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Advancing Deep Learning for Vision Based Agricultural Phenotyping and Monitoring

Project Team

  • Dr. Alexander Schwing, Assistant Professor, Dept. of Electrical and Computer Engineering
  • Dr. Girish Chowdhary, Assistant Professor, Dept. of Agricultural and Biological Engineering
  • Dr. Brian Diers, Professor, Dept. of Crop Sciences
  • Dr. Nicolas Martin, Assistant Professor, Dept. of Crop Sciences
  • Nathan Kleczweski, Senior Research Programmer, National Center for Supercomputing Applications
  • Dr. D.K. Lee, Associate Professor, Dept. of Crop Sciences
  • Dr. Adam Davis, Research Ecologist, United States Department of Agriculture, Agriculture Research Service

Abstract

Our primary objective is to simultaneously advance deep learning for machine vision based inference and agricultural phenotyping and monitoring. Agricultural data poses several unique challenges that are not obvious in other datasets. Our approach is to leverage these artifacts to create deep learning models and inference techniques that work well with agricultural data. Our team brings together expertise from machine learning, robotics, crop breeding, and crop pathology, and production agriculture. Utilizing the results from this seed grant, we will write several outstanding proposals to secure external funding for continuing this work.