Predicting In-Season Corn Grain Yield Using Optical Sensors
Abstract
:1. Introduction
Sensor-Based Datasets and Corn Correlations
2. Materials and Methods
2.1. Sensor Technologies
2.2. Data Processing
2.3. Vegetation Indices
2.4. Calculations and Statistics
3. Results
3.1. Best Method for Sensor-Based Grain Yield Predictions
3.2. Comparison of VIs for Sensor-Based Yield Prediction
3.3. Comparison of Sensors for Sensor-Based Yield Prediction
3.4. Comparison of Growth Stages for Sensor-Based Yield Prediction
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
References
- Stewart, W.M.; Dibb, D.W.; Johnston, A.E.; Smyth, T.J. The Contribution of Commercial Fertilizer Nutrients to Food Production. Agron. J. 2005, 97, 322–332. [Google Scholar] [CrossRef][Green Version]
- Foley, J.A.; Ramankutty, N.; Brauman, K.A.; Cassidy, E.S.; Gerber, J.S.; Johnston, M.; Mueller, N.D.; O’Connell, C.; Ray, D.K.; West, P.C.; et al. Solutions for a cultivated planet. Nature 2011, 478, 337–342. [Google Scholar] [CrossRef] [PubMed][Green Version]
- Raun, W.R.; Johnson, G.V. Improving Nitrogen Use Efficiency for Cereal Production. Agron. J. 1999, 91, 357–363. [Google Scholar] [CrossRef][Green Version]
- Raun, W.; Figueiredo, B.; Dhillon, J.; Fornah, A.; Bushong, J.; Zhang, H.; Taylor, R. Can Yield Goals Be Predicted? Agron. J. 2017, 109, 2389–2395. [Google Scholar] [CrossRef][Green Version]
- Rodriguez, D.G.P.; Bullock, D.S.; Boerngen, M.A. The Origins, Implications, and Consequences of Yield-Based Nitrogen Fertilizer Management. Agron. J. 2019, 111, 725–735. [Google Scholar] [CrossRef][Green Version]
- Morris, T.F.; Murrell, T.S.; Beegle, D.B.; Camberato, J.J.; Ferguson, R.B.; Grove, J.; Ketterings, Q.; Kyveryga, P.M.; Laboski, C.A.M.; McGrath, J.M.; et al. Strengths and Limitations of Nitrogen Rate Recommendations for Corn and Opportunities for Improvement. Agron. J. 2018, 110, 1–37. [Google Scholar] [CrossRef][Green Version]
- Oglesby, C.; Dhillon, J.; Fox, A.; Singh, G.; Ferguson, C.; Li, X.; Kumar, R.; Dew, J.; Varco, J. Discrepancy between the crop yield goal and optimum nitrogen rates for maize production in Mississippi. Agron. J. 2022. [Google Scholar] [CrossRef]
- Raun, W.R.; Solie, J.B.; Johnson, G.V.; Stone, M.L.; Lukina, E.V.; Thomason, W.E.; Schepers, J.S. In-Season Prediction of Potential Grain Yield in Winter Wheat Using Canopy Reflectance. Agron. J. 2001, 93, 131–138. [Google Scholar] [CrossRef][Green Version]
- Ali, A.; Thind, H.; Singh, V.; Singh, B. A framework for refining nitrogen management in dry direct-seeded rice using GreenSeeker™ optical sensor. Comput. Electron. Agric. 2015, 110, 114–120. [Google Scholar] [CrossRef]
- Dhillon, J.; Aula, L.; Eickhoff, E.; Raun, W. Predicting in-season maize (Zea mays L.) yield potential using crop sensors and climatological data. Sci. Rep. 2020, 10, 11479. [Google Scholar] [CrossRef]
- Dhital, S.; Raun, W.R. Variability in Optimum Nitrogen Rates for Maize. Agron. J. 2016, 108, 2165–2173. [Google Scholar] [CrossRef][Green Version]
- Bushong, J.T.; Mullock, J.L.; Arnall, D.B.; Raun, W.R. Effect of nitrogen fertilizer source on corn (Zea mays L.) optical sensor response index values in a rain-fed environment. J. Plant Nutr. 2018, 41, 1172–1183. [Google Scholar] [CrossRef]
