Yufeng Ge Publications

Yufeng Ge

  • Cribben, C. D., Thomasson, J. A., Ge, Y., Morgan, C. L. S., Yang, C., Isakeit, T., & Nichols, R. L. (2016). Site-specific relationships between Cotton Root Rot and soil properties. Journal of Cotton Science20(1), 67-75.
  • Ge, Y., & Thomasson, J. A. (2016). NIR reflectance and MIR attenuated total reflectance spectroscopy for characterizing algal biomass composition. Transactions of the ASABE59(2), 435-442.
  • Bagnall, G. C., Thomasson, J. A., & Ge, Y. (2016). Animal-drawn conservation-tillage planter designed for small farms in the developing world. Applied Engineering in Agriculture32(6), 931-799.
  • Schielack III, V. P., Thomasson, J. A., Sui, R., & Ge, Y. (2016). Harvester-based sensing system for cotton fiber quality mapping. Journal of Cotton Science20(4), 386-393.
  • Wijewardane, N. K., Ge, Y., & Morgan, C. L. (2016). Moisture insensitive prediction of soil properties from VNIR reflectance spectra based on external parameter orthogonalization. Geoderma267, 92-101.
  • Ge, Y., Pandey, P., & Bai, G. (2016, May). Estimating fresh biomass of maize plants from their RGB images in greenhouse phenotyping. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping (Vol. 9866, pp. 8-13). SPIE.
  • Meyer, G. E., & Ge, Y. (2016, June). Instrumentation and controls instruction for agricultural and biological engineering students. In 2016 ASEE Annual Conference & Exposition.
  • Wijewardane, N. K., Ge, Y., Wills, S., & Loecke, T. (2016). Prediction of soil carbon in the conterminous United States: Visible and near infrared reflectance spectroscopy analysis of the rapid carbon assessment project. Soil Science Society of America Journal80(4), 973-982.
  • Wijewardane, N. K., Ge, Y., Wills, S., & Loecke, T. (2016). Prediction of soil carbon in the conterminous United States: Visible and near infrared reflectance spectroscopy analysis of the rapid carbon assessment project. Soil Science Society of America Journal80(4), 973-982.
  • Ge, Y., Bai, G., Stoerger, V., & Schnable, J. C. (2016). Temporal dynamics of maize plant growth, water use, and leaf water content using automated high throughput RGB and hyperspectral imaging. Computers and Electronics in Agriculture127, 625-632.
  • Wijewardane, N. K., Ge, Y., & Morgan, C. L. S. (2016). Prediction of soil organic and inorganic carbon at different moisture contents with dry ground VNIR: a comparative study of different approaches. European Journal of Soil Science67(5), 605-615.
  • Bai, G., Ge, Y., Hussain, W., Baenziger, P. S., & Graef, G. (2016). A multi-sensor system for high throughput field phenotyping in soybean and wheat breeding. Computers and Electronics in Agriculture128, 181-192.
  • Loecke, T., Wills, S. A., Teachman, G., Sequeira, C., West, L., Wijewardane, N., & Ge, Y. (2016, December). A modern soil carbon stock baseline for the conterminous United States. In AGU Fall Meeting Abstracts (Vol. 2016, pp. B31L-07).
  • Bai, G., Blecha, S., Ge, Y., Walia, H., & Phansak, P. (2017). Characterizing wheat response to water limitation using multispectral and thermal imaging. Transactions of the ASABE60(5), 1457-1466.
  • Ackerson, J. P., Morgan, C. L. S., & Ge, Y. (2017). Penetrometer-mounted VisNIR spectroscopy: Application of EPO-PLS to in situ VisNIR spectra. Geoderma286, 131-138.
  • Lammers, P. J., Huesemann, M., Boeing, W., Anderson, D. B., Arnold, R. G., Bai, X., ... & Olivares, J. A. (2017). Review of the cultivation program within the National Alliance for Advanced Biofuels and Bioproducts. Algal research22, 166-186.
