UMN Alum Dr. Ali Moghimi to Present Dissertation Research in Upcoming Webinar

UMN Alum Dr. Ali Moghimi to Present Dissertation Research in Upcoming Webinar

Event Details:

  • Date: Tuesday, September 24, 2019
  • Time: 10:30 AM CDT
  • Event: IEEE RAS Technical Committee on Agricultural Robotics and Automation Webinar Series

  • Presentation Title: Artificial Intelligence and Hyperspectral Imaging for High-Throughput Plant Phenotyping

     

Abstract

Artificial intelligence (AI) is becoming an increasingly imperative tool for sustainable crop production in the era of digital agriculture. In this talk, I present my Ph.D. research work in which I utilized AI to leverage the unique advantages of hyperspectral imaging for investigating desired phenotyping traits in wheat with both indoor and field setups.

For our indoor setup, we developed a sensor-based framework for analysis of hyperspectral images to assess the difference between the salt tolerance of four wheat lines. We were able to attain a quantitative ranking as early as one day after applying salt treatment. In addition, we developed an ensemble feature selection pipeline to identify the most informative spectral bands associated with the desired trait in plant phenotyping. I present the results of testing the developed feature selection pipeline in finding the most prominent bands for salt stress assessment and Fusarium head blight detection in wheat.

For our field setup, we mounted the hyperspectral camera on an unmanned aerial vehicle to collect aerial imagery in two consecutive growing seasons from three experimental yield fields composed of hundreds of experimental wheat lines. We trained a deep neural network with fully connected layers for yield prediction. While conventional harvesting of plots for yield measurement relies on demanding, extremely laborious, and time-consuming tasks, our automated framework could predict the yield of wheat plots in a fast, cost-effective manner. In addition, our framework offers a unique insight for breeders to investigate the yield variation at sub-plot scale - a valuable new index in breeding programs to nominate high-yielding cultivars that are capable of producing a uniform yield across the plot. The results revealed that the proposed framework can also serve as a valuable tool for remote visual inspection of the plots and optimizing the plot size to investigate more lines in a dedicated field each year.

 

Presenter Biography

AliMoghimiseminar

Dr. Ali Moghimi is currently a postdoctoral research associate working in the Digital Agriculture lab at the University of California, Davis. He is passionate about conducting interdisciplinary research centered at the food-water-energy nexus. Ali's 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. He completed his Ph.D. at the University of Minnesota in February 2019. His Ph.D. research focused on developing sensor-based, automated frameworks for high-throughput phenotyping in wheat.

Educational Background:

  • Ph.D. in Biological and Agricultural Engineering, University of Minnesota (February 2019).
    • Focus: Sensor-based, automated frameworks for high-throughput wheat phenotyping.

Current Research Projects at UC Davis:

  1. Predicting nitrogen status in table grapes using aerial multispectral imagery.
  2. Developing a canopy profile mapping technique for almond orchards using UAV-based LiDAR data.
  3. Designing a low-maintenance spray drift reduction system without compromising spray or air delivery.

Dr. Moghimi’s innovative work aims to bridge the gap between technology and agriculture, enabling sustainable practices for the future of farming.

 

Join the Webinar

Don’t miss this opportunity to hear from a leading expert in AI-driven agricultural innovation. Dr. Moghimi’s work provides valuable insights into automation, cost reduction, and enhanced productivity for modern agriculture.

Event Details:

For more information, visit the IEEE RAS webinar page.

 

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