References
- Al-Gaadi, K. A., A. A. Hassaballa, E. Tola, A. G. Kayad, R. Madugundu, B. Alblewi, and F. Assiri. 2016. “Prediction of Potato Crop Yield Using Precision Agriculture Techniques.” PLOS ONE 11: e0162219. doi:https://doi.org/10.1371/journal.pone.0162219.
- Ali, A., R. Martelli, F. Lupia, and L. Barbanti. 2019. “Assessing Multiple Years’ Spatial Variability of Crop Yields Using Satellite Vegetation Indices.” Remote Sensing 11 (20): 2384. MDPI AG. doi:https://doi.org/10.3390/rs11202384.
- Altman, D. G., and J. M. Bland. 1983. “Measurement in Medicine: The Analysis of Method Comparison Studies.” Journal of the Royal Statistical Society. Series D (The Statistician) 32. John Wiley & Sons, Ltd: 307–317. doi:https://doi.org/10.2307/2987937.
- Aparicio, N., D. Villegas, J. Casadesus, J. L. Araus, and C. Royo. 2000. “Spectral Vegetation Indices as Nondestructive Tools for Determining Durum Wheat Yield.” Agronomy Journal 92 (1): 83–91. John Wiley & Sons, Ltd. doi:https://doi.org/10.2134/agronj2000.92183x.
- Aparicio, N., D. Villegas, J. L. Araus, J. Casadesús, and C. Royo. 2002. “Relationship between Growth Traits and Spectral Vegetation Indices in Durum Wheat.” Crop Science 42 (5): 1547. Crop Science Society of America. doi:https://doi.org/10.2135/cropsci2002.1547.
- Araus, J. L., and J. E. Cairns. 2014. “Field High-Throughput Phenotyping: The New Crop Breeding Frontier.” Trends in Plant Science 19 (1): 52–61. Elsevier Current Trends. doi:https://doi.org/10.1016/J.TPLANTS.2013.09.008.
- Barati, S., B. Rayegani, M. Saati, A. Sharifi, and M. Nasri. 2011. “Comparison the Accuracies of Different Spectral Indices for Estimation of Vegetation Cover Fraction in Sparse Vegetated Areas.” Egyptian Journal of Remote Sensing and Space Science 14 (1): 49–56. Elsevier B.V. doi:https://doi.org/10.1016/j.ejrs.2011.06.001.
- Berra, E. F., R. Gaulton, and S. Barr. 2017. “Commercial Off-the-Shelf Digital Cameras on Unmanned Aerial Vehicles for Multitemporal Monitoring of Vegetation Reflectance and NDVI.” IEEE Transactions on Geoscience and Remote Sensing 55 (9): 4878–4886. doi:https://doi.org/10.1109/TGRS.2017.2655365.
- Bland, J. M., and D. G. Altman. 2010. “Statistical Methods for Assessing Agreement between Two Methods of Clinical Measurement.” International Journal of Nursing Studies 47 (8): 931–936. Pergamon. doi:https://doi.org/10.1016/j.ijnurstu.2009.10.001.
- Broge, N. H., and E. Leblanc. 2001. “Comparing Prediction Power and Stability of Broadband and Hyperspectral Vegetation Indices for Estimation of Green Leaf Area Index and Canopy Chlorophyll Density.” Remote Sensing of Environment 76 (2): 156–172. doi:https://doi.org/10.1016/S0034-4257(00)00197-8.
- Clevers, J. G. P. W. 1989. “Application of a Weighted Infrared-Red Vegetation Index for Estimating Leaf Area Index by Correcting for Soil Moisture.” Remote Sensing of Environment 29 (1): 25–37. Elsevier. doi:https://doi.org/10.1016/0034-4257(89)90076-X.
- Cogato, A., V. Pagay, F. Marinello, F. Meggio, P. Grace, and M. De Antoni Migliorati. 2019. “Assessing the Feasibility of Using Sentinel-2 Imagery to Quantify the Impact of Heatwaves on Irrigated Vineyards.” Remote Sensing 11 (23): 2869. MDPI AG. doi:https://doi.org/10.3390/rs11232869.
