Journal article

Machine learning regression analysis for estimation of crop emergence using multispectral uav imagery

BP Banerjee, V Sharma, G Spangenberg, S Kant

Remote Sensing | Published : 2021

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 wa..

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