Full list of publication and latest papers can be found at:

2023

  • Feng, A., C. N. Vong, JN. Zhou, S. Conway, J. Zhou, E. Vories, K. Sudduth and N. Kitchen. 2022. Developing an image processing pipeline to improve the position accuracy of single UAV images. Computers and Electronics in Agriculture. 206, 107650. https://doi.org/10.1016/j.compag.2023.107650
  • Canella Vieira, C., JN. Zhou†, M. Usovsky, T. D. Vuong, H. Amanda, D. Lee, Z. Li, Zhou, J. G. Shannon, H. T. Nguyen, and P. Chen*. 2022. Exploring machine learning algorithms to unveil genomic regions associated with resistance to southern root-knot nematode in soybeans. Frontiers in Plant Science. 13. https://doi.org/10.3389/fpls.2022.1090072

2022

  • Xu, Z. †, R. Sullivan, Zhou*, T. T. Lim, T. J. Safranski, C. Bromfield and Z. Yan. 2022. Detection of vulvar volume change around estrus in sows using a LiDAR camera and machine learning. Smart Agricultural Technology. 3, 100090. https://doi.org/10.1016/j.atech.2022.100090
  • Canella Vieira, C., JN. Zhou†, C. Cross, J. Heiser, B. Diers, D. E. Riechers, Zhou, D. H. Jarquin, H. T. Nguyen, G. Shannon, and P. Chen*. 2022. Differential responses of soybean genotypes to off-target dicamba damage. Crop Science. In Press. https://doi.org/10.1002/csc2.20757
  • Vong, C. N. †, S. Conway, J. Zhou*, N. R. Kitchen and K. A. Sudduth. 2022. Corn stand uniformity estimation and mapping using UAV imagery and deep learning. Computers and Electronics in Agriculture. 198, 107008. https://doi.org/10.1016/j.compag.2022.107008
  • Canella Vieira, C., JN. Zhou†, M. Usovsky, T. D. Vuong, H. Amanda, D. Lee, Z. Li, Zhou, J. G. Shannon, H. T. Nguyen, and P. Chen*. 2022. Exploring machine learning algorithms to unveil genomic regions associated with resistance to southern root-knot nematode in soybeans. Frontiers in Plant Science. 13, 883280. https://doi.org/10.3389/fpls.2022.883280
  • Canella Vieira, C., Sakar†, F. Tian†, JN. Zhou†, D. Jarquin, H. T. Nguyen, J. Zhou, and P. Chen*. 2022. Differentiate soybean response to off-target dicamba damage based on UAV imagery and machine learning. Remote Sensing. 14(7), 1618. https://doi.org/10.3390/rs14071618
  • Feng, A.†, J. Zhou*, E. Vories, and K. Sudduth. 2022. Quantifying the effects of soil texture and weather on cotton development and yield using UAV imagery. Precision Agriculture. Online. https://doi.org/10.1007/s11119-022-09883-6

2021

  • Bernhardt, H., L. Schumacher, J. Zhou, M. Treiber, and K. Shannon. (2021). Digital Agriculture Infrastructure in the USA and Germany. Engineering Proceedings, 9(1), 1. https://doi.org/10.3390/engproc2021009001
  • Zhou, J., J. Zhou, A. Scaboo, D. Yungbluth, and P. Chen. 2021. Soybean variety selection using UAV high-throughput phenotyping and machine learning. Frontiers in Plant Science. 12, 2543. https://doi.org/10.3389/fpls.2021.768742
  • Oseland, E., K. Shannon, J. Zhou, F. Fritschi, M. D. Bish, and K. W. Bradley. Evaluating the spectral response and yield of soybean following exposure to sublethal rates of 2,4-D and Dicamba at vegetative and reproductive growth stages. Remote Sens. 2021(13), 3682. https://doi.org/10.3390/rs13183682
  • Zhou, J., H. Mou, J. Zhou, H. Ye, M.L. Ali, H. Nguyen, and P. Chen. 2021. Qualification of soybean responses to flooding stress using UAV-based imagery and deep learning. Plant Phenomics. 2021, 9892570. https://doi.org/10.34133/2021/9892570
  • Vong, C. N., S. Conway, J. Zhou*, N. R. Kitchen and K. A. Sudduth. 2021. Early corn stand count of different cropping systems using UAV-imagery and deep learning. Computers and Electronics in Agriculture. 186, 106214. https://doi.org/10.1016/j.compag.2021.106214
  • Vong, C. N., S. A. Stewart, Zhou*, N. R. Kitchen and K. A. Sudduth. 2021. Estimation of Corn Emergence Date Using UAV Imagery. Transactions of the ASABE, 64(4), 1173-1183. https://doi.org/10.13031/trans.14145
  • Fu, D.,  M. Scaboo, X. Niu, Q. Wang, and J. Zhou*. 2021. Non-destructive phenotyping fatty acid trait of single soybean seeds using reflective hyperspectral imagery. Journal of Food Process Engineering. e106001. https://doi.org/10.1111/jfpe.13759
  • Zhou, S., † Mou, †J. Zhou, J. Zhou*, H. Ye and H. Nguyen. 2021. An automated plant phenotyping system for evaluation of salt tolerance in soybean. Computers and Electronics in Agriculture. 182, 106001. https://doi.org/10.1016/j.compag.2021.106001
  • Zhou, J., Zhou*, H. Ye, M.L. Ali, H. Nguyen, and P. Chen. 2021. Yield estimation of soybean breeding lines using UAV multispectral imagery and convolutional neuron network. Biosystems Engineering. 204, 90-103. https://doi.org/10.1016/j.biosystemseng.2021.01.017