- Daughtry, C. S. T., K. P. Gallo, and M. E. Bauer. 1983. “Spectral Estimates of Solar Radiation Intercepted by Corn Canopies.” Purdue University, LARS Technical Report 030182 (3): 527–531. American Society of Agronomy. doi:https://doi.org/10.2134/agronj1983.00021962007500030026x.
- Deng, L., Z. Mao, X. Li, Z. Hu, F. Duan, and Y. Yan. 2018. “UAV-Based Multispectral Remote Sensing for Precision Agriculture: A Comparison between Different Cameras.” ISPRS Journal of Photogrammetry and Remote Sensing 146: 124–136. December. Elsevier. doi:https://doi.org/10.1016/J.ISPRSJPRS.2018.09.008.
- Di Gennaro, S. F., F. Rizza, F. W. Badeck, A. Berton, S. Delbono, B. Gioli, P. Toscano, A. Zaldei, and A. Matese. 2018. “UAV-Based High-Throughput Phenotyping to Discriminate Barley Vigour with Visible and near-Infrared Vegetation Indices.” International Journal of Remote Sensing 39 (15–16): 5330–5344. Taylor and Francis Ltd. doi:https://doi.org/10.1080/01431161.2017.1395974.
- Din, M., W. Zheng, M. Rashid, S. Wang, and Z. Shi. 2017. “Evaluating Hyperspectral Vegetation Indices for Leaf Area Index Estimation of Oryza Sativa L. at Diverse Phenological Stages.” Frontiers in Plant Science 8: 820. May. Frontiers Media S.A. doi:https://doi.org/10.3389/fpls.2017.00820.
- Doraiswamy, P. C., J. L. Hatfield, T. J. Jackson, B. Akhmedov, J. Prueger, and A. Stern. 2004. “Crop Condition and Yield Simulations Using Landsat and MODIS.” Remote Sensing of Environment 92: 548–559. doi:https://doi.org/10.1016/j.rse.2004.05.017.
- Du, M., N. Noguchi, M. Du, and N. Noguchi. 2017. “Monitoring of Wheat Growth Status and Mapping of Wheat Yield’s within-Field Spatial Variations Using Color Images Acquired from UAV-Camera System.” Remote Sensing 9 (3): 289. Multidisciplinary Digital Publishing Institute. doi:https://doi.org/10.3390/rs9030289.
- Duarte-Carvajalino, J., D. Alzate, A. Ramirez, J. Santa-Sepulveda, A. Fajardo-Rojas, M. Soto-Suárez, J. M. Duarte-Carvajalino, et al. 2018. “Evaluating Late Blight Severity in Potato Crops Using Unmanned Aerial Vehicles and Machine Learning Algorithms.” Remote Sensing 10 (10): 1513. Multidisciplinary Digital Publishing Institute. doi:https://doi.org/10.3390/rs10101513.
- Fernández, E., G. Gorchs, and L. Serrano. 2019. “Use of Consumer-Grade Cameras to Assess Wheat N Status and Grain Yield.” PLOS ONE 14 (2): e0211889. Edited by David A. Lightfoot. Public Library of Science. doi:https://doi.org/10.1371/journal.pone.0211889.
- Fróna, D., J. Szenderák, and M. Harangi-Rákos. 2019. “The Challenge of Feeding the World.” Sustainability 11 (20): 5816. MDPI AG. doi:https://doi.org/10.3390/su11205816.
- Fu, Z., J. Jiang, Y. Gao, B. Krienke, M. Wang, K. Zhong, Q. Cao, et al. 2020. “Wheat Growth Monitoring and Yield Estimation Based on Multi-Rotor Unmanned Aerial Vehicle.” Remote Sensing 12 (3): 508. MDPI AG. doi:https://doi.org/10.3390/rs12030508.
