{"id":111,"date":"2019-04-06T03:53:39","date_gmt":"2019-04-06T03:53:39","guid":{"rendered":"https:\/\/cafnrfaculty.missouri.edu\/mupaa\/?page_id=111"},"modified":"2023-07-25T04:29:58","modified_gmt":"2023-07-25T04:29:58","slug":"peer-reviewed","status":"publish","type":"page","link":"https:\/\/cafnrfaculty.missouri.edu\/mupaa\/publications\/peer-reviewed\/","title":{"rendered":"Peer-reviewed papers"},"content":{"rendered":"<hr \/>\n<p>Full list of publication and latest papers can be found at:<\/p>\n<ul>\n<li>ResearchGate: <a href=\"https:\/\/www.researchgate.net\/profile\/Jianfeng-Zhou-9\" target=\"_blank\" rel=\"noopener\">https:\/\/www.researchgate.net\/profile\/Jianfeng-Zhou-9<\/a><\/li>\n<li>Google Scholar: <a href=\"https:\/\/scholar.google.com\/citations?user=QgVBu-kAAAAJ&amp;hl=en\" target=\"_blank\" rel=\"noopener\">https:\/\/scholar.google.com\/citations?user=QgVBu-kAAAAJ&amp;hl=en<\/a><\/li>\n<\/ul>\n<hr \/>\n<h3>2023<\/h3>\n<ul>\n<li>Feng, A., C. N. Vong, JN. Zhou, S. Conway, <strong>J. Zhou<\/strong>, E. Vories, K. Sudduth and N. Kitchen. 2022. Developing an image processing pipeline to improve the position accuracy of single UAV images. <em>Computers and Electronics in Agriculture<\/em>. 206, 107650<em>.<\/em> <a href=\"https:\/\/doi.org\/10.1016\/j.compag.2023.107650\">https:\/\/doi.org\/10.1016\/j.compag.2023.107650<\/a><\/li>\n<li>Canella Vieira, C., JN. Zhou<sup>\u2020<\/sup>, M. Usovsky, T. D. Vuong, H. Amanda, D. Lee, Z. Li, <strong> Zhou<\/strong>, 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. <em>Frontiers in Plant Science<\/em>. 13. <a href=\"https:\/\/doi.org\/10.3389\/fpls.2022.1090072\">https:\/\/doi.org\/10.3389\/fpls.2022.1090072<\/a><\/li>\n<\/ul>\n<hr \/>\n<h3>2022<\/h3>\n<ul>\n<li>Xu, Z.<sup> \u2020<\/sup>, R. Sullivan, <strong> Zhou*<\/strong>, 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. <em>Smart Agricultural Technology<\/em>. 3, 100090. <a href=\"https:\/\/doi.org\/10.1016\/j.atech.2022.100090\">https:\/\/doi.org\/10.1016\/j.atech.2022.100090<\/a><\/li>\n<li>Canella Vieira, C., JN. Zhou<sup>\u2020<\/sup>, C. Cross, J. Heiser, B. Diers, D. E. Riechers, <strong> Zhou<\/strong>, D. H. Jarquin, H. T. Nguyen, G. Shannon, and P. Chen*. 2022. Differential responses of soybean genotypes to off-target dicamba damage. <em>Crop Science<\/em>. <em>In Press<\/em><em>. <\/em><a href=\"https:\/\/doi.org\/10.1002\/csc2.20757\">https:\/\/doi.org\/10.1002\/csc2.20757<\/a><\/li>\n<li>Vong, C. N.<sup> \u2020<\/sup>, S. Conway, <strong>J. Zhou<\/strong>*, N. R. Kitchen and K. A. Sudduth. 2022. Corn stand uniformity estimation and mapping using UAV imagery and deep learning. <em>Computers and Electronics in Agriculture<\/em>. 198, 107008<em>.<\/em> <a href=\"https:\/\/doi.org\/10.1016\/j.compag.2022.107008\">https:\/\/doi.org\/10.1016\/j.compag.2022.107008<\/a><\/li>\n<li>Canella Vieira, C., JN. Zhou<sup>\u2020<\/sup>, M. Usovsky, T. D. Vuong, H. Amanda, D. Lee, Z. Li, <strong> Zhou<\/strong>, 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. <em>Frontiers in Plant Science<\/em>. 