Estimating Biomass of Barley Using Crop Surface Models (CSMs) Derived from UAV-Based RGB Imaging

By Juliane Bendig, Andreas Bolten, Simon Bennertz, Janis Broscheit, Silas Eichfuss, Georg Bareth. Remote Sensing, 2014

630 citations recorded by the source corpus.

Crop monitoring is important in precision agriculture. Estimating above-ground biomass helps to monitor crop vitality and to predict yield. In this study, we estimated fresh and dry biomass on a summer barley test site with 18 cultivars and two nitrogen (N)-treatments using the plant height (PH) from crop surface models (CSMs). The super-high resolution, multi-temporal (1 cm/pixel) CSMs were derived from red, green, blue (RGB) images captured from a small unmanned aerial vehicle (UAV). Comparison with PH reference measurements yielded an R2 of 0.92. The test site with different cultivars and treatments was monitored during “Biologische Bundesanstalt, Bundessortenamt und CHemische Industrie” (BBCH) Stages 24–89. A high correlation was found between PH from CSMs and fresh biomass (R2 = 0.81) and dry biomass (R2 = 0.82). Five models for above-ground fresh and dry biomass estimation were tested by cross-validation. Modelling biomass between different N-treatments for fresh biomass produced the best results (R2 = 0.71). The main limitation was the influence of lodging cultivars in the later growth stages, producing irregular plant heights. The method has potential for future application by non-professionals, i.e., farmers.

Subfields: Ecology, Environmental Engineering, Geology

View Paper: https://www.semanticscholar.org/paper/b22697902076b38ce9440e01d05753e30d2a9cbb

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Citations in the corpus

Modeling maize above-ground biomass based on machine learning approaches using UAV remote-sensing data (2019, methodology): “Several studies have already used CSM to estimate plant height and biomass for various crops, including maize [15–17], rice [18], barley [19, 20], cotton [21, 22], sugarcane [23], wheat [24] and sorghum [16, 25].”
Estimating the Leaf Area Index of Winter Wheat Based on Unmanned Aerial Vehicle RGB-Image Parameters (2019, background): “[18], UAV-based RGB images have potential for estimating biomass in the future application by non-professionals, i.”
Using UAV Borne, Multi-Spectral Imaging for the Field Phenotyping of Shoot Biomass, Leaf Area Index and Height of West African Sorghum Varieties under Two Contrasted Water Conditions (2021, background): “Previous studies already demonstrated the potential of UAV phenotyping for capturing vegetation index like Normalized Difference Vegetation Index (NDVI) and assess the seasonal leaf area dynamics of sorghum [31] and plants biomass growth in barley [32].”
Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives (2017, methodology): “…imager, LIDAR, combing the methods including empirical statistical analysis of vegetation indices, estimation based on net primary productivity, and crop growth model have been widely deployed for biomass estimation (Hunt et al., 2005; Swain et al., 2010; Wallace et al., 2012; Bendig et al., 2014).”
Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research (2016, background): “Consumer-level digital cameras provide low-cost high-resolution images that can be used for SfM and measurement of some plant phenotypes including population, height, lodging, flowering time and yield [23 – 26].”
Agricultural remote sensing big data: Management and applications (2018, background): “2012; Mathews and Jensen 2013) for extraction of surface features such as plant height and biomass volume (Bendig et al. 2014; Huang et al. 2017).”