Phenotyping of Plant Biomass and Performance Traits Using Remote Sensing Techniques in Pea (Pisum sativum, L.)

Abstract

Field pea cultivars are constantly improved through breeding programs to enhance biotic and abiotic stress tolerance and increase seed yield potential. In pea breeding, the Above Ground Biomass (AGBM) is assessed due to its influence on seed yield, canopy closure, and weed suppression. It is also the primary yield component for peas used as a cover crop and/or grazing. Measuring AGBM is destructive and labor-intensive process. Sensor-based phenotyping of such traits can greatly enhance crop breeding efficiency. In this research, high resolution RGB and multispectral images acquired with unmanned aerial systems were used to assess phenotypes in spring and winter pea breeding plots. The Green Red Vegetation Index (GRVI), Normalized Difference Vegetation Index (NDVI), Normalized Difference Red Edge Index (NDRE), plot volume, canopy height, and canopy coverage were extracted from RGB and multispectral information at five imaging times (between 365 to 1948 accumulated degree days/ADD after 1 May) in four winter field pea experiments and at three imaging times (between 1231 to 1648 ADD) in one spring field pea experiment. The image features were compared to ground-truth data including AGBM, lodging, leaf type, days to 50% flowering, days to physiological maturity, number of the first reproductive node, and seed yield. In two of the winter pea experiments, a strong correlation between image features and seed yield was observed at 1268 ADD (flowering). An increase in correlation between image features with the phenological traits such as days to 50% flowering and days to physiological maturity was observed at about 1725 ADD in these winter pea experiments. In the spring pea experiment, the plot volume estimated from images was highly correlated with ground truth canopy height ( r = 0.83) at 1231 ADD. In two other winter pea experiments and the spring pea experiment, the GRVI and NDVI features were significantly correlated with AGBM at flowering. When selected image features were used to develop a least absolute shrinkage and selection operator model for AGBM estimation, the correlation coefficient between the actual and predicted AGBM was 0.60 and 0.84 in the winter and spring pea experiments, respectively. A SPOT-6 satellite image (1.5 m resolution) was also evaluated for its applicability to assess biomass and seed yield. The image features extracted from satellite imagery showed significant correlation with seed yield in two winter field pea experiments, however, the trend was not consistent. In summary, the study supports the potential of using unmanned aerial system-based imaging techniques to estimate biomass and crop performance in pea breeding programs.

Plant phenotyping relevance

UASのRGB・マルチスペクトル画像からバイオマス、草丈、被覆率などの植物形質を抽出・推定し、地上真値との相関およびLASSOモデルで技術評価しており、フェノタイピング手法が研究の中心である。

Code and data availability

The supplied blocks contain no public phenotype/trait datasets, UAS/satellite imagery, author analysis code, or trained model deposits. The paper describes UAS RGB/multispectral and SPOT-6 imagery analysis and Lasso modeling, but no data or code availability statement with a public authors' URL appears; the only URLs (

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