- Gautam, D., A. Lucieer, J. Bendig, and Z. Malenovsky. 2020. “Footprint Determination of a Spectroradiometer Mounted on an Unmanned Aircraft System.” IEEE Transactions on Geoscience and Remote Sensing 58 (5): 3085–3096. Institute of Electrical and Electronics Engineers Inc. doi:https://doi.org/10.1109/TGRS.2019.2947703.
- Gianquinto, G., F. Orsini, G. Pennisi, and S. Bona. 2019. “Sources of Variation in Assessing Canopy Reflectance of Processing Tomato by Means of Multispectral Radiometry.” Sensors 19 (21): 4730. MDPI AG. doi:https://doi.org/10.3390/s19214730.
- Gitelson, A. A., and M. N. Merzlyak. 1997. “Remote Estimation of Chlorophyll Content in Higher Plant Leaves.” International Journal of Remote Sensing 18 (12): 2691–2697. Taylor & Francis Group. doi:https://doi.org/10.1080/014311697217558.
- Gitelson, A. A., and M. N. Merzlyak. 1998. “Remote Sensing of Chlorophyll Concentration in Higher Plant Leaves.” Advances in Space Research 22 (5): 689–692. Elsevier Ltd. doi:https://doi.org/10.1016/S0273-1177(97)01133-2.
- Haboudane, D., J. R. Miller, E. Pattey, P. J. Zarco-Tejada, and I. B. Strachan. 2004. “Hyperspectral Vegetation Indices and Novel Algorithms for Predicting Green LAI of Crop Canopies: Modeling and Validation in the Context of Precision Agriculture.” Remote Sensing of Environment 90 (3): 337–352. doi:https://doi.org/10.1016/j.rse.2003.12.013.
- Haboudane, D., J. R. Miller, N. Tremblay, P. J. Zarco-Tejada, and L. Dextraze. 2002. “Integrated Narrow-Band Vegetation Indices for Prediction of Crop Chlorophyll Content for Application to Precision Agriculture.” Remote Sensing of Environment 81 (2–3): 416–426. doi:https://doi.org/10.1016/S0034-4257(02)00018-4.
- Haghighattalab, A., L. G. Pérez, S. Mondal, D. Singh, D. Schinstock, J. Rutkoski, I. Ortiz-Monasterio, R. P. Singh, D. Goodin, and J. Poland. 2016. “Application of Unmanned Aerial Systems for High Throughput Phenotyping of Large Wheat Breeding Nurseries.” Plant Methods 12: 35. BioMed Central. doi:https://doi.org/10.1186/s13007-016-0134-6.
- Hansen, P. M., J. R. Jørgensen, and A. Thomsen. 2002. “Predicting Grain Yield and Protein Content in Winter Wheat and Spring Barley Using Repeated Canopy Reflectance Measurements and Partial Least Squares Regression.” Journal of Agricultural Science 139 (3): 307–318. Cambridge University Press. doi:https://doi.org/10.1017/S0021859602002320.
- Hashimoto, N., Y. Saito, M. Maki, and K. Homma. 2019. “Simulation of Reflectance and Vegetation Indices for Unmanned Aerial Vehicle (UAV) Monitoring of Paddy Fields.” Remote Sensing 11 (18): 2119. MDPI AG. doi:https://doi.org/10.3390/rs11182119.
- Hassan, M. A., M. Yang, A. Rasheed, G. Yang, M. Reynolds, X. Xia, Y. Xiao, and Z. He. 2019. “A Rapid Monitoring of NDVI across the Wheat Growth Cycle for Grain Yield Prediction Using a Multi-Spectral UAV Platform.” Plant Science 282: 95–103. May. Elsevier Ireland Ltd. doi:https://doi.org/10.1016/j.plantsci.2018.10.022.
- Hatfield, J. L., and J. H. Prueger. 2010. “Value of Using Different Vegetative Indices to Quantify Agricultural Crop Characteristics at Different Growth Stages under Varying Management Practices.” Remote Sensing 2 (2): 562–578. Molecular Diversity Preservation International. doi:https://doi.org/10.3390/rs2020562.
