Machine Learning Regression Analysis for Estimation of Crop Emergence Using Multispectral UAV Imagery

Article


Banerjee, Bikram P., Sharma, Vikas, Spangenberg, German and Kant, Surya. 2021. "Machine Learning Regression Analysis for Estimation of Crop Emergence Using Multispectral UAV Imagery." Remote Sensing. 13 (15). https://doi.org/10.3390/rs13152918
Article Title

Machine Learning Regression Analysis for Estimation of Crop Emergence Using Multispectral UAV Imagery

ERA Journal ID201448
Article CategoryArticle
AuthorsBanerjee, Bikram P., Sharma, Vikas, Spangenberg, German and Kant, Surya
Journal TitleRemote Sensing
Journal Citation13 (15)
Article Number2918
Number of Pages18
Year2021
PublisherMDPI AG
Place of PublicationSwitzerland
ISSN2072-4292
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.

Keywordsfield trials; plant count; plant phenotyping; wheat
ANZSRC Field of Research 2020300206. Agricultural spatial analysis and modelling
401304. Photogrammetry and remote sensing
300406. Crop and pasture improvement (incl. selection and breeding)
Byline AffiliationsAgriculture Victoria
La Trobe University
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