{"id":17,"date":"2013-02-26T20:32:40","date_gmt":"2013-02-26T20:32:40","guid":{"rendered":"http:\/\/faculty.engineering.ucdavis.edu\/template\/?page_id=17"},"modified":"2023-02-28T17:43:14","modified_gmt":"2023-02-28T17:43:14","slug":"publications","status":"publish","type":"page","link":"https:\/\/faculty.engineering.ucdavis.edu\/pourreza\/publications\/","title":{"rendered":"Publications"},"content":{"rendered":"

Visit the Digital Agriculture lab Website<\/a> for an updated list of publications<\/p>\n

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Refereed Journal Papers<\/h2>\n
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  1. Jafarbiglu, H., & Pourreza, A.<\/strong> (2023). Impact of sun-view geometry on canopy spectral reflectance variability. ISPRS Journal of Photogrammetry and Remote Sensing, 196, 270-286.<\/li>\n
  2. Jafarbiglu, H., Pourreza, A. <\/strong>(2022). A Comprehensive Review of Remote Sensing Sensors, Platforms, and Applications for Nut Crop Production. Journal of Computers and Electronics in Agriculture, 197, <\/em>106844.<\/em><\/li>\n
  3. Omidi, R., Pourreza, A<\/strong>., Moghimi, A., Zuniga-Ramirez, G., Jafarbiglu, H., Maung, Z., & Westphal, A. (2022). A Semi-supervised approach to cluster symptomatic and asymptomatic leaves in root lesion nematode infected walnut trees. Computers and Electronics in Agriculture<\/em>, 194<\/em>, 106761.<\/li>\n
  4. Zhang X, Pourreza A<\/strong>, Cheung KH, Zuniga-Ramirez G, Lampinen BD and Shackel KA (2021). Estimation of Fractional Photosynthetically Active Radiation From a Canopy 3D Model; Case Study: Almond Yield Prediction. Front. Plant Sci.<\/em> 12:715361. doi: 10.3389\/fpls.2021.715361<\/li>\n
  5. Pourreza, A., <\/strong>Moghimi, A., Niederholzer, F. J., Larbi, P. A., Zuniga-Ramirez, G., Cheung, K. H., & Khorsandi, F. (2020). Spray Backstop: A Method to Reduce Orchard Spray Drift Potential without Limiting the Spray and Air Delivery. Sustainability<\/em>, 12<\/em>(21), 8862.<\/li>\n
  6. Omidi, R., Moghimi, A., Pourreza, A<\/strong>., El-Hadedy, M., & Eddin, A. S. (2020, December). Ensemble Hyperspectral Band Selection for Detecting Nitrogen Status in Grape Leaves. In 2020 19th IEEE International Conference on Machine Learning and Applications (ICMLA)<\/em> (pp. 286-293). IEEE.<\/li>\n
  7. Moghimi, A., Pourreza, A., <\/strong>Zuniga-Ramirez, G., Williams, L. E., & Fidelibus, M. W. (2020). A Novel Machine Learning Approach to Estimate Grapevine Leaf Nitrogen Concentration Using Aerial Multispectral Imagery. Remote Sensing<\/em>, 12<\/em>(21), 3515.<\/li>\n
  8. Montazar, A., Krueger, R., Corwin, D., Pourreza, A.<\/strong>, Little, C., Rios, S., & Snyder, R. L. (2020). Determination of Actual Evapotranspiration and Crop Coefficients of California Date Palms Using the Residual of Energy Balance Approach. Water<\/em>, 12<\/em>(8), 2253.<\/li>\n
  9. Chaji, S., Pourreza, A.<\/strong>, Pourreza, H., & Rouhani, M. (2018). Estimation of the camera spectral sensitivity function using neural learning and architecture. Journal of the Optical Society of America A, 35<\/em>(6), 850-858. doi:10.1364\/JOSAA.35.000850<\/li>\n
  10. Pourreza, A.<\/strong>, Lee, W. S., Czarnecka, E., Verner, L., & Gurley, W. (2017). Feasibility of Using the Optical Sensing Techniques for Early Detection of Huanglongbing in Citrus Seedlings. Robotics, 6<\/em>(2), 11.<\/li>\n
  11. Pourreza, A.<\/strong>, Lee, W. S., Ritenour, M. A., & Roberts, P. (2016). Spectral Characteristics of Citrus Black Spot Disease. HortTechnology, 26<\/em>(3), 254-260.<\/li>\n
  12. Pourreza, A.,<\/strong> Lee, W. S., Etxeberria, E., & Zhang, Y. (2016). Identification of Citrus Huanglongbing Disease at the Pre-Symptomatic Stage Using Polarized Imaging Technique. IFAC-PapersOnLine, 49<\/em>(16), 110-115.<\/li>\n
  13. Pourreza, A.<\/strong>, Lee, W. S., Etxeberria, E., & Banerjee, A. (2015). An evaluation of a vision-based sensor performance in Huanglongbing disease identification. Biosystems Engineering, 130<\/em>(0), 13-22. doi:http:\/\/dx.doi.org\/10.1016\/j.biosystemseng.2014.11.013<\/li>\n
  14. Pourreza, A.<\/strong>, Lee, W. S., Ehsani, R., Schueller, J. K., & Raveh, E. (2015). An optimum method for real-time in-field detection of Huanglongbing disease using a vision sensor. Computers and Electronics in Agriculture, 110<\/em>(0), 221-232. doi:http:\/\/dx.doi.org\/10.1016\/j.compag.2014.11.021<\/li>\n
  15. Pourreza, A.<\/strong>, Lee, W. S., Raveh, E., Ehsani, R., & Etxeberria, E. (2014). Citrus Huanglongbing detection using narrow-band imaging and polarized illumination. Trans. ASABE, 57<\/em>(1), 259-272.<\/li>\n
  16. Pourreza, A.<\/strong>, Pourreza, H., Abbaspour-Fard, M.-H., & Sadrnia, H. (2012). Identification of nine Iranian wheat seed varieties by textural analysis with image processing. Computers and Electronics in Agriculture, 83<\/em>, 102-108. doi:10.1016\/j.compag.2012.02.005<\/li>\n
  17. Aghkhani, M. H., & Pourreza, A.<\/strong> (2007). Egg Sorting by Machine Vision Method. Journal of Agricultural Engineering Research, 8<\/em>(3), 141-150.<\/li>\n<\/ol>\n

    Patent<\/h2>\n
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    1. Raveh, Eran, Lee, Wonsuk, Pourreza, Alireza <\/strong>and Ehsani, Reza. \u201cMethod for Huanglongbing (HLB) Detection.\u201d WO 2015\/193885, 2015 and US20170131200A1, 2017.<\/li>\n<\/ol>\n<\/div>\n<\/div>\n<\/article>\n<\/div>\n<\/div>\n<\/div>\n