- Huang, J., X. Wang, X. Li, H. Tian, and Z. Pan. 2013. “Remotely Sensed Rice Yield Prediction Using Multi-Temporal NDVI Data Derived from NOAA’S-AVHRR.” PLoS ONE 8 (8): e70816. Edited by Wengui Yan. Public Library of Science. doi:https://doi.org/10.1371/journal.pone.0070816.
- Huete, A., K. Didan, T. Miura, E. P. Rodriguez, X. Gao, and L. G. Ferreira. 2002. “Overview of the Radiometric and Biophysical Performance of the MODIS Vegetation Indices.” Remote Sensing of Environment 83 (1–2): 195–213. Elsevier. doi:https://doi.org/10.1016/S0034-4257(02)00096-2.
- Hussain, S., K. Gao, M. Din, Y. Gao, Z. Shi, and S. Wang. 2020. “Assessment of UAV-Onboard Multispectral Sensor for Non-Destructive Site-Specific Rapeseed Crop Phenotype Variable at Different Phenological Stages and Resolutions.” Remote Sensing 12 (3): 397. MDPI AG. doi:https://doi.org/10.3390/rs12030397.
- Huuskonen, J., and T. Oksanen. 2018. “Soil Sampling with Drones and Augmented Reality in Precision Agriculture.” Computers and Electronics in Agriculture 154: 25–35. November. Elsevier. doi:https://doi.org/10.1016/J.COMPAG.2018.08.039.
- Jaafar, H. H., and F. A. Ahmad. 2015. “Crop Yield Prediction from Remotely Sensed Vegetation Indices and Primary Productivity in Arid and Semi-Arid Lands.” International Journal of Remote Sensing 36 (18): 4570–4589. Taylor and Francis Ltd. doi:https://doi.org/10.1080/01431161.2015.1084434.
- Jiang, Z., A. R. Huete, K. Didan, and T. Miura. 2008. “Development of a Two-Band Enhanced Vegetation Index without a Blue Band.” Remote Sensing of Environment 112 (10): 3833–3845. Elsevier. doi:https://doi.org/10.1016/J.RSE.2008.06.006.
- Jordan, C. F. 1969. “Derivation of Leaf-Area Index from Quality of Light on the Forest Floor.” Ecology 50 (4): 663–666. Wiley-Blackwell. doi:https://doi.org/10.2307/1936256.
- Jurecka, F., P. Hlavinka, V. Lukas, M. Trnka, and Z. Zalud. 2016. “Crop Yield Estimation in the Field Level Using Vegetation Indices.” https://mendelnet.cz/pdfs/mnt/2016/01/14.pdf
- Kang, Y., M. Özdoğan, S. C. Zipper, M. O. Román, J. Walker, S. Y. Hong, M. Marshall, et al. 2016. “How Universal Is the Relationship between Remotely Sensed Vegetation Indices and Crop Leaf Area Index? A Global Assessment.” Remote Sensing 8 (7): 597. MDPI AG. doi:https://doi.org/10.3390/rs8070597.
- Kayad, A. G., K. A. Al-Gaadi, T. Elkamil, R. Madugundu, A. M. Zeyada, and C. Kalaitzidis. 2016. “Assessing the Spatial Variability of Alfalfa Yield Using Satellite Imagery and Ground-Based Data.” PLoS ONE 11 (6): e0157166. Public Library of Science. doi:https://doi.org/10.1371/journal.pone.0157166.
- Kyratzis, A. C., D. P. Skarlatos, G. C. Menexes, V. F. Vamvakousis, and A. Katsiotis. 2017. “Assessment of Vegetation Indices Derived by UAV Imagery for Durum Wheat Phenotyping under a Water Limited and Heat Stressed Mediterranean Environment.” Frontiers in Plant Science 8: 1114. June. Frontiers. doi:https://doi.org/10.3389/fpls.2017.01114.
