Kitagenki, a high-yielding rice variety, exhibits a high yield potential under optimum crop management practices

Yagioka, Atsushi; Hayashi, Satoshi; Kimiwada, Kenji; Kondo, Motohiko

doi:10.1016/j.eja.2022.126606

Cited by 2 publications

(1 citation statement)

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“…However, yields in rain-fed systems can also be high under favorable environmental and management conditions [3]. Various initiatives are in place to improve rice productivity and sustainability, including promoting high-yielding varieties and providing technical assistance and training to farmers [4][5][6]. One topic of interest is plant breeding, where, through the selection of favorable genes and higher-level omics characterization [7], optimal rice varieties are obtained for cultivation.…”

Section: Introductionmentioning

confidence: 99%

Characterization of Rice Yield Based on Biomass and SPAD-Based Leaf Nitrogen for Large Genotype Plots

Duque

Patino

Colorado

et al. 2023

Sensors

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The use of Unmanned Aerial Vehicle (UAV) images for biomass and nitrogen estimation offers multiple opportunities for improving rice yields. UAV images provide detailed, high-resolution visual information about vegetation properties, enabling the identification of phenotypic characteristics for selecting the best varieties, improving yield predictions, and supporting ecosystem monitoring and conservation efforts. In this study, an analysis of biomass and nitrogen is conducted on 59 rice plots selected at random from a more extensive trial comprising 400 rice genotypes. A UAV acquires multispectral reflectance channels across a rice field of subplots containing different genotypes. Based on the ground-truth data, yields are characterized for the 59 plots and correlated with the Vegetation Indices (VIs) calculated from the photogrammetric mapping. The VIs are weighted by the segmentation of the plants from the soil and used as a feature matrix to estimate, via machine learning models, the biomass and nitrogen of the selected rice genotypes. The genotype IR 93346 presented the highest yield with a biomass gain of 10,252.78 kg/ha and an average daily biomass gain above 49.92 g/day. The VIs with the highest correlations with the ground-truth variables were NDVI and SAVI for wet biomass, GNDVI and NDVI for dry biomass, GNDVI and SAVI for height, and NDVI and ARVI for nitrogen. The machine learning model that performed best in estimating the variables of the 59 plots was the Gaussian Process Regression (GPR) model with a correlation factor of 0.98 for wet biomass, 0.99 for dry biomass, and 1 for nitrogen. The results presented demonstrate that it is possible to characterize the yields of rice plots containing different genotypes through ground-truth data and VIs.

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