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Review

Current Status and Future Opportunities for Grain Protein Prediction Using On- and Off-Combine Sensors: A Synthesis-Analysis of the Literature

1
Department of Agronomy, Kansas State University, Manhattan, KS 66502, USA
2
Department of Biological and Agricultural Engineering, Kansas State University, Manhattan, KS 66502, USA
3
John Deere, 7100 NW 62nd 18 Ave., Johnston, IA 50131, USA
*
Author to whom correspondence should be addressed.
Academic Editor: Krzysztof Kusnierek
Remote Sens. 2021, 13(24), 5027; https://doi.org/10.3390/rs13245027
Received: 23 October 2021 / Revised: 3 December 2021 / Accepted: 6 December 2021 / Published: 10 December 2021
(This article belongs to the Special Issue Proximal and Remote Sensing for Precision Crop Management)
The spatial information about crop grain protein concentration (GPC) can be an important layer (i.e., a map that can be utilized in a geographic information system) with uses from nutrient management to grain marketing. Recently, on- and off-combine harvester sensors have been developed for creating spatial GPC layers. The quality of these GPC layers, as measured by the coefficient of determination (R2) and the root mean squared error (RMSE) of the relationship between measured and predicted GPC, is affected by different sensing characteristics. The objectives of this synthesis analysis were to (i) contrast GPC prediction R2 and RMSE for different sensor types (on-combine, off-combine proximal and remote); (ii) contrast and discuss the best spatial, temporal, and spectral resolutions and features, and the best statistical approach for off-combine sensors; and (iii) review current technology limitations and provide future directions for spatial GPC research and application. On-combine sensors were more accurate than remote sensors in predicting GPC, yet with similar precision. The most optimal conditions for creating reliable GPC predictions from off-combine sensors were sensing near anthesis using multiple spectral features that include the blue and green bands, and that are analyzed by complex statistical approaches. We discussed sensor choice in regard to previously identified uses of a GPC layer, and further proposed new uses with remote sensors including same season fertilizer management for increased GPC, and in advance segregated harvest planning related to field prioritization and farm infrastructure. Limitations of the GPC literature were identified and future directions for GPC research were proposed as (i) performing GPC predictive studies on a larger variety of crops and water regimes; (ii) reporting proper GPC ground-truth calibrations; (iii) conducting proper model training, validation, and testing; (iv) reporting model fit metrics that express greater concordance with the ideal predictive model; and (v) implementing and benchmarking one or more uses for a GPC layer. View Full-Text
Keywords: grain protein sensing; combine harvester sensors; proximal sensing; hand held sensors; remote sensors grain protein sensing; combine harvester sensors; proximal sensing; hand held sensors; remote sensors
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MDPI and ACS Style

Bastos, L.M.; Froes de Borja Reis, A.; Sharda, A.; Wright, Y.; Ciampitti, I.A. Current Status and Future Opportunities for Grain Protein Prediction Using On- and Off-Combine Sensors: A Synthesis-Analysis of the Literature. Remote Sens. 2021, 13, 5027. https://doi.org/10.3390/rs13245027

AMA Style

Bastos LM, Froes de Borja Reis A, Sharda A, Wright Y, Ciampitti IA. Current Status and Future Opportunities for Grain Protein Prediction Using On- and Off-Combine Sensors: A Synthesis-Analysis of the Literature. Remote Sensing. 2021; 13(24):5027. https://doi.org/10.3390/rs13245027

Chicago/Turabian Style

Bastos, Leonardo M., Andre Froes de Borja Reis, Ajay Sharda, Yancy Wright, and Ignacio A. Ciampitti. 2021. "Current Status and Future Opportunities for Grain Protein Prediction Using On- and Off-Combine Sensors: A Synthesis-Analysis of the Literature" Remote Sensing 13, no. 24: 5027. https://doi.org/10.3390/rs13245027

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