Young Chang

Title

Precision Agriculture Automation Engineer

Office Building

Raven Precision Agriculture Center

Office

115

Mailing Address

Raven Precision Ag Building 115
Ag and Biosystems Engineering-Box 2100
University Station
Brookings, SD 57007

Education

• Ph.D. in agricultural biotechnology | Seoul National University, South Korea
• M.S. in food engineering | Seoul National University, South Korea
• B.S. in food engineering | Seoul National University, South Korea

Academic Interests

• Precision agriculture automation
• Cost-effective camera/sensor system development
• Biosystems authomation and robotics (drone and unmanned ground vehicles)
• Spot-applications of Agrochemicals using image processing and deep learning
• Information and communication technology (ICT) for agricultural big data processing
• Edge/cloud computing connectivity and cyber security
• Animal behavior analysis using image/IoT devices

Academic Responsibilities

• AST-426 (Fall) – Technology Applications for Precision Agriculture
• PRAG-304 (Spring) – Electrical Diagnostics for Farm Machinery

Grants

• From satellite to cloud: Novel methods ensuring agricultural data confidentiality and integrity. Cyber-Ag-Law Research Collaborations, Jan. 2023 - Dec. 2024,$249,996.
• Connected Edge Computing for AI-based Agroecosystem: Big Data and Connected Technology for Sustainable Production. SDAES, Oct. 2022 - Sept. 2025, $125,000.
• Innovating a crop assessment system using a real-time, hardware-based drone image processing system to support on-the-spot decision in agriculture. Mitacs Accelerate through Lab2Market program, Nov. 2020 - Feb. 2021, $15,000.
• Development of Deep Learning Models for Amylose and Amylopectin Estimation in Cereal Grains with Near-Infrared Spectroscopy. Mitacs Accelerate Entrepreneur, Aug. 2020 - Nov. 2020, $15,000.
• FPGA-based Drone Image Processing System for Rapid Crop Management Decision. Innovacorp, Blue-Green Challenge, Nov. 2019 - March 2020, $5,000.
• Infrastructure for a Soil-Landscape Analysis and Modelling Research Program. CFI-JELF, 2019 - 2024, $211,006.
• Development of Synthetic Aperture Radar Image processing system using a Software Defined Radar for object detection in vegetation. NSERC – Undergraduate Student Research Awards (USRA), May 2019 - Aug. 2019, $4,500.
• Phenolic compounds assessment using hyper- and multi-spectral machine vision system and deep learning algorithm. Killam Program, Sept. 2018 - Aug. 2022, $60,000.
• Unmanned Ground Vehicles (UGV) application for real-time grape phenolic compounds assessment. Nova Scotia Innovation and Research Graduate Scholarship Program, Sept. 2018 - Aug. 2022, $60,000.
• Fast Real-Time Lobster Meat Yield Detection Using Non-destructive Technologies. Nova Scotia Innovation and Research Graduate Scholarship Program, May 2018 - April 2020, $20,000.
• Real-time Optimization using ANN/Deep Convolutional Neural Network for Lowbush Blueberry Harvesting. NSERC Discovery Grant (Canadian equivalent of NSF Faculty Early Career Development (CAREER) Program), May 2017 - April 2022, $120,000.
• Improving efficiency of commercial wild blueberry harvester using precision agriculture technologies. Natural Sciences and Engineering Research Council of Canada Collaborative Research and Development Grants (NSERC CRD PROGRAM), April 2013 - March 2016, $754,575.

Patents

• Variable rate sprayer system and method of variably applying agrochemicals (US Patent No.: 8488874 B2).
• Koji control system using knowledge-based database, method, and medium recording that method program (Korean Patent No. 10-0589524).
• Kimchi refrigerator and kimchi ripening method (Korean Patent No. 10-0588131).

Work Experience

• Samsung Electronics, Living System Laboratory.
• Mission to Mars project at UC Davis.
• Bio-imaging and Sensing Center at NDSU.
• Precision Agriculture Research Program at Nova Scotia Agricultural College.

Publications

• Shin, J., Chang, Y.K., Heung, B. (2022). Detection of powdery mildew using image-based texture analysis with supervised machine learning and deep learning. Texture analysis in image processing. Vol 1. CRC Press (Taylor & Francis Group). In press.
• Chang, Y. K. and Rehman, T. U. (2017). Current and Future Applications of Cost-Effective Smart Cameras in Agriculture. Robotics and Mechatronics for Agriculture, 75-120. CRC Press (Taylor & Francis Group).
• Ravichandran, P., Viswanathan, S., Pan, Y. and Chang, Y.K.† Estimation of Blast Severity in Rice with Deep Learning Networks and Proximal Canopy Images from Universal Blast Nursery (UBN). Smart Agricultural Technology. ATECH-D-22-00242_R1. Under revision.
• Shin, J., Mahmud, M., Rehman, T. U., Ravichandran, P., Heung, B. and Chang, Y. K. (2023). Trends and Prospect of Machine Vision Technology for Stresses and Diseases Detection in Precision Agriculture. AgriEngineering, 5(1), 20-39.
• Ravichandran, P., Viswanathan, S., Ravichandran, S., Pan, Y. and Chang, Y.K. (2022) Estimation of grain quality parameters in rice with Near-Infrared Spectroscopy and Deep Learning. Cereal Chemistry, 99 (4), 907-919. In press DOI:10.1002/cche.10546.
• Immaneni, A., and Chang, Y. K. (2022). Real-time counting of strawberry using cost-effective embedded GPU and YOLOv4-tiny. In 2022 ASABE Annual International Meeting (p. 1). American Society of Agricultural and Biological Engineers.
• Shin, J., Chang, Y.K., Heung, B., Nguyen-Quang, T., Price, G. W. and Al-Mallahi, A. (2021). Deep learning application for image-based powdery mildew disease detection on strawberry leaves. Computers and Electronics in Agriculture, 183, 106041 DOI:10.1016/j.compag.2021.106042.
• Shin, J., Chang, Y.K., Heung, B, Nguyen-Quang, T., Price, G. W. and Al-Mallahi, A. (2020). Effect of directional augmentation using supervised machine learning techniques – A case study of strawberry powdery mildew detection. Biosystems Engineering, 194, 49-60.
• Rehman, T., Mahmud, M.S., Chang, Y.K., Jian, J. and Shin, J. (2019). Current and future applications of statistical machine learning algorithms for agricultural machine vision systems. Computers and Electronics in Agriculture, 156, 585-605.