Machine Learning Regression Analysis for Estimation of Crop Emergence Using Multispectral UAV Imagery
Article
Article Title | Machine Learning Regression Analysis for Estimation of Crop Emergence Using Multispectral UAV Imagery |
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ERA Journal ID | 201448 |
Article Category | Article |
Authors | Banerjee, Bikram P., Sharma, Vikas, Spangenberg, German and Kant, Surya |
Journal Title | Remote Sensing |
Journal Citation | 13 (15) |
Article Number | 2918 |
Number of Pages | 18 |
Year | 2021 |
Publisher | MDPI AG |
Place of Publication | Switzerland |
ISSN | 2072-4292 |
Digital Object Identifier (DOI) | https://doi.org/10.3390/rs13152918 |
Web Address (URL) | https://www.mdpi.com/2072-4292/13/15/2918 |
Abstract | Optimal crop emergence is an important trait in crop breeding for genotypic screening and for achieving potential growth and yield. Emergence is conventionally quantified manually by counting the sub-sections of field plots or scoring; these are less reliable, laborious and inefficient. Remote sensing technology is being increasingly used for high-throughput estimation of agronomic traits in field crops. This study developed a method for estimating wheat seedlings using multispectral images captured from an unmanned aerial vehicle. A machine learning regression (MLR) analysis was used by combining spectral and morphological information extracted from the multispectral images. The approach was tested on diverse wheat genotypes varying in seedling emergence. In this study, three supervised MLR models including regression trees, support vector regression and Gaussian process regression (GPR) were evaluated for estimating wheat seedling emergence. The GPR model was the most effective compared to the other methods, with R2 = 0.86, RMSE = 4.07 and MAE = 3.21 when correlated to the manual seedling count. In addition, imagery data collected at multiple flight altitudes and different wheat growth stages suggested that 10 m altitude and 20 days after sowing were desirable for optimal spatial resolution and image analysis. The method is deployable on larger field trials and other crops for effective and reliable seedling emergence estimates. |
Keywords | field trials; plant count; plant phenotyping; wheat |
ANZSRC Field of Research 2020 | 300206. Agricultural spatial analysis and modelling |
401304. Photogrammetry and remote sensing | |
300406. Crop and pasture improvement (incl. selection and breeding) | |
Byline Affiliations | Agriculture Victoria |
La Trobe University |
https://research.usq.edu.au/item/z3088/machine-learning-regression-analysis-for-estimation-of-crop-emergence-using-multispectral-uav-imagery
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