2020

  • Feng, A., Zhou*, E. Vories, and K. Sudduth. 2020. Evaluation of cotton emergence using UAV-based narrow-band spectral imagery with customized image alignment and stitching algorithms. Remote Sensing, 12(11), 1764. https://doi.org/10.3390/rs12111764
  • Zhou, J., Zhou*, † H. Ye, M.L. Ali, H. Nguyen, and P. Chen. 2020. Classification of soybean leaf wilting due to drought stress using UAV-based imagery. Computers and Electronics in Agriculture. 175, 105576. https://doi.org/10.1016/j.compag.2020.105576
  • Feng, A., M. Zhang, K. Sudduth, E. Vories, and J. Zhou*. 2020. Yield estimation in cotton using UAV-based multi-sensor imagery. Biosystems Engineering. 193, 101-114. https://doi.org/10.1016/j.biosystemseng.2020.02.014
  • Zhang, M., J. Zhou*, K. A. Sudduth, and N. R. Kitchen. 2020. Estimation of maize yield and effects of variable-rate nitrogen application using UAV-based RGB imagery. Biosystems Engineering. 189, 24-35. https://doi.org/10.1016/j.biosystemseng.2019.11.001

2019

  • Zhou, J., D. Yungbluth, C.N. Vong, A. Scaboo, and J. Zhou*. 2019. Estimation of the Maturity Date of Soybean Breeding Lines Using UAV-Based Multispectral Imagery. Remote Sens. 11, 2075. https://doi.org/10.3390/rs11182075
  • Cao, W., J. Zhou, Y. Yuan, H. Ye, H. Nguyen, J. Chen, and J. Zhou*. 2019. Quantifying variation in soybean due to flood using a low-cost 3D imaging system. Sensors. 19(12), 2682. https://doi.org/10.3390/s19122682
  • Zhang, M., A. Feng, J. Zhou, and X. Lv. 2019. Cotton yield prediction using remote visual and spectral images captured by UAV systems. Transactions of the Chinese Society of Agricultural Engineering. 35(5): 91-98.
  • Feng, A., M. Zhang, K. Sudduth, E. Vories, and J. Zhou. 2019. Cotton yield estimation from UAV-based plant height. Transactions of the ASABE. 62(2). https://:doi.org/10.13031/trans.1306
  • Ranjan, R., A. Chandel, L. Khot*, H. Bahlol, J. Zhou, R. Boydston, and P. Miklas. 2019. Irrigated pinto bean crop stress and yield assessment using ground based low altitude remote sensing technology. Information Processing in Agriculture. https://doi.org/10.1016/j.inpa.2019.01.005