  • Pandey, P., Ge, Y., Stoerger, V., & Schnable, J. C. (2017). High throughput in vivo analysis of plant leaf chemical properties using hyperspectral imaging. Frontiers in plant science8, 1348.
  • Schnable, J. C., Pandey, P., Ge, Y., Xu, Y., Qiu, Y., & Liang, Z. (2017, December). Lessons From Paired Data From exPVP Maize Lines in Agronomic Field Trials and RGB And Hyperspectral Time-Series Imaging In Controlled Environments. In AGU Fall Meeting Abstracts (Vol. 2017, pp. B41J-04).
  • Shi, Y., Veeranampalayam-Sivakumar, A. N., Li, J., Ge, Y., Schnable, J. C., Rodriguez, O., ... & Miao, C. (2017, December). Breeding for Increased Water Use Efficiency in Corn (Maize) Using a Low-altitude Unmanned Aircraft System. In AGU Fall Meeting Abstracts (Vol. 2017, pp. B51A-1774).
  • Ge, Y., Bai, G., Irmak, S., Awada, T., Stoerger, V., Graef, G., ... & Schnable, J. (2017, December). High throughput field plant phenotyping facility at University of Nebraska-Lincoln and the first year experience. In AGU Fall Meeting Abstracts (Vol. 2017, pp. B51A-1771).
  • Yao, Y., Ge, Y., Thomasson, J. A., & Sui, R. (2018). Algae optical density sensor for pond monitoring and production process control. International Journal of Agricultural & Biological Engineering11(1).
  • Lo, T. H., Rudnick, D. R., Ge, Y., Heeren, D. M., Irmak, S., Barker, J. B., ... & Shaver, T. M. (2018). Ground-based Thermal Sensing of Field Crops and Its Relevance to Irrigation Management. University of Nebraska-Lincoln, Extension.
  • Liang, Z., Pandey, P., Stoerger, V., Xu, Y., Qiu, Y., Ge, Y., & Schnable, J. C. (2018). Conventional and hyperspectral time-series imaging of maize lines widely used in field trials. Gigascience7(2), gix117.
  • Thapa, S., Zhu, F., Walia, H., Yu, H., & Ge, Y. (2018). A novel LiDAR-based instrument for high-throughput, 3D measurement of morphological traits in maize and sorghum. Sensors18(4), 1187.
  • Wijewardane, N. K., Ge, Y., Wills, S., & Libohova, Z. (2018). Predicting physical and chemical properties of US soils with a mid‐infrared reflectance spectral library. Soil Science Society of America Journal82(3), 722-731.
  • Jiao, X., Zhang, H., Zheng, J., Yin, Y., Wang, G., Chen, Y., ... & Ge, Y. (2018). Comparative analysis of nonlinear growth curve models for Arabidopsis thaliana rosette leaves. Acta Physiologiae Plantarum40(6), 1-8.
  • Bai, G., Jenkins, S., Yuan, W., Graef, G. L., & Ge, Y. (2018). Field-based scoring of soybean iron deficiency chlorosis using RGB imaging and statistical learning. Frontiers in plant science9, 1002.
  • Yuan, W., Li, J., Bhatta, M., Shi, Y., Baenziger, P. S., & Ge, Y. (2018). Wheat height estimation using LiDAR in comparison to ultrasonic sensor and UAS. Sensors18(11), 3731.
  • Belamkar, V., Blecha, S., El-basyoni, I., Jarquin, D., Ge, Y., Shi, Y., ... & Baenziger, P. S. (2018, November). Genomic Selection in Practice: Insights from the University of Nebraska Winter Wheat Breeding Program. In ASA, CSSA, and CSA International Annual Meeting (2018). ASA-CSSA-SSSA.
  • Bai, G., Ge, Y., Leavitt, B., Gamon, J. A., Qi, Y., Awada, T., ... & Stoerger, V. (2018, December). Capturing diurnal variation of phenotypic traits for breeding plots using UNL Field Plant Phenotyping Facility. In AGU Fall Meeting Abstracts (Vol. 2018, pp. B23D-06).
