Visit Google Scholar for the most up-to-date list of publications

• Moghimi, A., Pourreza, A., 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, 12, 3515. https://doi.org/10.3390/rs12213515
• Pourreza, A., Moghimi, A., Niederholzer, F.J.A., 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, 12, 8862. https://doi.org/10.3390/su12218862
• Moghimi, A., Yang, C., & Anderson, J.A. 2020. Aerial hyperspectral imagery and deep neural networks for high-throughput yield phenotyping in wheat. Computers and Electronics in Agriculture, 172, 105299. https://doi.org/10.1016/j.compag.2020.105299
• Qiu, R., Yang, C., Moghimi, A., Zhang, M., & Steffenson, B. 2019. Detection of Fusarium head blight in wheat using a deep neural network and color imaging. Remote Sensing. https://www.mdpi.com/2072-4292/11/22/2658
• Moghimi, A., Yang, C., & Marchetto, P. M. 2018. Ensemble Feature Selection for Plant Phenotyping: A Journey from Hyperspectral to Multispectral Imaging. IEEE Access, 6, 56870-56884. https://doi.org/10.1109/ACCESS.2018.2872801
• Moghimi, A., Yang, C., Miller, M. E., Kianian, S. F., & Marchetto, P. M. 2018. A Novel Approach to Assess Salt Stress Tolerance in Wheat Using Hyperspectral Imaging. Frontiers in Plant Science, 9, 1182. https://doi.org/10.3389/fpls.2018.01182
• Moghimi, A., Saiedirad, M.H., & Ganji Moghadam, E. 2011. Interpretation of viscoelastic behaviour of sweet cherries (Prunus avium L.) using rheological models. International Journal of Food Science & Technology, 46, 855-861. https://doi.org/10.1111/j.1365-2621.2011.02563.x
• Moghimi, A., Aghkhani, M.H., Sazgarnia, A., & Sarmad, M. 2010. Vis/NIR spectroscopy and chemometrics for the prediction of soluble solids content and acidity (pH) of kiwifruit. Journal of Biosystems Engineering, 106, 205-302. https://doi.org/10.1016/j.biosystemseng.2010.04.002
• Moghimi, A., Aghkhani, M.H., Sazgarnia, A., & Abbaspour-Fard, M.H. 2009. Improvement of NIR transmission mode for internal quality assessment of fruit using different orientations. Journal of Food Process Engineering, 34, 1759-1774. https://doi.org/10.1111/j.1745-4530.2009.00547.x
  
  

Conference Processings

• Moghimi, A., Pourreza, A., & Zuniga-Ramirez, G. 2020. Radiometric calibration of airborne spectral data for plant phenotyping: a journey from raw images to reflectance images. Phenome Conference. Tucson, AZ.
• Cheung, K., Pourreza, A., Moghimi, A., & Zuniga-Ramirez, G. 2020. Calibration of photogrammetry-based canopy profile mapping using dense, sUAS-based LiDAR data in almond orchards. SPIE Conference on Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping V. Anaheim, CA.
• Moghimi, A., Pourreza, A., Cheung, K., Zuniga-Ramirez, G., Batista da Silva, B., Niederholzer, F., & Larbi, P. 2019. Development of a low-maintenance system to reduce spray drift without limiting the spray and air delivery in almond orchards. Almond Board Conference. Sacramento, CA.
• Cheung, K., Pourreza, A., Moghimi, A., Zuniga-Ramirez, G., Lampinen, B., & Shackel, K. 2019. Development of an unmanned aerial vehicle (UAV)-based canopy profile mapping technique to replace the mobile platform lightbar. Almond Board Conference. Sacramento, CA.
• Pourreza, A., Moghimi, A., Zuniga-Ramirez, G., Williams, L., & Fidelibus, M. 2019. Estimating nitrogen status of table grapes through aerial multispectral imaging. Sustainable Agriculture & Food Systems, Berlin, Germany.
• Moghimi, A., Yang, C., Anderson, J.A., & Reynolds, S.K. 2019. Deep autoencoder to reduce dimensionality of hyperspectral images collected by UAV flying over experimental plots. ASABE, Boston, MA.
• Moghimi, A., Yang, C., Anderson, J.A., & Reynolds, S.K. 2019. Selecting informative spectral bands using machine learning techniques to detect Fusarium head blight in wheat. ASABE, Boston, MA. (oral presentation.) https://elibrary.asabe.org/abstract.asp?aid=50476
• Moghimi, A., Yang, C., Anderson, J.A., & Reynolds, S.K. 2018. Aerial Imagery for Yield Prediction of Experimental Wheat Plots. ASABE, Detroit, MI.
• Moghimi, A., Yang, C., Miller, M. E., & Kianian, S. 2017. Hyperspectral imaging to identify salt-tolerant wheat lines. SPIE Conference on Autonomous Air and Ground Sensing Systems for Agricultural Optimization and Phenotyping II, Anaheim, CA. https://doi.org/10.1117/12.2262388
• Moghimi, A., Aghkhani, M.H., Golzarian, M.R., Rohani, A., & Yang, C. 2015. A Robo-vision Algorithm for Automatic Harvesting of Green Bell Pepper. ASABE, New Orleans, LA. https://elibrary.asabe.org/abstract.asp?aid=46320
• Moghimi, A., Aghkhani, M.H., & Golzarian, M.R. 2014. Grippers’ Design Factors Determined by Integration of Computer Vision System and Mechanical Tests. The 8th National congress on Biosystems Engineering and Mechanization, Mashhad, Iran.
• Saiedirad, M.H., Zarif Neshat, S., & Moghimi, A. 2011. Evaluation of Pomegranate Resistance against the Imposed Forces during Harvest. National Congress on Agricultural Loss, Tehran, Iran.
• Zarif Neshat, S., Saiedirad, M.H., & Moghimi, A. 2011. Effect of Harvest Time, Soil Moisture and Varieties on Mechanical Damage of Potato. National Congress on Agricultural Loss, Tehran, Iran.
• Moghimi, A., & Saiedirad, M.H. 2010. Viscoelastic Behavior of Cherries under Constant Strain. The 6th National congress on Agricultural Machinery Engineering and Mechanization, Tehran, Iran.
• Moghimi, A., Aghkhani, M.H., Sazgarnia, A., & Sarmad, M. 2008. Application of Near-infrared Spectroscopy in Determination of Internal Quality of Apple, Orange and Kiwifruit in a Nondestructive Way. The 18th National Congress on Food Technology, Mashhad, Iran.