- Raun, W.R.; Solie, J.B.; Johnson, G.V.; Stone, M.L.; Mullen, R.W.; Freeman, K.W.; Thomason, W.E.; Lukina, E.V. Improving Nitrogen Use Efficiency in Cereal Grain Production with Optical Sensing and Variable Rate Application. Agron. J. 2002, 94, 815–820. [Google Scholar] [CrossRef][Green Version]
- Paiao, G.D.; Fernández, F.G.; Spackman, J.A.; Kaiser, D.E.; Weisberg, S. Ground-based optical canopy sensing technologies for corn–nitrogen management in the Upper Midwest. Agron. J. 2020, 112, 2998–3011. [Google Scholar] [CrossRef]
- Ritchie, S.W.; Hanway, J.J.; Benson, G.O. How a Corn Plant Develops; Sp. Rpt. 48; Iowa Agricultural and Home Economics Experiment Station Publications; Iowa State University of Science and Technology, Cooperative Extension: Ames, IA, USA, 1986; Volume 48, pp. 1–21. [Google Scholar]
- Martin, K.L.; Girma, K.; Freeman, K.W.; Teal, R.K.; Tubańa, B.; Arnall, D.B.; Chung, B.; Walsh, O.; Solie, J.B.; Stone, M.L.; et al. Expression of Variability in Corn as Influenced by Growth Stage Using Optical Sensor Measurements. Agron. J. 2007, 99, 384–389. [Google Scholar] [CrossRef][Green Version]
- Tagarakis, A.C.; Ketterings, Q.M. In-Season Estimation of Corn Yield Potential Using Proximal Sensing. Agron. J. 2017, 109, 1323–1330. [Google Scholar] [CrossRef][Green Version]
- Sharma, L.K.; Bu, H.; Franzen, D.W. Comparison of two ground-based active-optical sensors for in-season estimation of corn (Zea mays L.) yield. J. Plant Nutr. 2016, 39, 957–966. [Google Scholar] [CrossRef]
- Barzin, R.; Pathak, R.; Lotfi, H.; Varco, J.; Bora, G.C. Use of UAS Multispectral Imagery at Different Physiological Stages for Yield Prediction and Input Resource Optimization in Corn. Remote Sens. 2020, 12, 2392. [Google Scholar] [CrossRef]
- Sumner, Z.; Varco, J.J.; Dhillon, J.S.; Fox, A.A.A.; Czarnecki, J.; Henry, W.B. Ground versus aerial canopy reflectance of corn: Red-edge and non-red edge vegetation indices. Agron. J. 2021, 113, 2782–2797. [Google Scholar] [CrossRef]
- Parker, J.N. Sustainable Sidedress Nitrogen Applications for Early Corn and Cotton Crops Using Small Unmanned Aerial Systems. Master’s Thesis, Mississippi State University, Starkville, MS, USA, 2022. Available online: https://scholarsjunction.msstate.edu/td/5604 (accessed on 1 September 2020).
- Whelan, B. Proximal Crop Reflectance Sensors. 2015. Available online: https://grdc.com.au/__data/assets/pdf_file/0011/17300/grdc_proximinal-crop-reflectance.pdf.pdf (accessed on 1 September 2020).
- Thomson, E.R.; Spiegel, M.P.; Althuizen, I.H.J.; Bass, P.; Chen, S.; Chmurzynski, A.; Halbritter, A.H.; Henn, J.J.; Jónsdóttir, I.S.; Klanderud, K.; et al. Multiscale mapping of plant functional groups and plant traits in the High Arctic using field spectroscopy, UAV imagery and Sentinel-2A data. Environ. Res. Lett. 2021, 16, 055006. [Google Scholar] [CrossRef]
- Barzin, R.; Lotfi, H.; Varco, J.J.; Bora, G.C. Machine Learning in Evaluating Multispectral Active Canopy Sensor for Prediction of Corn Leaf Nitrogen Concentration and Yield. Remote Sens. 2022, 14, 120. [Google Scholar] [CrossRef]
- Mission Planner. ArduPilot Development Team. 2021. Available online: http://ardupilot.org/planner/ (accessed on 1 September 2020).
