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Speaker: Dr. Sruti Das Choudhury, Research Assistant Professor at University of Nebraska-Lincoln
Recent Advances of Artificial Intelligence in Multimodal Plant Phenotyping Analysis
- Teaching (20%): “Computer Vision” (CSCE 473/873) in the Department of Computer Science and Engineering
- Research (30%): Conduct research improving existing and developing new innovative algorithms for computer vision-based plant phenotyping research using a suite of imaging modalities, e.g. visible, fluorescent, infrared, near-infrared and hyperspectral; participate in transdisciplinary research pertaining to plant phenotyping.
- Scholary service and creative activity (50%): Lead the plant phenotyping software development using OpenCV, C++ and Matlab; dataset creation tailored to specific computer vision challenges with proper description and direction of use.
With the ease of digital image capturing facility and an enormous increase in the processing power of the computers, computer vision and artificial intelligence have gained immense popularity in order to meet current and emerging issues in agriculture relating to future food security under dwindling natural resources and projected climate variability. Imaging techniques facilitate the measurement of observable traits of plants as a result of complex interaction between genotype and environment (referred to as phenotypes) by analyzing a large number of plants in a short time interval with precision, nullifying the need for time-consuming physical human labor. Based on the electromagnetic spectrum in which an image is captured, information about different aspects of plants can be computed, e.g., visible light image sequences provide information about plant’s morphological structure, while hyperspectral images are well suited to describe a plant’s physiological processes and metabolism.
In this session, we would like to discuss two novel applications of artificial intelligence in plant phenotyping: (1) early detection and quantification of drought stress in plants using hyperspectral imagery and (2) detection of emergence timing and growth tracking of flowers using visible light image sequences.
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