Zhihang Song
Assistant Professor; Emphasis: Plant Phenomics, Controlled Environment Agriculture
Areas of expertise
About
Dr. Zhihang Song is an Assistant Professor of Controlled Environment Agriculture (CEA) Phenomics in the Department of Horticulture with a focus on advancing digital agriculture to enhance food security and sustainability. His work integrates technologies such as imagery sensors, machine learning, computer vision, and robotics to tackle critical challenges in specialty crop breeding and crop condition monitoring in CEA. Dr. Song is also passionate about mentoring the next generation of scientists, engineers, and scholars ready to contribute to the agricultural industry and academia.
Education
Doctor of Philosophy, Agricultural Engineering.
Purdue University, IN, United States (2024)
Master of Science, Agricultural Engineering.
Purdue University, IN, United States (2020)
Bachelor of Science, Agricultural Engineering.
Purdue University, IN, United States (2017)
Courses
- HORT 8160: Measurement and Control in Plant and Soil Science (Fall 2025)
- HORT 7000: Master's Research (Fall 2025)
- HORT 7000: Master's Research (Spring 2026)
- HORT 9000: Doctoral Research (Spring 2026)
- HORT 7000: Master's Research (Summer 2026)
- HORT 9000: Doctoral Research (Summer 2026)
- HORT 9000: Doctoral Research (Fall 2026)
- HORT 4710: Plant Phenotyping Technologies (Fall 2026)
- HORT 6710: Plant Phenotyping Technologies (Fall 2026)
- HORT 7000: Master's Research (Fall 2026)
Scholarly Works
- SegRoot: A high throughput segmentation method for root image analysis, COMPUTERS AND ELECTRONICS IN AGRICULTURE, (2019).
- LeafSpec: An accurate and portable hyperspectral corn leaf imager, COMPUTERS AND ELECTRONICS IN AGRICULTURE, (2020).
- High-energy and durable aqueous Zn batteries enabled by multi-electron transfer reactions, ENERGY MATERIALS, (2024).
- Automated in-field leaf-level hyperspectral imaging of corn plants using a Cartesian robotic platform, COMPUTERS AND ELECTRONICS IN AGRICULTURE, (2021).
- Machine learning-based spectral and spatial analysis of hyper-and multi-spectral leaf images for Dutch elm disease detection and resistance screening, ARTIFICIAL INTELLIGENCE IN AGRICULTURE, (2023).
Contact
Mailing Address
1111 Plant Sciences Building
Athens, GA 30602
Shipping Address
1111 Plant Sciences Building
Athens, GA 30602
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Controlled Environment AgricultureMeet the “Fab 5” giving Georgia growers a high-tech edge through controlled environment agriculture