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Thesis Defence: Sensing Technologies for High-Throughput Plant Phenotyping: A Comprehensive Review with a Case Study
July 4, 2023 at 9:00 am - 12:00 pm
Zhenyu Ma, supervised by Dr. Zheng Liu, will defend their dissertation titled “Sensing Technologies for High-Throughput Plant Phenotyping: A Comprehensive Review with a Case Study” in partial fulfillment of the requirements for the degree of Master of Applied Science in Electrical Engineering.
An abstract for Zhenyu Ma’s thesis is included below.
Defences are open to all members of the campus community as well as the general public. Please email email@example.com to receive the zoom link for this defence.
Agriculture has been instrumental in shaping human civilization and the social-economic conditions of society. However, the food supply chain has been severely disrupted by the rapid growth of the world’s population, increased urbanization, and the unforeseen impact of the pandemic. A recent report suggests that the global population could reach 9.3 billion by 2060. This impending crisis highlights the urgent need for innovative techniques and efforts to increase food production and ensure adequate food supply for the world’s population. Crop monitoring has the potential to address the global issue of food scarcity by providing farmers with real-time crop information to support increased food production. High-throughput plant phenotyping is considered a vital aspect of crop monitoring, as it enables the acquisition of large-scale crop characteristics data.
This thesis reviews state-of-art, high-throughput plant phenotyping development and corresponding image sensors and platforms. It also analyzes the current situation of crop monitoring and identifies the upcoming challenges and potential future trends for researchers in this field.
Based on the review result, a case study about a low-cost depth image sensor-based crop monitoring system is developed for plant height and biomass estimation work. The developed system can collect real-time plant phenotyping data and upload it to the cloud for further analysis. The system incorporates a 3D construction-based method for estimating plant height and a DGCNN-based deep learning model for plant biomass estimation. Experimental results show promising potential in achieving crop monitoring.