Abstract
High-throughput field phenotyping using close remote sensing platforms and sensors for non-destructive assessment of plant traits can support the objective evaluation of yield predictions of large breeding trials. The main objective of this study was to examine the potential of unmanned aerial vehicle (UAV)-based structural and spectral features and their combination in herbage yield predictions across diploid and tetraploid varieties and breeding populations of perennial ryegrass (Lolium perenne L.). Canopy structural (i.e., canopy height) and spectral (i.e., vegetation indices) information were derived from data gathered with two sensors: a consumer-grade RGB and a 10-band multispectral (MS) camera system, which were compared in the analysis. A total of 468 field plots comprising 115 diploid and 112 tetraploid varieties and populations were considered in this study. A modelling framework established to predict dry matter yield (DMY), was used to test three machine learning algorithms, including Partial Least Squares Regression (PLSR), Random Forest (RF), and Support Vector Machines (SVM). The results of the nested cross-validation revealed: (a) the fusion of structural and spectral features achieved better DMY estimates as compared to models fitted with structural or spectral data only, irrespective of the sensor, ploidy level or machine learning algorithm applied; (b) models built with MS-based predictor variables, despite their lower spatial resolution, slightly outperformed the RGB-based models, as lower mean relative root mean square error (rRMSE) values were delivered; and (c) on average, the RF technique reported the best model performances among tested algorithms, regardless of the dataset used. The approach introduced in this study can provide accurate yield estimates (up to an RMSE = 308 kg ha−1) and useful information for breeders and practical farm-scale applications.
Highlights
Grasslands cover up to 40% of the earth’s landmass
Borra-Serrano et al [29] found that models built with unmanned aerial vehicle (UAV)-derived canopy height information based on an RGB sensor achieved lower relative root mean square error (rRMSE) values (27.6%) than models based on rising plate meter (RPM) data (31%)
This study investigated the potential of UAV-based structural and spectral features and their fusion to predict dry matter yield in perennial ryegrass
Summary
Grasslands cover up to 40% of the earth’s landmass. Grasslands have great ecological and economical relevance, as they supply essential goods and services at the local, regional, and global levels [1,2]. Progress and innovations in remote and proximal sensing, computer sciences, and electronics fill the gap between genomic and phenotypic data [13], and it is anticipated that the development and implementation of high-throughput phenotyping technologies will promote the acceleration of the breeding progress in forage species [14]. This situation has created high demand for rapid, nondestructive and HTFP protocols [15]. The use of remotely sensed data to monitor spectral responses can help optimize breeding and grassland management [16]
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