  • Zhu, F., Thapa, S., Gao, T., Ge, Y., Walia, H., & Yu, H. (2018, December). 3D reconstruction of plant leaves for high-throughput phenotyping. In 2018 IEEE International Conference on Big Data (Big Data) (pp. 4285-4293). IEEE.
  • Wijewardane, N. K., Wang, L., Zhan, Y., Franz, T., Yu, H., Zhou, Y., ... & Ge, Y. (2019). Mapping infield variability of soil properties to support precision agriculture using UAV, multi-depth EC, and aerial hyperspectral imagery. PSS 2019, 15.
  • Li, J., Bhatta, M., Garst, N. D., Stoll, H., Veeranampalayam-Sivakumar, A. N., Baenziger, P. S., ... & Shi, Y. (2019). Principal Variable Selection to Explain Grain Yield Variation in Winter Wheat from UAV-derived Phenotypic Traits. In 2019 ASABE Annual International Meeting (p. 1). American Society of Agricultural and Biological Engineers.
  • Palli, P., Liew, C. T., Drozda, A., Mwunguzi, H., Pitla, S. K., Walia, H., & Ge, Y. (2019). Robotic gantry for automated imaging, sensing, crop input application, and high-throughput analysis. In 2019 ASABE Annual International Meeting (p. 1). American Society of Agricultural and Biological Engineers.
  • Ge, Y., Wijewardane, N. K., Ackerson, J. P., Morgan, C. L., & Hetrick, S. E. (2019, January). Automated, in-Situ, and High-Resolution Vertical Soil Sensing with a Visnir Penetrometer System. In SSSA International Soils Meeting (2019). ASA-CSSA-SSSA.
  • Williams, C., Gates, J., Wills, S., Ge, Y., & Hanson, P. (2019, January). A Comparison of VNIR and Mir Spectroscopy for Predicting Various Soil Properties. In SSSA International Soils Meeting (2019). ASA-CSSA-SSSA.
  • Hetrick, S. E., Wijewardane, N. K., Morgan, C. L., Ge, Y., & Ackerson, J. P. (2019, January). In Situ Soil Profile Characterization with a Visnir Penetrometer: State-to-State Transferability. In SSSA International Soils Meeting (2019). ASA-CSSA-SSSA.
  • Luo, Z., Brock, J., Dyer, J. M., Kutchan, T., Schachtman, D., Augustin, M., ... & Abdel-Haleem, H. (2019). Genetic diversity and population structure of a Camelina sativa spring panel. Frontiers in Plant Science10, 184.
  • Wen, S., Zhang, Q., Yin, X., Lan, Y., Zhang, J., & Ge, Y. (2019). Design of plant protection UAV variable spray system based on neural networks. Sensors19(5), 1112.
  • Bai, G., Ge, Y., Scoby, D., Leavitt, B., Stoerger, V., Kirchgessner, N., ... & Awada, T. (2019). NU-Spidercam: A large-scale, cable-driven, integrated sensing and robotic system for advanced phenotyping, remote sensing, and agronomic research. Computers and Electronics in Agriculture160, 71-81.
  • Lo, Tsz Him, et al. "Water effects on optical canopy sensing for late-season site-specific nitrogen management of maize." Computers and Electronics in Agriculture 162 (2019): 154-164.
  • Morgan, Cristine, Yufeng Ge, David Brown, and Ross Bricklemyer. "Vis-NIR equipped soil penetrometer." U.S. Patent 10,337,159, issued July 2, 2019.
  • Atefi, A., Ge, Y., Pitla, S., & Schnable, J. (2019). In vivo human-like robotic phenotyping of leaf traits in maize and sorghum in greenhouse. Computers and Electronics in Agriculture163, 104854.
  • Nan, Y., Zhang, H., Zheng, J., Bian, L., Li, Y., Yang, Y., ... & Ge, Y. (2019). Estimating leaf area density of Osmanthus trees using ultrasonic sensing. Biosystems Engineering186, 60-70.