- Pix4Dmapper. Pix4D SA. 2021. Available online: www.pix4d.com (accessed on 1 September 2020).
- QGIS.org. QGIS Geographic Information System. QGIS Association. 2021. Available online: http://www.qgis.org (accessed on 1 September 2020).
- ESRI. ArcGIS Desktop. Redlands, CA: Environmental Systems Research Institute. 2021. Available online: https://www.arcgis.com/index.html (accessed on 1 September 2020).
- R Core Team. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2021; Available online: https://www.R-project.org/ (accessed on 29 June 2021).
- Fox, A.A.A. An Integrated Approach for Predicting Nitrogen Status in Early Cotton and Corn. Available from Dissertations & Theses @ Mississippi State University; Pro- Quest Dissertations & Theses Global. 2015. Available online: https://www.proquest.com/pqdtglobal/docview/1679463711/AC3CA9ABEADF465EPQ/1?accountid=34815 (accessed on 1 September 2020).
- Rouse, J.W.; Haas, R.H.; Schell, J.A.; Deering, D.W. Monitoring Vegetation Systems in the Great Plains with ERTS; Third ERTS Sym.; NASA: Washington, DC, USA, 1973; pp. 309–317, NASA SP-351 I. [Google Scholar]
- Barnes, E.M.; Clarke, T.R.; Richards, S.E.; Colaizzi, P.D.; Haberland, J.; Kostrzewski, M.; Waller, P.; Choi, C.; Riley, E.; Thompson, T.; et al. Coincident detection of crop water stress, nitrogen status and canopy density using ground based multispectral data. In Proceedings of the 5th International Conference on Precision Agriculture, Bloomington, MI, USA, 16–19 July 2000. [Google Scholar]
- Varco, J.J.; Fox, A.A.; Raper, T.B.; Hubbard, K.J. Development of sensor based detection of crop nitrogen status for utilization in variable rate nitrogen fertilization. In Precision Agriculture; Wageningen Academica Publishers: Wageningen, The Netherlands, 2013; pp. 145–150. [Google Scholar]
- Raper, T.B.; Varco, J.J. Canopy-scale wavelength and vegetative index sensitivities to cotton growth parameters and nitrogen status. Precis. Agric. 2014, 16, 62–76. [Google Scholar] [CrossRef][Green Version]
- Frels, K.; Guttieri, M.; Joyce, B.; Leavitt, B.; Baenziger, P.S. Evaluating canopy spectral reflectance vegetation indices to estimate nitrogen use traits in hard winter wheat. Field Crop. Res. 2018, 217, 82–92. [Google Scholar] [CrossRef]
- Colaço, A.; Richetti, J.; Bramley, R.; Lawes, R. How will the next-generation of sensor-based decision systems look in the context of intelligent agriculture? A case-study. Field Crop. Res. 2021, 270, 108205. [Google Scholar] [CrossRef]
Location | Year | P (kg ha−1) | K (kg ha−1) | Mg (kg ha−1) | pH |
---|---|---|---|---|---|
Brooksville | 2020 | 59 | 237 | 74 | 6.7 |