- Liu, J., E. Pattey, and J. Guillaume. 2012. “Assessment of Vegetation Indices for Regional Crop Green LAI Estimation from Landsat Images over Multiple Growing Seasons.” Remote Sensing of Environment 123: 347–358. August. Elsevier. doi:https://doi.org/10.1016/J.RSE.2012.04.002.
- Maimaitiyiming, M., V. Sagan, P. Sidike, and M. T. Kwasniewski. 2019. “Dual Activation Function-Based Extreme Learning Machine (ELM) for Estimating Grapevine Berry Yield and Quality.” Remote Sensing 11 (7): 740. MDPI AG. doi:https://doi.org/10.3390/rs11070740.
- Massawe, F. J., S. Mayes, A. Cheng, H. H. Chai, P. Cleasby, R. Symonds, W. K. Ho, et al. 2015. “The Potential for Underutilised Crops to Improve Food Security in the Face of Climate Change.” Procedia Environmental Sciences 29: 140–141. January. Elsevier. doi:https://doi.org/10.1016/J.PROENV.2015.07.228.
- Massawe, F. J., S. S. Mwale, S. N. Azam-Ali, and J. A. Roberts. 2005. “Breeding in Bambara Groundnut (Vigna subterranea (L.) Verdc): Strategic Considerations.” African Journal of Biotechnology 4 (6): 463–471. http://www.academicjournals.org/AJB
- Matese, A., P. Toscano, S. Di Gennaro, L. Genesio, F. Vaccari, J. Primicerio, C. Belli, A. Zaldei, R. Bianconi, and B. Gioli. 2015. “Intercomparison of UAV, Aircraft and Satellite Remote Sensing Platforms for Precision Viticulture.” Remote Sensing 7 (3): 2971–2990. MDPI AG. doi:https://doi.org/10.3390/rs70302971.
- Mkhabela, M. S., M. S. Mkhabela, and N. N. Mashinini. 2005. “Early Maize Yield Forecasting in the Four Agro-Ecological Regions of Swaziland Using NDVI Data Derived from NOAA’s-AVHRR.” Agricultural and Forest Meteorology 129 (1–2): 1–9. Elsevier. doi:https://doi.org/10.1016/j.agrformet.2004.12.006.
- Moran, M. S., Y. Inoue, and E. M. Barnes. 1997. “Opportunities and Limitations for Image-Based Remote Sensing in Precision Crop Management.” Remote Sensing of Environment 61 (3): 319–346. Elsevier. doi:https://doi.org/10.1016/S0034-4257(97)00045-X.
- Morellos, A., X. E. Pantazi, D. Moshou, T. Alexandridis, R. Whetton, G. Tziotzios, J. Wiebensohn, R. Bill, and A. M. Mouazen. 2016. “Machine Learning Based Prediction of Soil Total Nitrogen, Organic Carbon and Moisture Content by Using VIS-NIR Spectroscopy.” Biosystems Engineering 152: 104–116. December. Academic Press. doi:https://doi.org/10.1016/j.biosystemseng.2016.04.018.
- Musa, M., F. Massawe, S. Mayes, I. Alshareef, and A. Singh. 2016. “Nitrogen Fixation and N-Balance Studies on Bambara Groundnut (Vigna Subterranea L. Verdc) Landraces Grown on Tropical Acidic Soils of Malaysia.” Communications in Soil Science and Plant Analysis 47 (4): 1–10. Taylor and Francis Inc. doi:https://doi.org/10.1080/00103624.2016.1141928.
- Mutanga, O., and A. K. Skidmore. 2004. “Narrow Band Vegetation Indices Overcome the Saturation Problem in Biomass Estimation.” International Journal of Remote Sensing 25 (19): 3999–4014. Taylor and Francis Ltd. doi:https://doi.org/10.1080/01431160310001654923.
- Nijland, W., R. de Jong, S. M. de Jong, M. A. Wulder, C. W. Bater, and N. C. Coops. 2014. “Monitoring Plant Condition and Phenology Using Infrared Sensitive Consumer Grade Digital Cameras.” Agricultural and Forest Meteorology 184: 98–106. January. Elsevier. doi:https://doi.org/10.1016/J.AGRFORMET.2013.09.007.
