The DOI matches its Crossref record; 27 of the 27 references it cites were found in a registry or an index.
Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System
Open accessAbstract
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.
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Cited by
Unmanned Aerial Vehicles (UAVs): A Survey on Civil Applications and Key Research Challenges
Shakhatreh, H., Sawalmeh, A. H., Al-Fuqaha, A., Dou, Z., Almaita, E., Khalil, I., Othman, N. S., Khreishah, A., & Guizani, M. (2019) · IEEE Access · doi:10.1109/access.2019.2909530 · Cited by 2,529
Holman, F., Riche, A., Michalski, A., Castle, M., Wooster, M., & Hawkesford, M. (2016) · Remote Sensing · doi:10.3390/rs8121031 · Cited by 398
Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research
Shi, Y., Thomasson, J. A., Murray, S. C., Pugh, N. A., Rooney, W. L., Shafian, S., Rajan, N., Rouze, G., Morgan, C. L. S., Neely, H. L., et al. (2016) · PLOS ONE · doi:10.1371/journal.pone.0159781 · Cited by 334
What good are unmanned aircraft systems for agricultural remote sensing and precision agriculture?
Hunt, E. R., & Daughtry, C. S. T. (2018) · International Journal of Remote Sensing · doi:10.1080/01431161.2017.1410300 · Cited by 325
De Castro, A., Torres-Sánchez, J., Peña, J., Jiménez-Brenes, F., Csillik, O., & López-Granados, F. (2018) · Remote Sensing · doi:10.3390/rs10020285 · Cited by 283
Metadata
- Authors
- Geipel, J., Link, J., & Claupein, W.
- Venue
- Remote Sensing
- Publisher
- MDPI AG
- Published
- 2014-10-27
- Volume
- 6
- Issue
- 11
- Pages
- 10335-10355
- ISSN
- 2072-4292 (ISSN-L)
- OpenAlex
- W2087991080
- Cited by
- 326
- References
- 27
- FWCI
- 12.87
- Type
- journal article
- Language
- en
- Open access
- gold
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- Geipel, J., Link, J., & Claupein, W. (2014). Combined Spectral and Spatial Modeling of Corn Yield Based on Aerial Images and Crop Surface Models Acquired with an Unmanned Aircraft System. Remote Sensing. https://doi.org/10.3390/rs61110335
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