  • Yuan, W., Wijewardane, N. K., Jenkins, S., Bai, G., Ge, Y., & Graef, G. L. (2019). Early prediction of soybean traits through color and texture features of canopy RGB imagery. Scientific reports9(1), 1-17.
  • Wen, S., Han, J., Ning, Z., Lan, Y., Yin, X., Zhang, J., & Ge, Y. (2019). Numerical analysis and validation of spray distributions disturbed by quad-rotor drone wake at different flight speeds. Computers and Electronics in Agriculture166, 105036.
  • Hetrick, S. E., Morgan, C. L., Ackerson, J. P., Neely, H. L., & Ge, Y. (2019, November). Diving Down: In Situ Characterization of Clay, Carbon, and Bulk Density Using a Visnir-Mounted Penetrometer. In ASA, CSSA and SSSA International Annual Meetings (2019). ASA-CSSA-SSSA.
  • Ge, Y., Atefi, A., Zhang, H., Miao, C., Ramamurthy, R. K., Sigmon, B., ... & Schnable, J. C. (2019). High-throughput analysis of leaf physiological and chemical traits with VIS–NIR–SWIR spectroscopy: a case study with a maize diversity panel. Plant methods15(1), 1-12.
  • Wang, L., Hu, Q., Zhou, Y., Tang, Z., Ge, Y., Smith, A., ... & Shi, Y. (2019, December). Multi-species Forest Classification Using Original and Down-sampled UAS Images with Various Machine Learning Models. In AGU Fall Meeting Abstracts (Vol. 2019, pp. B11F-2398).
  • Li, J., Veeranampalayam-Sivakumar, A. N., Bhatta, M., Garst, N. D., Stoll, H., Stephen Baenziger, P., ... & Shi, Y. (2019). Principal variable selection to explain grain yield variation in winter wheat from features extracted from UAV imagery. Plant Methods15(1), 1-13.
  • Wen, S., Shen, N., Zhang, J., Lan, Y., Han, J., Yin, X., ... & Ge, Y. (2019). Single-rotor UAV flow field simulation using generative adversarial networks. Computers and Electronics in Agriculture167, 105004.
  • Sandhu, J., Zhu, F., Paul, P., Gao, T., Dhatt, B. K., Ge, Y., ... & Walia, H. (2019). PI-Plat: a high-resolution image-based 3D reconstruction method to estimate growth dynamics of rice inflorescence traits. Plant Methods15(1), 1-13.
  • Singh, J., Heeren, D. M., Rudnick, D. R., Woldt, W. E., Bai, G., Ge, Y., & Luck, J. D. (2020). Soil structure and texture effects on the precision of soil water content measurements with a capacitance-based electromagnetic sensor. Transactions of the ASABE63(1), 141-152.
  • Singh, J., Heeren, D. M., Ge, Y., & Bai, G. (2020). Capturing Spatial Variability in Maize and Soybean using Stationary Sensor Nodes.
  • 张慧春, 周宏平, 郑加强, 葛玉峰, & 李杨先. (2020). 植物表型平台与图像分析技术研究进展与展望. 农业机械学报51(3), 1-17.
  • Ge, Y., Sihota, N., Hoelen, T., Miao, T., & Weindorf, D. C. (2020). Predicting total petroleum hydrocarbons in field soils with Vis–NIR models developed on laboratory-constructed samples.
  • Li, M., Wijewardane, N. K., Ge, Y., Xu, Z., & Wilkins, M. R. (2020). Visible/near infrared spectroscopy and machine learning for predicting polyhydroxybutyrate production cultured on alkaline pretreated liquor from corn stover. Bioresource Technology Reports9, 100386.
  • Lo, T. H., Rudnick, D. R., Singh, J., Nakabuye, H. N., Katimbo, A., Heeren, D. M., & Ge, Y. (2020). Field assessment of interreplicate variability from eight electromagnetic soil moisture sensors. Agricultural Water Management231, 105984.