- Nyarko, E. K., I. Vidović, K. Radočaj, and R. Cupec. 2018. “A Nearest Neighbor Approach for Fruit Recognition in RGB-D Images Based on Detection of Convex Surfaces.” Expert Systems with Applications 114: 454–466. December. Pergamon. doi:https://doi.org/10.1016/J.ESWA.2018.07.048.
- Onyia, N., H. Balzter, and J.-C. Berrio. 2018. “Normalized Difference Vegetation Vigour Index: A New Remote Sensing Approach to Biodiversity Monitoring in Oil Polluted Regions.” Remote Sensing 10 (6): 897. MDPI AG. doi:https://doi.org/10.3390/rs10060897.
- Paul, S. R., and X. Zhang. 2010. “Testing for Normality in Linear Regression Models.” Journal of Statistical Computation and Simulation 80 (10): 1101–1113. Taylor & Francis. doi:https://doi.org/10.1080/00949650902964275.
- Plummer, S. E. 1988. “Exploring the Relationships between Leaf Nitrogen Content, Biomass and the near-Infrared/Red Reflectance Ratio.” International Journal of Remote Sensing 9 (1): 177–183. Taylor & Francis Group. doi:https://doi.org/10.1080/01431168808954845.
- Potgieter, A. B., B. George-Jaeggli, S. C. Chapman, L. Kenneth, L. A. Suárez Cadavid, J. Wixted, J. Watson, M. Eldridge, D. R. Jordan, and G. L. Hammer. 2017. “Multi-Spectral Imaging from an Unmanned Aerial Vehicle Enables the Assessment of Seasonal Leaf Area Dynamics of Sorghum Breeding Lines.” Frontiers in Plant Science 8: 1532. September. Frontiers. doi:https://doi.org/10.3389/fpls.2017.01532.
- Primicerio, J., S. F. Di Gennaro, F. Edoardo, L. Genesio, E. Lugato, A. Matese, and F. P. Vaccari. 2012. “A Flexible Unmanned Aerial Vehicle for Precision Agriculture.” Precision Agriculture 13 (4): 517–523. Springer. doi:https://doi.org/10.1007/s11119-012-9257-6.
- Ren, J., Z. Chen, Q. Zhou, and H. Tang. 2008. “Regional Yield Estimation for Winter Wheat with MODIS-NDVI Data in Shandong, China.” International Journal of Applied Earth Observation and Geoinformation 10 (4): 403–413. Elsevier. doi:https://doi.org/10.1016/J.JAG.2007.11.003.
- Reynolds, C. A., M. Yitayew, D. C. Slack, C. F. Hutchinson, A. Huete, and M. S. Petersen. 2000. “Estimating Crop Yields and Production by Integrating the FAO Crop Specific Water Balance Model with Real-Time Satellite Data and Ground-Based Ancillary Data.” International Journal of Remote Sensing 21 (18): 3487–3508. Taylor & Francis Group. doi:https://doi.org/10.1080/014311600750037516.
- Rouse, J. W., R. H. Haas, J. A. Schell, D. W. Deering, and J. C. Harlan. 1974. Monitoring the Vernal Advancement and Retrogradation (Green Wave Effect) of Natural Vegetation - NASA-CR-144661. Bryan, TX: RS Center, A Texas, GSF Center Texas A&M University, Remote Sensing Center.
- Sankaran, S., L. R. Jianfeng Zhou, J. J. Khot, E. M. Trapp, and P. N. Miklas. 2018. “High-Throughput Field Phenotyping in Dry Bean Using Small Unmanned Aerial Vehicle Based Multispectral Imagery.” Computers and Electronics in Agriculture 151: 84–92. August. Elsevier. doi:https://doi.org/10.1016/J.COMPAG.2018.05.034.
- Serrano, L., I. Filella, and J. Pen˜uelas. 2000. “Remote Sensing of Biomass and Yield of Winter Wheat under Different Nitrogen Supplies.” Crop Science 40 (3): 723. Crop Science Society of America. doi:https://doi.org/10.2135/cropsci2000.403723x.
