Multi-Temporal Predictive Modelling of Sorghum Biomass Using UAV-Based Hyperspectral and LiDAR Data

Abstract

High-throughput phenotyping using high spatial, spectral, and temporal resolution remote sensing (RS) data has become a critical part of the plant breeding chain focused on reducing the time and cost of the selection process for the “best” genotypes with respect to the trait(s) of interest. In this paper, the potential of accurate and reliable sorghum biomass prediction using visible and near infrared (VNIR) and short-wave infrared (SWIR) hyperspectral data as well as light detection and ranging (LiDAR) data acquired by sensors mounted on UAV platforms is investigated. Predictive models are developed using classical regression-based machine learning methods for nine experiments conducted during the 2017 and 2018 growing seasons at the Agronomy Center for Research and Education (ACRE) at Purdue University, Indiana, USA. The impact of the regression method, data source, timing of RS and field-based biomass reference data acquisition, and the number of samples on the prediction results are investigated. R2 values for end-of-season biomass ranged from 0.64 to 0.89 for different experiments when features from all the data sources were included. Geometry-based features derived from the LiDAR point cloud to characterize plant structure and chemistry-based features extracted from hyperspectral data provided the most accurate predictions. Evaluation of the impact of the time of data acquisition during the growing season on the prediction results indicated that although the most accurate and reliable predictions of final biomass were achieved using remotely sensed data from mid-season to end-of-season, predictions in mid-season provided adequate results to differentiate between promising varieties for selection. The analysis of variance (ANOVA) of the accuracies of the predictive models showed that both the data source and regression method are important factors for a reliable prediction; however, the data source was more important with 69% significance, versus 28% significance for the regression method.

Estimation of Maize Yield and Flowering Time Using Multi-Temporal UAV-Based Hyperspectral Data

“…However, the higher-yield plots tended to have significantly higher reflectance values in the NIR region (800-1000 nm, Figure 3c,f,i) throughout the growing season. Similar findings were also reported on other crops, such as wheat [70][71][72][73], alfalfa [55,74], sorghum [75], and soybean [21,50,76]. This is because higher NIR reflectance values indicate more chlorophyll within plants, representing greater vegetation vigor and health status [77][78][79].…”

“…Comparatively, the RF and SVR models are more prone to the multicollinearity issue. It was also reported in [75,88] that tree-based models such as the RF are easily overfitted with a small training data size and a high feature dimension. It can also be seen from Table 3 that the RF models with 81 VIs outperformed the ones with the full spectra.…”

Seedling growth and fall armyworm feeding preference influenced by dhurrin production in sorghum

“…Many studies have demonstrated the value of image-based sorghum phenotyping technologies for assessment of plant growth and development (Neilson et al 2015 ; Batz et al 2016 ; Thapa et al 2018 ; Masjedi et al 2020 ). In this study, the ARIS phenotyping platform was shown to be useful for collecting nondestructive measurements of green plant area (GPA) in sorghum.…”

What can aerial phenotyping do and bring to us (breeders)?

“…The first marked deployment and utilisation of drones was accomplished during World War II, and the first evidence of using drones for agriculture was seen with crop spraying in the 1980s (Radoglou-Grammatikis et al, 2020). The use of drones has proved an efficient and effective technique for field and plot plant phenotyping because of its capabilities in: (1) the required throughput, that is UAVs can collect huge quantities of data 'on demand' (Masjedi et al, 2020);…”