13, 883280. <a href=\"https:\/\/doi.org\/10.3389\/fpls.2022.883280\">https:\/\/doi.org\/10.3389\/fpls.2022.883280<\/a><\/li>\n<li>Canella Vieira, C., Sakar<sup>\u2020<\/sup>, F. Tian<sup>\u2020<\/sup>, JN. Zhou<sup>\u2020<\/sup>, D. Jarquin, H. T. Nguyen, <strong>J. Zhou<\/strong>, and P. Chen<strong>*<\/strong>. 2022. Differentiate soybean response to off-target dicamba damage based on UAV imagery and machine learning. <em>Remote Sensing<\/em>. 14(7), 1618. <a href=\"https:\/\/doi.org\/10.3390\/rs14071618\">https:\/\/doi.org\/10.3390\/rs14071618<\/a><\/li>\n<li>Feng, A.<sup>\u2020<\/sup>, <strong>J. Zhou<\/strong>*, E. Vories, and K. Sudduth. 2022. Quantifying the effects of soil texture and weather on cotton development and yield using UAV imagery. <em>Precision Agriculture<\/em>. Online<em>.<\/em> <a href=\"https:\/\/doi.org\/10.1007\/s11119-022-09883-6\">https:\/\/doi.org\/10.1007\/s11119-022-09883-6<\/a><\/li>\n<\/ul>\n<h3>2021<\/h3>\n<hr \/>\n<ul>\n<li>Bernhardt, H., L. Schumacher, <strong>J. Zhou<\/strong>, M. Treiber, and K. Shannon. (2021). Digital Agriculture Infrastructure in the USA and Germany. <em>Engineering Proceedings<\/em>, 9(1), 1. <a href=\"https:\/\/doi.org\/10.3390\/engproc2021009001\">https:\/\/doi.org\/10.3390\/engproc2021009001<\/a><\/li>\n<li>Zhou, J., J. <strong>Zhou<\/strong>, A. Scaboo, D. Yungbluth, and P. Chen. 2021. Soybean variety selection using UAV high-throughput phenotyping and machine learning. <em>Frontiers in Plant Science<\/em>. 12, 2543<em>.<\/em> <a href=\"https:\/\/doi.org\/10.3389\/fpls.2021.768742\">https:\/\/doi.org\/10.3389\/fpls.2021.768742<\/a><\/li>\n<li>Oseland, E., K. Shannon, <strong> J. Zhou<\/strong>, 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. <em>Remote Sens.<\/em> 2021(13), 3682. <a href=\"https:\/\/doi.org\/10.3390\/rs13183682\">https:\/\/doi.org\/10.3390\/rs13183682<\/a><\/li>\n<li>Zhou, J., H. Mou, <strong>J. Zhou<\/strong>, 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. <em>Plant Phenomics<\/em>. 2021, 9892570. <a href=\"https:\/\/doi.org\/10.34133\/2021\/9892570\">https:\/\/doi.org\/10.34133\/2021\/9892570<\/a><\/li>\n<li>Vong, C. N., S. Conway, <strong>J. Zhou<\/strong>*, N. R. Kitchen and K. A. Sudduth. 2021. Early corn stand count of different cropping systems using UAV-imagery and deep learning. <em>Computers and Electronics in Agriculture.<\/em> 186, 106214<em>. <\/em><a href=\"https:\/\/doi.org\/10.1016\/j.compag.2021.106214\">https:\/\/doi.org\/10.1016\/j.compag.2021.106214<\/a><\/li>\n<li>Vong, C. N., S. A. Stewart, <strong> Zhou<\/strong>*, N. R. Kitchen and K. A. Sudduth. 2021. Estimation of Corn Emergence Date Using UAV Imagery. <em>Transactions of the ASABE<\/em>, 64(4), 1173-1183. <a href=\"https:\/\/doi.org\/10.13031\/trans.14145\">https:\/\/doi.org\/10.13031\/trans.14145<\/a><\/li>\n<li>Fu, D., \u00a0M. Scaboo, X. Niu, Q. Wang, and <strong>J. Zhou*<\/strong>. 2021. Non-destructive phenotyping fatty acid trait of single soybean seeds using reflective hyperspectral imagery. <em>Journal of Food Process Engineering.