2018

  • Zhang, C., M. Pumphrey, J. Zhou, Q. Zhang, and S. Sankaran*. 2018. Development of automated high-throughput phenotyping system for wheat evaluation in controlled environment. Transactions of the ASABE. 62(1):61-74. https://doi.org/10.13031/trans.12856
  • Zhou, J., X. Fu, L. Schumacher, and J. Zhou*. 2018. Evaluating geometric measurement accuracy based on 3D reconstruction of automated imagery in greenhouse. Sensors. 18(7), 2270-2286.  https://doi.org/10.3390/s18072270
  • Zhou, J.*, H. Chen, J. Zhou, X. Fu, H. Ye, H. Nguyen. 2018. Develop an automated phenotyping platform for quantifying soybean dynamic responses to salinity stress in greenhouse environments. Computers and Electronics in Agriculture. 151, 319-330. https://doi.org/10.1016/j.compag.2018.06.016
  • Sankaran, S.*, J. Zhou, P. 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. https://doi.org/10.1016/j.compag.2018.05.034
  • Zhou, J., L. Khot, R. A*. Boydston, P. N. Miklas and L. Porter. 2018. Low altitude remote sensing technologies for crop stress monitoring: a case study on spatial and temporal monitoring of irrigated pinto bean. Precision Agriculture. 19(3), 555-569. https://doi.org/10.1007/s11119-017-9539-0

2017

  • Zhou, J., L. Khot*, H. Bahlol, G. Kafle, T. Peters, M. D. Whiting, Q. Zhang, and D. Granatstein. 2017. In-field sensing for crop loss management: efficacy of air-blast sprayer generated crosswind in rainwater removal from cherry canopies. Crop Protection. 91 (2017), 27-33.

2016

  • Zhou, J., L. He, Q. Zhang*, and M. Karkee. 2016. Field Evaluation of a mechanical-assist cherry harvesting system. Engineering in Agriculture, Environment and Food. 9(4), 324-331.
  • Zhou, J., M. J. Pavek, S. C. Shelton, Z. J. Holden, and S. Sankaran*. 2016. Aerial multispectral imaging for crop hail damage assessment in potato. Computers and Electronics in Agriculture. 127(2016), 406-412.
  • Zhou, J., L. Khot*, T. Peters, M. D. Whiting, Q. Zhang, and D. Granatstein. 2016. Efficacy of unmanned helicopter in rainwater removal from cherry canopies. Computers and Electronics in Agriculture. 124(2016), 161-167.
  • Kafle, G., L. Khot*, J. Zhou, H. Bahlol, and Y. Si. 2016. Towards precision spray applications to prevent rain-induced sweet cherry cracking: understanding calcium washout due to rain and fruit cracking susceptibility. Scientia Horticulturae. 203(2016), 152-157.
  • Trapp, J. J., C. A. Urrea, J. Zhou, L. R. Khot, S. Sankaran, and P. N. Miklas*. 2016. Selective phenotyping traits related to multiple stress and drought response in dry bean. Crop Science. 56(2016), 1-13.
  • Wang, M., P. Ellsworth, J. Zhou, A. Cousins, S. Sankaran*. 2016. Evaluation of water-use efficiency in foxtail millet (Setaria italica) using visible-near infrared and thermal spectral sensing techniques. Talanta. 152(2016), 531-539.
  • Zhou, J., L. He, Q. Zhang*, and M. Karkee. 2016. Analysis of shaking-induced cherry fruit motion and damage. Biosystems Engineering. 144(2016): 105-114.
  • Zhou, J., L. He, Q. Zhang*, and M. Karkee. 2016. Effect of catching surface and tilt angle on bruise damage of sweet cherry due to mechanical impact. Computers and Electronics in Agriculture. 121(2016), 282-289.
  • He, L., J. Zhou, Q. Zhang*, and H. J. Charvet. 2016. A string twining robot for high-trellis hop production. Computers and Electronics in Agriculture. 121(2016), 207-214.

Before 2015

  • He, L., J. Zhou, Q. Zhang*, and M. Karkee. 2015. Evaluation of multi-pass mechanical harvesting on ‘Skeena’ sweet cherries. HortScience. 50(8), 1178-1182.
  • Zhou, J., L. He, Q. Zhang*, and M. Karkee. 2014. Effect of excitation position of a handheld shaker on fruit removal efficiency and damage in mechanical harvesting of sweet cherry. Biosystems Engineering. 125(2014): 36-44.
  • Zhou, J., L. He, Q. Zhang*, X. Du, D. Chen, and M. Karkee. 2013. Evaluation of the influence of shaking frequency and duration in mechanical harvest of sweet cherry. Applied Engineering in Agriculture. 29(5): 607-612.
  • He, L., J. Zhou, X. Du, D. Chen, Q. Zhang*, and M. Karkee. 2013. Energy efficacy analysis of a mechanical shaker in sweet cherry harvesting. Biosystems Engineering. 116(4): 309-315.