Ali Moghimi, Ph.D.

Dr. Ali Moghimi

Position Title
Assistant Professor of Teaching

  • Biological and Agricultural Engineering
3040 Bainer Hall
Bio

Biography

Dr. Moghimi is an assistant professor of teaching in remote sensing and AI applications for precision and digital agriculture. He received his Ph.D. in Biosystems Engineering with a Ph.D. minor in Computer Science from the University of Minnesota.

Dr. Moghimi is passionate about teaching and conducting interdisciplinary research centered at the food-water-energy nexus. He teaches ABT 060, "Introduction to Unmanned Aerial Systems for Agriculture & Environmental Science." His current research focuses on applying innovative technologies (LiDAR and multispectral/hyperspectral imaging), automation (UAVs), and artificial intelligence (machine learning and deep learning algorithms) in agriculture to facilitate the digital revolution in agriculture.

Selected Publications

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

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