Improving Accuracy of Herbage Yield Predictions in Perennial Ryegrass with UAV-Based Structural and Spectral Data Fusion and Machine Learning
| Autor: | Ivan A. Janssens, Tom De Swaef, Jonas Aper, Joanna Pranga, An Ghesquiere, Paul Quataert, Greet Ruysschaert, Peter Lootens, Isabel Roldán-Ruiz, Irene Borra-Serrano |
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| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
0106 biological sciences
Canopy 010504 meteorology & atmospheric sciences Mean squared error Economics Science Multispectral image high-throughput field phenotyping (HTFP) RGB sensor Machine learning computer.software_genre 01 natural sciences Partial least squares regression pasture forage multispectral sensor close remote sensing partial least squares regression (PLSR) random forest (RF) support vector machines (SVM) Biology Image resolution 0105 earth and related environmental sciences Mathematics business.industry Physics Vegetation Random forest Support vector machine Chemistry General Earth and Planetary Sciences Artificial intelligence business Engineering sciences. Technology computer 010606 plant biology & botany |
| Zdroj: | Remote Sensing; Volume 13; Issue 17; Pages: 3459 Remote Sensing, Vol 13, Iss 3459, p 3459 (2021) Remote sensing |
| ISSN: | 2072-4292 |
| DOI: | 10.3390/rs13173459 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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