- Sesay, A., T. Mpuisang, T. S. Morake, I. Al-Shareef, H. J. Chepete, and B. Moseki. 2010. “Preliminary Assessment of Bambara Groundnut (Vigna subterranea (L.)) Landraces for Temperature and Water Stress Tolerance under Field Conditions in Botswana.” South African Journal of Plant and Soil 27 (4): 312–321. Taylor & Francis Group. doi:https://doi.org/10.1080/02571862.2010.10640000.
- Shi, Y. J., S. C. Alex Thomasson, M. N. Ace Pugh, W. L. Rooney, S. Shafian, N. Rajan, G. Rouze, et al. 2016. “Unmanned Aerial Vehicles for High-Throughput Phenotyping and Agronomic Research.” PLOS ONE 11 (7): e0159781. Edited by Jinfa Zhang. Public Library of Science. doi:https://doi.org/10.1371/journal.pone.0159781.
- Smith, G. M., and E. J. Milton. 1999. “The Use of the Empirical Line Method to Calibrate Remotely Sensed Data to Reflectance.” International Journal of Remote Sensing 20 (13): 2653–2662. Taylor & Francis Group. doi:https://doi.org/10.1080/014311699211994.
- Suhairi, T. A. S. T. M., E. Jahanshiri, and N. M. M. Nizar. 2018. “Multicriteria Land Suitability Assessment for Growing Underutilised Crop, Bambara Groundnut in Peninsular Malaysia.” IOP Conference Series: Earth and Environmental Science 169. Institute of Physics Publishing. doi:https://doi.org/10.1088/1755-1315/169/1/012044.
- Sun, L., M. C. Feng Gao, W. P. Anderson, M. M. Kustas, L. S. Alsina, B. Sams, L. McKee, et al. 2017. “Daily Mapping of 30 m LAI and NDVI for Grape Yield Prediction in California Vineyards.” Remote Sensing 9 (4). MDPI AG. doi:https://doi.org/10.3390/rs9040317.
- Tattaris, M., M. P. Reynolds, and S. C. Chapman.2016. “A Direct Comparison of Remote Sensing Approaches for High-Throughput Phenotyping in Plant Breeding.” Frontiers in Plant Science 7: 1131. August. Frontiers Research Foundation. doi:https://doi.org/10.3389/fpls.2016.01131.
- Toth, C., and J. Grzegorz. 2016. “Remote Sensing Platforms and Sensors: A Survey.” ISPRS Journal of Photogrammetry and Remote Sensing 115: 22–36. May. Elsevier. doi:https://doi.org/10.1016/J.ISPRSJPRS.2015.10.004.
- Tucker, C. J. 1979. “Red and Photographic Infrared Linear Combinations for Monitoring Vegetation.” Remote Sensing of Environment 8 (2): 127–150. Elsevier. doi:https://doi.org/10.1016/0034-4257(79)90013-0.
- Wafula, J. S., B. O. Nyongesa, B. A. Were, and S. Gudu. 2021. “Genotypic Variation of Bambara Groundnut (Vigna subterranea (L.) Verdc) for Phosphorus Efficiency.” Acta Agriculturae Scandinavica, Section B — Soil & Plant Science 71 (1): 68–80. Taylor and Francis Ltd. doi:https://doi.org/10.1080/09064710.2020.1850852.
- Walsh, O. S., S. Shafian, J. M. Marshall, C. Jackson, J. R. McClintick-Chess, S. M. Blanscet, K. Swoboda, C. Thompson, K. M. Belmont, and W. L. Walsh. 2018. “Assessment of UAV Based Vegetation Indices for Nitrogen Concentration Estimation in Spring Wheat.” Advances in Remote Sensing 7 (2): 71–90. Scientific Research Publishing, Inc. doi:https://doi.org/10.4236/ars.2018.72006.