<\/em> e106001. <a href=\"https:\/\/doi.org\/10.1111\/jfpe.13759\">https:\/\/doi.org\/10.1111\/jfpe.13759<\/a><\/li>\n<li>Zhou, S., <strong><sup>\u2020<\/sup><\/strong> Mou, <strong><sup>\u2020<\/sup><\/strong>J. Zhou, <strong>J. Zhou*<\/strong>, H. Ye and H. Nguyen. 2021. An automated plant phenotyping system for evaluation of salt tolerance in soybean. <em>Computers and Electronics in Agriculture.<\/em> 182, 106001. <a href=\"https:\/\/doi.org\/10.1016\/j.compag.2021.106001\">https:\/\/doi.org\/10.1016\/j.compag.2021.106001<\/a><\/li>\n<li>Zhou, J., <strong> Zhou*<\/strong>, 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. <em>Biosystems Engineering<\/em>. 204, 90-103. <a href=\"https:\/\/doi.org\/10.1016\/j.biosystemseng.2021.01.017\">https:\/\/doi.org\/10.1016\/j.biosystemseng.2021.01.017<\/a><\/li>\n<\/ul>\n<h3>2020<\/h3>\n<hr \/>\n<ul>\n<li>Feng, A.,<strong> Zhou<\/strong>*, E. Vories, and K. Sudduth. 2020. Evaluation of cotton emergence using UAV-based narrow-band spectral imagery with customized image alignment and stitching algorithms. <em>Remote Sensing, <\/em>12(11), 1764. <a href=\"https:\/\/doi.org\/10.3390\/rs12111764\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/doi.org\/10.3390\/rs12111764<\/a><\/li>\n<li>Zhou, J.,<strong> Zhou*<\/strong>, <strong><sup>\u2020 <\/sup><\/strong>H. Ye, M.L. Ali, H. Nguyen, and P. Chen. 2020. Classification of soybean leaf wilting due to drought stress using UAV-based imagery. <em>Computers and Electronics in Agriculture<\/em>. 175, 105576. <a href=\"https:\/\/doi.org\/10.1016\/j.compag.2020.105576\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/doi.org\/10.1016\/j.compag.2020.105576<\/a><\/li>\n<li>Feng, A., M. Zhang, K. Sudduth, E. Vories, and <strong>J. Zhou<\/strong>*. 2020. Yield estimation in cotton using UAV-based multi-sensor imagery. <em>Biosystems Engineering.<\/em> 193, 101-114. <a href=\"https:\/\/doi.org\/10.1016\/j.biosystemseng.2020.02.014\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/doi.org\/10.1016\/j.biosystemseng.2020.02.014<\/a><\/li>\n<li>Zhang, M., <strong>J. Zhou*<\/strong>, K. A. Sudduth, and N. R. Kitchen. 2020. Estimation of maize yield and effects of variable-rate nitrogen application using UAV-based RGB imagery. <em>Biosystems Engineering.<\/em> <em>189<\/em>, 24-35. <a href=\"https:\/\/doi.org\/10.1016\/j.biosystemseng.2019.11.001\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/doi.org\/10.1016\/j.biosystemseng.2019.11.001<\/a><\/li>\n<\/ul>\n<h3>2019<\/h3>\n<hr \/>\n<ul>\n<li>Zhou, J., D. Yungbluth, C.N. Vong, A. Scaboo, and <strong>J. Zhou*<\/strong>. 2019. Estimation of the Maturity Date of Soybean Breeding Lines Using UAV-Based Multispectral Imagery. <em>Remote Sens.<\/em> <em>11<\/em>, 2075. <a href=\"https:\/\/doi.org\/10.3390\/rs11182075\">https:\/\/doi.org\/10.3390\/rs11182075<\/a><\/li>\n<li>Cao, W., J. Zhou, Y. Yuan, H. Ye, H. Nguyen, J. Chen, and<strong> J. Zhou*<\/strong>. 2019. Quantifying variation in soybean due to flood using a low-cost 3D imaging system. <em>Sensors<\/em>. 19(12), 2682<em>. <\/em><a href=\"https:\/\/doi.org\/10.3390\/s19122682\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/doi.org\/10.3390\/s19122682<\/a><\/li>\n<li>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.