  • Wijewardane, N. K., Hetrick, S., Ackerson, J., Morgan, C. L., & Ge, Y. (2020). VisNIR integrated multi-sensing penetrometer for in situ high-resolution vertical soil sensing. Soil and Tillage Research199, 104604.
  • Mazis, A., Choudhury, S. D., Morgan, P. B., Stoerger, V., Hiller, J., Ge, Y., & Awada, T. (2020). Application of high-throughput plant phenotyping for assessing biophysical traits and drought response in two oak species under controlled environment. Forest Ecology and Management465, 118101.
  • Ge, Y., Sihota, N., Hoelen, T., Miao, T., & Weindorf, D. C. (2020). Predicting total petroleum hydrocarbons in field soils with Vis–NIR models developed on laboratory-constructed samples.
  • Atefi, A., Ge, Y., Pitla, S., & Schnable, J. (2020). Robotic detection and grasp of maize and sorghum: stem measurement with contact. Robotics9(3), 58.
  • Ge, Y., Morgan, C. L., & Wijewardane, N. K. (2020). Visible and near‐infrared reflectance spectroscopy analysis of soils. Soil Science Society of America Journal84(5), 1495-1502.
  • Lo, T. H., Rudnick, D. R., DeJonge, K. C., Bai, G., Nakabuye, H. N., Katimbo, A., ... & Heeren, D. M. (2020). Differences in soil water changes and canopy temperature under varying water× nitrogen sufficiency for maize. Irrigation Science38(5), 519-534.
  • Zhao, B., Li, J., Baenziger, P. S., Belamkar, V., Ge, Y., Zhang, J., & Shi, Y. (2020). Automatic wheat lodging detection and mapping in aerial imagery to support high-throughput phenotyping and in-season crop management. Agronomy10(11), 1762.
  • Sanderman, J., Dangal, S. R., Todd-Brown, K. E., Hengl, T., Ferguson, R. R., Ge, Y., ... & Caon, L. (2020, December). Filling the soil data gap. In AGU Fall Meeting Abstracts (Vol. 2020, pp. B030-05).
  • Miao, C., Guo, A., Thompson, A. M., Yang, J., Ge, Y., & Schnable, J. C. (2021). Automation of leaf counting in maize and sorghum using deep learning. The Plant Phenome Journal4(1), e20022.
  • Atefi, A., Ge, Y., Pitla, S., & Schnable, J. (2021). Robotic technologies for high-throughput plant phenotyping: contemporary reviews and future perspectives. Frontiers in Plant Science12.
  • Wang, L., Zhou, Y., Hu, Q., Tang, Z., Ge, Y., Smith, A., ... & Shi, Y. (2021). Early detection of encroaching woody juniperus virginiana and its classification in multi-species forest using UAS imagery and semantic segmentation algorithms. Remote Sensing13(10), 1975.
  • AHM, N. C., Alkady, K. H., Jin, H., Bai, F., Samal, A., & Ge, Y. (2021). A deep convolutional neural network based image processing framework for monitoring the growth of soybean crops. In 2021 ASABE Annual International Virtual Meeting (p. 1). American Society of Agricultural and Biological Engineers.
  • Singh, J., Heeren, D. M., Ge, Y., Bai, G., Neale, C. M., Maguire, M. S., & Bhatti, S. (2021). Sensor-based irrigation of maize and soybean in East-Central Nebraska under a sub-humid climate. In 2021 ASABE Annual International Virtual Meeting (p. 1). American Society of Agricultural and Biological Engineers.
  • Bai, G. F., & Ge, Y. (2021). Cable Suspended Large-Scale Field Phenotyping Facility for High-Throughput Phenotyping Research. In High-Throughput Crop Phenotyping (pp. 39-53). Springer, Cham.
  • Bai, G., & Ge, Y. (2021). Crop Sensing and Its Application in Precision Agriculture and Crop Phenotyping. In Fundamentals of Agricultural and Field Robotics (pp. 137-155). Springer, Cham.
