Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System

By Jakob Geipel, Johanna Link, Wilhelm Claupein. Remote Sensing, 2014

298 citations recorded by the source corpus.

Precision Farming (PF) management strategies are commonly based on estimations of within-field yield potential, often derived from remotely-sensed products, e.g., Vegetation Index (VI) maps. These well-established means, however, lack important information, like crop height. Combinations of VI-maps and detailed 3D Crop Surface Models (CSMs) enable advanced methods for crop yield prediction. This work utilizes an Unmanned Aircraft System (UAS) to capture standard RGB imagery datasets for corn grain yield prediction at three early- to mid-season growth stages. The imagery is processed into simple VI-orthoimages for crop/non-crop classification and 3D CSMs for crop height determination at different spatial resolutions. Three linear regression models are tested on their prediction ability using site-specific (i) unclassified mean heights, (ii) crop-classified mean heights and (iii) a combination of crop-classified mean heights with according crop coverages. The models show determination coefficients \({R}^{2}\) of up to 0.74, whereas model (iii) performs best with imagery captured at the end of stem elongation and intermediate spatial resolution (0.04m\(\cdot\)px\(^{-1}\)).Following these results, combined spectral and spatial modeling, based on aerial images and CSMs, proves to be a suitable method for mid-season corn yield prediction.

Subfields: Ecology, Environmental Engineering, Geology

View Paper: https://www.semanticscholar.org/paper/befb418f58475b7ebafd1826ba2e60d354eecc80

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Citations in the corpus

Unmanned Aerial Vehicle Remote Sensing for Field-Based Crop Phenotyping: Current Status and Perspectives (2017, background): “Classification of remote sensing images is an important part of image feature analysis and can be used for leaf color monitoring and crop identification (Geipel et al., 2014).”
An overview of current and potential applications of thermal remote sensing in precision agriculture (2017, background): “Using visible and NIR images collected by satellite, traditional aircraft, and unmanned aerial vehicles (UAVs), several studies (Hatfield and Prueger, 2010; Gitelson et al., 2003; Huete et al., 2002; Jordan, 1969) have examined vegetative conditions in agriculture.”
Precision Agriculture: Using Low-Cost Systems to Acquire Low-Altitude Images (2016, methodology): “Other examples include a multiresolution approach with indices used to obtain agricultural terraces,(10) a semisupervised method for weed mapping in sunflower crops with crop row detection,(11) and a vegetation mapping on corn grain crop using UAVs.(12)”
On the Use of Unmanned Aerial Systems for Environmental Monitoring (2018, unknown): “The large availability of UAS equipped with visible (VIS) commercial cameras (see Table A1) has been the main driver for research that has explored the potential use of low-cost sensors for vegetation monitoring [43–46].”
Unmanned aircraft system-derived crop height and normalized difference vegetation index metrics for sorghum yield and aphid stress assessment (2017, background): “The combination of vegetation indices and UAS-based canopy surface models have been shown to estimate biomass in barley and corn more accurately than vegetation indices alone.(15,16) The results from these studies suggest that crop height metrics can be used as a complement to NDVI and potentially a”
Assessment of a canopy height model (CHM) in a vineyard using UAV-based multispectral imaging (2017, background): “Crop heights, generally in the form of CHM, are the most common remotely sensed 3D vegetation measurements (Mathews and Jensen 2013; Bendig et al. 2015; Geipel, Link, and Claupein 2014; Kalisperakis et al. 2015).”
Multi-Temporal Site-Specific Weed Control of Cirsium arvense (L.) Scop. and Rumex crispus L. in Maize and Sugar Beet Using Unmanned Aerial Vehicle Based Mapping (2018, methodology): “Combining this information can provide biomass estimations on barley [12] or yield predictions in maize, when combined with multispectral images [13].”