Paul Heinemann

Applied AI Research Thrusts

• Expert systems for agricultural production decision making
• Agricultural odor assessment utilizing sensors and neural networks to simulate human odor panel assessments
• Automation of human-based tasks in specialty crop production

Externally Funded AI Projects

• Green Fruit Removal Dynamics and Robotic Green Fruit Thinning System (USDA – NIFA)
• CPS: Medium: Integrated Design of Sensing, Networks, and Cooperative Control of Multi-Vehicle Systems for Preventing Frost and Freeze Damage to Flowers and Buds of Fruit Trees (NSF/USDA-NIFA)
• Precision Crop Load Management for Apples (USDA-NIFA)

Recent Publications

1. Zahid, A., He, L., Zeng, L., Choi, D., Schupp, J., & Heinemann, P. 2020. Development of a robotic end-effector for apple tree pruning. Transactions of the ASABE. 63(4): 847-856.
2. Chang, F., & Heinemann, P. 2020. Prediction of human odour assessments based on hedonic tone method using instrument measurements and multi-sensor data fusion integrated Neural Networks. Biosystems Engineering. 200:272-283.
3. Zahid, A., Mahmud, M.S., Choi, D., Heinemann, P.H., & Schupp, J. 2020. Development of an integrated 3R end-effector with a cartesian manipulator for pruning apple trees. Computers and Electronics in Agriculture. 179.