by 1 publication

(1 citation statement)

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“…Aboveground biomass (AGB) is an important indicator of crop growth [9]. Accurate measurement of AGB is a prerequisite for reasonable yield estimation during the whole rice crop period [10], while the weight of the spike (SB) in AGB directly reflects the weight of each grain and the percentage of grains in the spike. Therefore, proper monitoring of rice spike biomass is essential for precision agriculture.…”

Section: Introductionmentioning

confidence: 99%

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

Song,

Shao,

Zhu

et al. 2024

Agronomy

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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.

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Section: Introductionmentioning

confidence: 99%