- Wang, L., X. Zhou, X. Zhu, Z. Dong, and W. Guo. 2016. “Estimation of Biomass in Wheat Using Random Forest Regression Algorithm and Remote Sensing Data.” The Crop Journal 4 (3): 212–219. Elsevier. doi:https://doi.org/10.1016/J.CJ.2016.01.008.
- Wang, L., Y. Tian, X. Yao, Y. Zhu, and W. Cao. 2014. “Predicting Grain Yield and Protein Content in Wheat by Fusing Multi-Sensor and Multi-Temporal Remote-Sensing Images.” Field Crops Research 164: 178–188. August. Elsevier. doi:https://doi.org/10.1016/J.FCR.2014.05.001.
- Wessel, M., W. G. Keltjens, and E. V. Cleef. 2012. “The Phosphorus and Nitrogen Nutrition of Bambara Groundnut (Vigna subterranea L.Verdc) in Botswana Soils.”
- White, J. W., P. Andrade-Sanchez, M. A. Gore, K. F. Bronson, T. A. Coffelt, M. M. Conley, K. A. Feldmann, et al. 2012. “Field-Based Phenomics for Plant Genetics Research.” Field Crops Research 133: 101–112. July. Elsevier. doi:https://doi.org/10.1016/J.FCR.2012.04.003.
- Xue, L. H., W. X. Cao, and L. Z. Yang. 2007. “Predicting Grain Yield and Protein Content in Winter Wheat at Different N Supply Levels Using Canopy Reflectance Spectra.” Pedosphere 17 (5): 646–653. Soil Science Society of China. doi:https://doi.org/10.1016/S1002-0160(07)60077-0.
- Yu, N., L. Li, N. Schmitz, L. F. Tian, J. A. Greenberg, and B. W. Diers. 2016. “Development of Methods to Improve Soybean Yield Estimation and Predict Plant Maturity with an Unmanned Aerial Vehicle Based Platform.” Remote Sensing of Environment 187: 91–101. December. Elsevier. doi:https://doi.org/10.1016/J.RSE.2016.10.005.
- Zhang, S., G. Zhao, K. Lang, B. Su, X. Chen, X. Xi, and H. Zhang. 2019. “Integrated Satellite, Unmanned Aerial Vehicle (UAV) and Ground Inversion of the Spad Ofwinter Wheat in the Reviving Stage.” Sensors (Switzerland) 19 (7). MDPI AG. doi:https://doi.org/10.3390/s19071485.
- Zhao, D., R. V. K. Raja, G. Kakani, J. J. Read, and K. Sailaja. 2007. “Canopy Reflectance in Cotton for Growth Assessment and Lint Yield Prediction.” European Journal of Agronomy 26 (3): 335–344. Elsevier. doi:https://doi.org/10.1016/J.EJA.2006.12.001.
- Zhao, Y., A. B. Potgieter, M. Zhang, B. Wu, and G. L. Hammer. 2020. “Predicting Wheat Yield at the Field Scale by Combining High-Resolution Sentinel-2 Satellite Imagery and Crop Modelling.” Remote Sensing 12 (6): 1024. MDPI AG. doi:https://doi.org/10.3390/rs12061024.
- Zhou, X., H. B. Zheng, X. Q. Xu, J. Y. He, X. K. Ge, X. Yao, T. Cheng, Y. Zhu, W. X. Cao, and Y. C. Tian. 2017. “Predicting Grain Yield in Rice Using Multi-Temporal Vegetation Indices from UAV-Based Multispectral and Digital Imagery.” ISPRS Journal of Photogrammetry and Remote Sensing 130: 246–255. August. Elsevier. doi:https://doi.org/10.1016/J.ISPRSJPRS.2017.05.003.
- Zhuang, S., P. Wang, B. Jiang, M. Li, and Z. Gong. 2017. “Early Detection of Water Stress in Maize Based on Digital Images.” Computers and Electronics in Agriculture 140: 461–468. August. Elsevier. doi:https://doi.org/10.1016/J.COMPAG.2017.06.022.