  • Meier, M. A., Xu, G., Lopez-Guerrero, M. G., Li, G., Smith, C., Sigmon, B., ... & Yang, J. (2021). Maize root-associated microbes likely under adaptive selection by the host to enhance phenotypic performance. bioRxiv.
  • Folkerts, C., Luck, J. D., Pitla, S. K., & Ge, Y. (2021). Optical Sensor Fusion Technique For Direct Nozzle Injection Chemical Flow Rate Monitoring. Journal of the ASABE, 0.
  • Wijewardane, N. K., Ge, Y., Sanderman, J., & Ferguson, R. (2021). Fine grinding is needed to maintain the high accuracy of mid‐infrared diffuse reflectance spectroscopy for soil property estimation. Soil Science Society of America Journal85(2), 263-272.
  • Zhao, L., Wang, L., Li, J., Bai, G., Shi, Y., & Ge, Y. (2021, April). Toward accurate estimating of crop leaf stomatal conductance combining thermal IR imaging, weather variables, and machine learning. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI (Vol. 11747, pp. 98-105). SPIE.
  • Wang, L., Li, J., Zhao, L., Zhao, B., Bai, G., Ge, Y., & Shi, Y. (2021, April). Investigate the potential of UAS-based thermal infrared imagery for maize leaf area index estimation. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VI (Vol. 11747, p. 1174703). SPIE.
  • Zhang, H., Ge, Y., Xie, X., Atefi, A., Wijewardane, N., & Thapa, S. (2021). Estimation of the Chlorophyll Concentration in Sorghum Using Three High Throughput Phenotyping Imaging Techniques.
  • Chai, Y. N., Ge, Y., Stoerger, V., & Schachtman, D. P. (2021). High‐resolution phenotyping of sorghum genotypic and phenotypic responses to low nitrogen and synthetic microbial communities. Plant, Cell & Environment44(5), 1611-1626.
  • Pittaki‐Chrysodonta, Z., Hartemink, A. E., Sanderman, J., Ge, Y., & Huang, J. (2021). Evaluating three calibration transfer methods for predictions of soil properties using mid‐infrared spectroscopy. Soil Science Society of America Journal85(3), 501-519.
  • Grzybowski, M., Wijewardane, N. K., Atefi, A., Ge, Y., & Schnable, J. C. (2021). Hyperspectral reflectance-based phenotyping for quantitative genetics in crops: Progress and challenges. Plant Communications2(4), 100209.
  • Singh, J., Ge, Y., Heeren, D. M., Walter-Shea, E., Neale, C. M., Irmak, S., ... & Maguire, M. S. (2021). Inter-relationships between water depletion and temperature differential in row crop canopies in a sub-humid climate. Agricultural Water Management256, 107061.
  • Murad, M. O. F., Ge, Y., Wijewardane, N. K., Ackerson, J. P., & Morgan, C. L. (2021, November). In-Situ Estimation of Soil Organic Carbon Concentrations and Stocks Along the Soil Profile Using a Penetrometer. In ASA, CSSA, SSSA International Annual Meeting. ASA-CSSA-SSSA.
  • Pittaki-Chrysodonta, Z., Hartemink, A. E., Sanderman, J., Ge, Y., & Huang, J. (2021, November). Calibration Transfer Methods for Predictions of Soil Properties Using Mid-Infrared Spectroscopy. In ASA, CSSA, SSSA International Annual Meeting. ASA-CSSA-SSSA.
  • Lily, Z. L., Fahlgren, N., Kutchan, T., Schachtman, D., Ge, Y., Gesch, R., ... & Abdel-Haleem, H. (2021). Discovering candidate genes related to flowering time in the spring panel of Camelina sativa. Industrial Crops and Products173, 114104.
  • Folkerts, C., Luck, J. D., Pitla, S. K., & Ge, Y. (2022). Optical Sensor System for Chemical Flow Rate Monitoring with Direct Nozzle Injection. Journal of the ASABE65(1), 87-95.