Brooksville | 2021 | 18 | 268 | 189 | 6.6 |
Starkville | 2020 | 129 | 294 | 105 | 8.3 |
Starkville | 2021 | 151 | 268 | 166 | 8.2 |
Stoneville | 2020 | 26 | 233 | 453 | 6.4 |
Stoneville | 2021 | 82 | 286 | 851 | 6.3 |
Verona | 2020 | 79 | 301 | 115 | 6.5 |
Verona | 2021 | 66 | 228 | 168 | 8.1 |
2020 | 2021 | |||||
---|---|---|---|---|---|---|
Treatment | Application 1 kg N ha−1 | Application 2 kg N ha−1 | Total N Rate kg N ha−1 | Application 1 kg N ha−1 | Application 2 kg N ha−1 | Total N Rate kg N ha−1 |
1 | 0 | 0 | 0 | 0 | 0 | 0 |
2 | 45 | 0 | 45 | 90 | 0 | 90 |
3 | 45 | 35 | 80 | 45 | 45 | 90 |
4 | 90 | 0 | 90 | 135 | 0 | 135 |
5 | 45 | 70 | 115 | 45 | 90 | 135 |
6 | 135 | 0 | 135 | 180 | 0 | 180 |
7 | 45 | 100 | 145 | 45 | 135 | 180 |
8 | 180 | 0 | 180 | 225 | 0 | 225 |
9 | 45 | 135 | 180 | 45 | 180 | 225 |
10 | 45 | 170 | 215 | 270 | 0 | 270 |
11 | 225 | 0 | 225 | 45 | 225 | 270 |
12 | 45 | 200 | 245 | - | - | - |
Sensor Name | Blue λ | Green λ | Red λ | Red Edge λ | NIR λ |
---|---|---|---|---|---|
Crop Circle | 670 | 730 | 780 | ||
GreenSeeker | 656 | 774 | |||
MicaSense | 475 | 560 | 668 | 717 | 840 |
SPAD | 650 | 940 |
Acronym | Name | Algorithm | Reference |
---|---|---|---|
NDVI | Normalized Difference Vegetation Index | (R840-R650)/(R840 + R650) | Rouse et al. [31] |
NDRE | Normalized Difference Red Edge | (R780-R720)/(R780 + R720) | Barnes et al. [32] Varco et al. [33] |
SCCCI | Simplified Canopy Chlorophyll Content Index | NDRE/NDVI | Barnes et al. [32] Varco et al. [33] Raper and Varco [34] Fox [30] |
Sensor | Method | n | Y | R2 | p | AIC | RMSE |
---|---|---|---|---|---|---|---|
SPAD | SPAD | 448 | −1.39 + 0.236 X | 0.49 | <0.001 | 1873 | 1.945 |
SPAD-INSEY | 448 | 6.1 + 97.2 X | 0.17 | <0.001 | 2096 | 2.494 | |
rSPAD | 448 | −3.95 + 13.8 X | 0.48 | <0.001 | 1888 | 1.976 | |
GreenSeeker | GS-NDVI | 376 | 11.7 − 5.67 X | 0.11 | <0.001 | 1942 | 3.176 |
GS-INSEY | 376 | 3.75 + 6.61 × 103 X | 0.11 | <0.001 | 1941 | 3.171 | |
GS-rNDVI | 376 | 0.259 + 8.03 X | 0.08 | <0.001 | 1954 | 3.228 | |
Crop Circle | CC-NDVI | 1118 | 9.1 − 0.786 X | < 0.01 | 0.095 | 5681 | 3.062 |
CC-INSEY (NDVI) | 1118 | 4.91 + 5.38 × 103 X | 0.07 | <0.001 | 5602 | 2.955 | |
CC-rNDVI | 1118 | 1.18 + 7.74 X | 0.07 | <0.001 | 5607 | 2.962 | |
CC-NDRE | 1118 | 7.84 + 2.95 X | < 0.01 | 0.004 | 5675 | 3.054 | |
CC-INSEY (NDRE) | 1118 | 3.64 + 1.72 × 104 X | 0.16 | <0.001 | 5491 | 2.812 | |
CC-rNDRE | 1118 | −3.71 + 13.2 X | 0.29 | <0.001 | 5299 | 2.582 | |
CC-SCCCI | 1118 | −6.4 + 35.7 X | 0.21 | <0.001 | 5418 | 2.723 | |
CC-INSEY (SCCCI) | 1118 | 6.62 + 3.79 × 103 X | 0.09 | <0.001 | 5583 | 2.931 | |
