Abstract
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.
Highlights
Corn (Zea mays L.) biomass and grain yields vary depending on site, climatic conditions and management decisions
Following the idea of Precision Farming (PF), the identification of within-field spatial and temporal variability shows potential to support crop management concepts to meet much of the increasing environmental, economic, market and public pressures on arable agriculture [1]
This study focuses on modeling of corn grain yield with a combined spectral and spatial analysis of aerial imagery
Summary
Corn (Zea mays L.) biomass and grain yields vary depending on site, climatic conditions and management decisions. Following the idea of Precision Farming (PF), the identification of within-field spatial and temporal variability shows potential to support crop management concepts to meet much of the increasing environmental, economic, market and public pressures on arable agriculture [1]. Management strategies account for (i) environmental issues by adapting the input factors to the demand of the crop and, avoid over- or under-application [2,3], (ii) economic issues by calculating within-field net returns [4] and (iii) possibilities to improve the control and influence of the quality of the product [5]. Yield estimations prior to harvest play a key role in the determination of input factors, like nutrients, pesticides and water, as well as for the planning of upcoming labor- and cost-intensive actions, like harvesting, drying and storage.
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