  • Yang, J., Rodene, E., Xu, G., Smith, C., Ge, Y., & Schnable, J. C. (2021). A UAV-based high-throughput phenotyping approach to assess time-series nitrogen responses and identify traits associated genetic components in maize.
  • Xu, G., Lyu, J., Obata, T., Liu, S., Ge, Y., Schnable, J. C., & Yang, J. (2022). A historically balanced locus under recent directional selection in responding to changed nitrogen conditions during modern maize breeding. bioRxiv.
  • Demattê, J. A., da Silveira Paiva, A. F., Poppiel, R. R., Rosin, N. A., Ruiz, L. F. C., de Oliveira Mello, F. A., ... & Silvero, N. E. (2022). The Brazilian Soil Spectral Service (BraSpecS): A User-Friendly System for Global Soil Spectra Communication [Erratum: March 2022, v. 14 (6)].
  • Grzybowski, M. W., Zwiener, M., Jin, H., Wijewardane, N. K., Atefi, A., Naldrett, M. J., ... & Schnable, J. C. (2022). Variation in morpho-physiological and metabolic responses to low nitrogen stress across the sorghum association panel. bioRxiv.
  • Demattê, J. A., Paiva, A. F. D. S., Poppiel, R. R., Rosin, N. A., Ruiz, L. F. C., Mello, F. A. D. O., ... & Silvero, N. E. (2022). The Brazilian S oil S pectral S ervice (BraSpecS): A User-Friendly System for Global Soil Spectra Communication. Remote Sensing14(3), 740.
  • Singh, J., Ge, Y., Heeren, D. M., Walter-Shea, E., Neale, C. M., Irmak, S., & Maguire, M. S. (2022). Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize. Sensors22(5), 1863.
  • Wang, S., Guan, K., Zhang, C., Lee, D., Margenot, A. J., Ge, Y., ... & Huang, Y. (2022). Using soil library hyperspectral reflectance and machine learning to predict soil organic carbon: Assessing potential of airborne and spaceborne optical soil sensing. Remote Sensing of Environment271, 112914.
  • Ge, Y., Wadoux, A., & Peng, Y. (2022). A primer on soil analysis using visible and near-infrared (vis-NIR) and mid-infrared (MIR) spectroscopy: Soil spectroscopy training manual# 1. Food & Agriculture Org.
  • Demattê, J. A., Paiva, A. F. D. S., Poppiel, R. R., Rosin, N. A., Ruiz, L. F. C., Mello, F. A. D. O., ... & Silvero, N. E. (2022). Correction: Demattê et al. The Brazilian Soil Spectral Service (BraSpecS): A User-Friendly System for Global Soil Spectra Communication. Remote Sens. 2022, 14, 740. Remote Sensing14(6), 1459.
  • Li, J., Schachtman, D. P., Creech, C. F., Wang, L., Ge, Y., & Shi, Y. (2022). Evaluation of UAV-derived multimodal remote sensing data for biomass prediction and drought tolerance assessment in bioenergy sorghum. The Crop Journal.
  • Shepherd, K. D., Ferguson, R., Hoover, D., van Egmond, F., Sanderman, J., & Ge, Y. (2022). A global soil spectral calibration library and estimation service. Soil Security7, 100061.
  • Izere, P., Zhao, B., Ge, Y., & Shi, Y. (2022, June). Estimation of plant height using UAS with RTK GNSS technology. In Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping VII (Vol. 12114, pp. 195-205). SPIE.
  • Zhang, H., Ge, Y., Xie, X., Atefi, A., Wijewardane, N. K., & Thapa, S. (2022). High throughput analysis of leaf chlorophyll content in sorghum using RGB, hyperspectral, and fluorescence imaging and sensor fusion. Plant Methods18(1), 1-17.
  • Bian, L., Zhang, H., Ge, Y., Čepl, J., Stejskal, J., & EL-Kassaby, Y. A. (2022). Closing the gap between phenotyping and genotyping: review of advanced, image-based phenotyping technologies in forestry. Annals of Forest Science79(1), 1-21.