CC-rSCCCI | 1118 | −17.1 + 26.5 X | 0.40 | <0.001 | 5109 | 2.371 | |
MicaSense | MC-NDVI | 322 | 3.93 + 7.1 X | 0.11 | <0.001 | 1404 | 2.120 |
MC-INSEY (NDVI) | 322 | 7.37 + 3.27 × 103 X | 0.06 | <0.001 | 1421 | 2.178 | |
MC-rNDVI | 322 | −6.6 + 16.8 X | 0.25 | <0.001 | 1347 | 1.941 | |
MC-NDRE | 322 | 4.05 + 10.9 X | 0.24 | <0.001 | 1354 | 1.962 | |
MC-INSEY (NDRE) | 322 | 7.09 + 5.62 × 103 X | 0.11 | <0.001 | 1402 | 2.114 | |
MC-rNDRE | 322 | −3.22 + 13.6 X | 0.44 | <0.001 | 1251 | 1.674 | |
MC-SCCCI | 322 | 2.66 + 10.7 X | 0.17 | <0.001 | 1381 | 2.048 | |
MC-INSEY (SCCCI) | 322 | 8.22 + 2.33 × 103 X | 0.04 | <0.001 | 1428 | 2.202 | |
MC-rSCCCI | 322 | −14.6 + 24.9 X | 0.49 | <0.001 | 1227 | 1.611 |
Sensor | n | Y | R2 | p | AIC | RMSE |
---|---|---|---|---|---|---|
rSPAD | 144 | −6.31 + 15.6 X | 0.53 | <0.001 | 647.3 | 2.243 |
GS-rNDVI | 144 | −31.4 + 40.2 X | 0.24 | <0.001 | 714.3 | 2.831 |
CC-rNDVI | 144 | −35.9 + 45 X | 0.31 | <0.001 | 701.6 | 2.709 |
Sensor | Stage | n | y | R2 | p | AIC | RMSE |
---|---|---|---|---|---|---|---|
GreenSeeker | V4 | 88 | 8.77 + 1.89 X | 0.05 | 0.029 | 300.6 | 1.291 |
V6 | 48 | 8.89 + 1.6 X | 0.07 | 0.074 | 152.5 | 1.113 | |
V10 | 96 | −15.1 + 21 X | 0.52 | <0.001 | 359.7 | 1.527 | |
VT | 144 | −31.4 + 40.2 X | 0.24 | <0.001 | 714.3 | 2.831 | |
Crop Circle | V4 | 275 | −23.9 + 33.2 X | 0.15 | <0.001 | 1331 | 2.691 |
V6 | 192 | −32.2 + 42 X | 0.46 | <0.001 | 849.6 | 2.177 | |
V8 | 181 | −3.64 + 13.5 X | 0.27 | <0.001 | 786.6 | 2.091 | |
V10 | 192 | −25.3 + 34.6 X | 0.56 | <0.001 | 856.9 | 2.219 | |
VT | 278 | −16.1 + 25.9 X | 0.57 | <0.001 | 1156 | 1.916 | |
MicaSense | V6 | 48 | −45.7 + 54.9 X | 0.43 | <0.001 | 150.7 | 1.092 |
V8 | 48 | −60.4 + 72.7 X | 0.67 | <0.001 | 187.3 | 1.599 | |
V10 | 96 | −23.2 + 33.8 X | 0.5 | <0.001 | 366.9 | 1.585 | |
VT | 43 | −19.7 + 30.1 X | 0.78 | <0.001 | 134.3 | 1.075 | |
R1 | 43 | −14 + 24.1 X | 0.83 | <0.001 | 124.7 | 0.962 | |
R5 | 44 | −7.2 + 17.5 X | 0.54 | <0.001 | 156 | 1.331 |
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. |
© 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Oglesby, C.; Fox, A.A.A.; Singh, G.; Dhillon, J. Predicting In-Season Corn Grain Yield Using Optical Sensors. Agronomy 2022, 12, 2402. https://doi.org/10.3390/agronomy12102402
Oglesby C, Fox AAA, Singh G, Dhillon J. Predicting In-Season Corn Grain Yield Using Optical Sensors. Agronomy. 2022; 12(10):2402. https://doi.org/10.3390/agronomy12102402
Chicago/Turabian StyleOglesby, Camden, Amelia A. A. Fox, Gurbir Singh, and Jagmandeep Dhillon. 2022. "Predicting In-Season Corn Grain Yield Using Optical Sensors" Agronomy 12, no. 10: 2402. https://doi.org/10.3390/agronomy12102402