Development and Validation of a Phenotyping Computational Workflow to Predict the Biomass Yield of a Large Perennial Ryegrass Breeding Field Trial

Abstract

Increasing dry matter yield (DMY) is the most important objective in perennial ryegrass breeding programs. Current yield assessment methods like cutting are time-consuming and destructive, non-destructive measures such as scoring yield on single plants by visual inspection may be subjective. These assessments involve multiple measurements and selection procedures across seasons and years to evaluate biomass yield repeatedly. This contributes to the slow process of new cultivar development and commercialisation. This study developed and validated a computational phenotyping workflow for image acquisition, processing and analysis of spaced planted ryegrass and investigated sensor-based DMY yield estimation of individual plants through normalized difference vegetative index (NDVI) and ultrasonic plant height data extraction. The DMY of 48,000 individual plants representing 50 advanced breeding lines and commercial cultivars was accurately estimated at multiple harvests across the growing season. NDVI, plant height and predicted DMY obtained from aerial and ground-based sensors illustrated the variation within and between cultivars across different seasons. Combining NDVI and plant height of individual plants was a robust method to enable highthroughput phenotyping of biomass yield in ryegrass breeding. Similarly, the plot-level model indicated good to high-coefficients of determination (R 2) between the predicted and measured DMY across three seasons with R 2 between 0.19 and 0.81 and root mean square errors (RMSE) values ranging from 0.09 to 0.21 kg/plot. The model was further validated using a combined regression of the three seasons harvests. This study further sets a foundation for the application of sensor technologies combined with genomic studies that lead to greater rates of genetic gain in perennial ryegrass biomass yield.

Combining NDVI and Bacterial Blight Score to Predict Grain Yield in Field Pea

“…These flight parameters delivered high-resolution orthomosaics with a ground sampling distance of 2.69 cm. The acquired images were processed using the software Pix4D mapper Pro (Pix4D, Lausanne, Switzerland) to generate high-resolution orthomosaics and NDVI maps (Gebremedhin et al, 2020 ). In brief, the images were imported into the Pix4D software and radiometrically corrected using a known reflectance source, the Airinov calibration plate (Airinov, Paris, France), to generate orthomosaics and NDVI TIFF images.…”

Breeding has selected for architectural and photosynthetic traits in lentils

“…The Pix4D mapper (Pix4D, Lausanne, Switzerland) software was used to pre-process the images to obtain a map with a digital surface model (DSM) and a digital terrain model (DTM). The CHM was generated by pixelwise subtraction of DTM from DSM from rectangular polygons with a unique plot number ( Gebremedhin et al, 2020 ).…”

Within and combined season prediction models for perennial ryegrass biomass yield using ground- and air-based sensor data

“…where NIR and RED are spectral bands at wavelengths 820-860 and 663-673 nm, respectively. Polygons of individual rows were created based on the NDVI image and linked to identifiers (ID) following Gebremedhin et al (2020). Polygons were then used to extract per row NDVI, plant height, and LiDAR data for downstream analysis.…”