Abstract
Traditional methods to measure spatio-temporal variations in above-ground biomass dynamics (AGBD) predominantly rely on the extraction of several vegetation-index features highly associated with AGBD variations through the phenological crop cycle. This work presents a comprehensive comparison between two different approaches for feature extraction for non-destructive biomass estimation using aerial multispectral imagery. The first method is called GFKuts, an approach that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo-based K-means, and a guided image filtering for the extraction of canopy vegetation indices associated with biomass yield. The second method is based on a Graph-Based Data Fusion (GBF) approach that does not depend on calculating vegetation-index image reflectances. Both methods are experimentally tested and compared through rice growth stages: vegetative, reproductive, and ripening. Biomass estimation correlations are calculated and compared against an assembled ground-truth biomass measurements taken by destructive sampling. The proposed GBF-Sm-Bs approach outperformed competing methods by obtaining biomass estimation correlation of with and g. This result increases the precision in the biomass estimation by around compared to previous works.
Highlights
Rice is an essential grain to ensure global food security [1], contributing to 20% of food energy requirements worldwide
Above-Ground Biomass Estimation (AGBE) methods have recently gained significant traction [7,8,9,10], since machine learning models can be trained by features extracted according to different plant reflectance wavelengths, namely vegetation indices (VIs)
In order to compare the effectiveness of the proposed feature extraction based on graphs, the results were compared with two approaches: (i) the former graph method introduced in [4], namely Graph-Based Data Fusion (GBF), and (ii) the GFKuts approach [3]
Summary
Rice is an essential grain to ensure global food security [1], contributing to 20% of food energy requirements worldwide. Monitoring rice biomass at larger crop scales requires remote sensing approaches for precision sampling, mostly based on unmanned aerial vehicle (UAV-driven) multispectral imagery [3,4,5,6]. For this purpose, Above-Ground Biomass Estimation (AGBE) methods have recently gained significant traction [7,8,9,10], since machine learning models can be trained by features extracted according to different plant reflectance wavelengths, namely vegetation indices (VIs). This characterization runs only once and operates as long as the conditions of the images do not change (e.g., the type of crop, growing, or environmental conditions)
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