<\/li>\n<li>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).\u00a0<a href=\"https:\/\/doi.org\/10.13031\/trans.13067\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/:doi.org\/10.13031\/trans.1306<\/a><\/li>\n<li>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. <a href=\"https:\/\/doi.org\/10.1016\/j.inpa.2019.01.005\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/doi.org\/10.1016\/j.inpa.2019.01.005<\/a><\/li>\n<\/ul>\n<h3>2018<\/h3>\n<hr \/>\n<ul>\n<li>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. <a href=\"https:\/\/doi.org\/10.13031\/trans.12856\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/doi.org\/10.13031\/trans.12856<\/a><\/li>\n<li>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.\u00a0 <a href=\"https:\/\/doi.org\/10.3390\/s18072270\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/doi.org\/10.3390\/s18072270<\/a><\/li>\n<li>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. <a href=\"https:\/\/doi.org\/10.1016\/j.compag.2018.06.016\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/doi.org\/10.1016\/j.compag.2018.06.016<\/a><\/li>\n<li>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. <a href=\"https:\/\/doi.org\/10.1016\/j.compag.2018.05.034\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/doi.org\/10.1016\/j.compag.2018.05.034<\/a><\/li>\n<li>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. <a href=\"https:\/\/doi.org\/10.1007\/s11119-017-9539-0\" target=\"_blank\" rel=\"noopener noreferrer\">https:\/\/doi.org\/10.1007\/s11119-017-9539-0<\/a><\/li>\n<\/ul>\n<h3>2017<\/h3>\n<hr \/>\n<ul>\n<li>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.<\/li>\n<\/ul>\n<h3>2016<\/h3>\n<hr \/>\n<ul>\n<li>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.<\/li>\n<li>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.<\/li>\n<li>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.<\/li>\n<li>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.<\/li>\n<li>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.<\/li>\n<li>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.<\/li>\n<li>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.<\/li>\n<li>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.<\/li>\n<li>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.<\/li>\n<\/ul>\n<h3>Before 2015<\/h3>\n<hr \/>\n<ul>\n<li>He, L., J. Zhou, Q. Zhang*, and M. Karkee. 2015. Evaluation of multi-pass mechanical harvesting on \u2018Skeena\u2019 sweet cherries. HortScience. 50(8), 1178-1182.<\/li>\n<li>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.<\/li>\n<li>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.<\/li>\n<li>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.<\/li>\n<\/ul>\n","protected":false},"excerpt":{"rendered":"<p>Full list of publication and latest papers can be found at: ResearchGate: https:\/\/www.researchgate.net\/profile\/Jianfeng-Zhou-9 Google Scholar: https:\/\/scholar.google.com\/citations?user=QgVBu-kAAAAJ&amp;hl=en 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. 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