Young Chang

Education

PhD – Seoul National University, South Korea, Control & Automation, School of Agricultural Biotechnology
MS - Seoul National University, South Korea, Food Engineering
BS - Seoul National University, South Korea, Food Engineering

Academic Interests

• Precision Agriculture Automation
• Cost-effective camera/sensor system development
• Biosystems automation & robotics (drone & unmanned ground vehicles)
• Spot-application 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

Google Scholar: https://scholar.google.com/citations?user=RGyqQGMAAAAJ&hl=en

Academic Responsibilities

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

Grants

• Project title: From satellite to cloud: Novel methods ensuring agricultural data confidentiality and integrity. Role in project: Principal Investigator. Funding Agency: Cyber-Ag-Law Research Collaborations. Duration: 2023.01 -2024.12, Amount: $249,996
• Project title: Connected Edge Computing for AI-based Agroecosystem: Big Data and Connected Technology for Sustainable Production. Role in project: Principal Investigator. Funding Agency: SDAES. Duration: 2022.10 -2025.09, Amount: $125,000
• Project title: Innovating a crop assessment system using a real-time, hardware-based drone image processing system to support on-the-spot decision in agriculture. Role in project: Principal Investigator. Funding Agency: Mitacs Accelerate through Lab2Market program. Duration: 2020.11 -2021.02, Amount: $15,000
• Project title: Development of Deep Learning Models for Amylose and Amylopectin Estimation in Cereal Grains with Near-Infrared Spectroscopy. Role in project: Principal Investigator. Funding Agency: Mitacs Accelerate Entrepreneur. Duration: 2020.08-2020.11, Amount: $15,000
• Project title: FPGA-based Drone Image Processing System for Rapid Crop Management Decision. Role in project: Principal Investigator. Funding Agency: Innovacorp, Blue-Green Challenge. Duration: 2019.11-2020.03, Amount: $5,000
• Project title: Infrastructure for a Soil-Landscape Analysis & Modelling Research Program. Role in project: Collaborator. Funding Agency: CFI-JELF. Duration: 2019-2024, Amount: $211,006
• Project title: Development of Synthetic Aperture Radar Image processing system using a Software Defined Radar for object detection in vegetation. Role in project: Principal Investigator. Funding Agency: NSERC – Undergraduate Student Research Awards (USRA). Duration: 2019.05-2019.08, Amount: $4,500
• Project title: Phenolic compounds assessment using hyper- and multi-spectral machine vision system and deep learning algorithm. Role in project: Principal Investigator/Co-supervisor. Funding Agency: Killam Program. Duration: 2018.09-2022.08, Amount: $60,000
• Project title: Unmanned Ground Vehicles (UGV) application for real-time grape phenolic compounds assessment. Role in project: Principal Investigator/Co-supervisor. Funding Agency: Nova Scotia Innovation & Research Graduate Scholarship Program. Duration: 2018.09-2022.08, Amount: $60,000
• Project title: Fast Real-Time Lobster Meat Yield Detection Using Non-destructive Technologies. Role in project: Principal Investigator/Supervisor. Funding Agency: Nova Scotia Innovation & Research Graduate Scholarship Program. Duration: 2018.05-2020.04, Amount: $20,000
• Project title: Real-time Optimization using ANN/Deep Convolutional Neural Network for Lowbush Blueberry Harvesting. Role in project: Principal Investigator. Funding Agency: NSERC Discovery Grant (Canadian equivalent of NSF Faculty Early Career Development (CAREER) Program). Duration: 2017.05-2022.04, Amount: $120,000
• Project title: Improving efficiency of commercial wild blueberry harvester using precision agriculture technologies. Project Areas: Precision agriculture, bioelectronics, bio-instrumentations and bio-system modelling. Role in project: Collaborator. Funding Agency: Natural Sciences and Engineering Research Council of Canada Collaborative Research and Development Grants (NSERC CRD PROGRAM). Duration: 2013.04-2016.03, Amount: $754,575

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

Applications of Research

Selected Patents, Books, and Publications
• 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).

† - corresponding author
• 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.†, & 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., & 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., & 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., & 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: https://doi.org/10.1002/cche.10546).
• Immaneni, A., & 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., & 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: https://doi.org/10.1016/j.compag.2021.106042).
• Shin, J., Chang, Y.K. †, Heung, B, Nguyen-Quang, T., Price, G. W., & 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., & 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.

Please watch some of research activity videos: https://drive.google.com/drive/folders/17eo86j49PaHPoIkt09NHuJrrxkoowarI
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Watch TV news : https://globalnews.ca/news/6159755/dalhousie-university-agriculture-robot/