|QUIROS VARGAS, JUAN JOSE - Washington State University|
|ZHANG, CHONGYUAN - Washington State University|
|SMITCHGER, JAMIN - Washington State University|
|SANKARAN, SINDHUJA - Washington State University|
Submitted to: Sensors
Publication Type: Peer Reviewed Journal
Publication Acceptance Date: 4/26/2019
Publication Date: 4/30/2019
Citation: Quiros Vargas, J., Zhang, C., Smitchger, J., Mcgee, R.J., Sankaran, S. 2019. Phenotyping of plant biomass and performance traits using remote sensing techniques in pea (Pisum sativum, L). Sensors. 19(9): 2031. https://doi.org/10.3390/s19092031.
Interpretive Summary: Field pea cultivars are constantly improved through breeding programs. In pea breeding, the above ground biomass is frequently measured due to its contribution to seed yield and weed suppression. It is also the primary yield component for peas used as a cover crop or grazing. However, measuring above ground biomass is destructive, and labor and time intensive. Using photographs acquired from digital cameras could be a time and labor saving way to estimate biomass and make breeding of forage peas more efficient. In this research, high resolution red-green-blue and multispectral images acquired with cameras mounted on drones were used to estimate biomass in spring and winter pea breeding plots. In the winter peas, images extracted from photographs taken at flowering were highly correlated with biomass at flowering and with seed yield and days to physiological maturity. In the spring peas, there was significant correlation between images extracted from photographs taken at flowering and biomass at flowering. This study supports the potential of using cameras mounted on drones to estimate biomss and crop performance in pea.
Technical 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 or grazing. Measuring AGBM is destructive, and labor and time intensive. Sensor-based phenotyping can greatly enhance crop breeding efficiency. In this research, high resolution RGB and multispectral images acquired with an unmanned aerial system 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 (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 correlations between image features with days to 50% flowering and days to physiological maturity was observed at about 1725 ADD in winter pea experiments. In the spring pea experiment, the plot volume estimated from images was highly correlated with ground truth canopy height and stress (r = 0.83 and 0.79, respectively) at 1231 ADD. In two winter pea and the spring pea experiments, 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 (Lasso) model for AGBM estimation, the correlation coefficient between the actual and predicted AGBM was 0.63 and 0.87 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. Although, the image features extracted from satellite imagery showed significant correlation with seed yield in two winter field pea experiments, 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.