Paul Heinemann

Professor of Agricultural and Biological Engineering

Paul Heinemann

Huck Affiliations

Most Recent Publications

Development of a Machine vision system for apple bud thinning in precision crop load management

Kittiphum Pawikhum, Yanqiu Yang, Long He, Paul Heinemann, 2025, Computers and Electronics in Agriculture

Precision heating strategy based dynamic heater path planning for frost protection in apple orchards

Weiyun Hua, Long He, Paul Heinemann, Minghui Zhu, 2025, Biosystems Engineering on p. 117-132

Frost management in agriculture with advanced sensing, modeling, and artificial intelligent technologies: A review

Weiyun Hua, Paul Heinemann, Long He, 2025, Computers and Electronics in Agriculture

Heat transfer modeling with fixed and mobile heaters for frost protection in apple orchards

Weiyun Hua, Paul Heinemann, He Long, 2024, Computers and Electronics in Agriculture

Canopy protection cyber-physical system (CPCPS) for smart agricultural management of frost damage in apple orchards

Weiyun Hua, Paul Heinz Heinemann, Long He, 2024, Computers and Electronics in Agriculture

UGV-Based Precision Spraying System for Chemical Apple Blossom Thinning on Trellis Trained Canopies

Xinyang Mu, Long He, Paul Heinemann, James Rawlinson Schupp, Manoj Karkee, Minghui Zhu, 2024, Journal of Field Robotics on p. 1-12

Presymptomatic Detection of Fire Blight in Apple Orchards Using Portable Diffuse Reflectance Spectroscopy: A Machine Learning Approach

Yanqiu Yang, Paul H. Heinemann, Kittiphum Pawikhum, Kari A. Peter, 2024, ASABE

Green Fruit-Stem Pairing and Clustering for Machine Vision System in Robotic Thinning of Apples

Magni Hussain, Long He, James Rawlinson Schupp, David Lyons, Paul Heinemann, 2024, Journal of Field Robotics on p. 1-28

Detection and infected area segmentation of apple fire blight using image processing and deep transfer learning for site-specific management

Md Sultan Mahmud, Long He, Azlan Zahid, Paul Heinemann, Daeun Choi, Grzegorz Krawczyk, Heping Zhu, 2023, Computers and Electronics in Agriculture

Green fruit segmentation and orientation estimation for robotic green fruit thinning of apples

Magni Hussain, Long He, James Rawlinson Schupp, David Lyons, Paul Heinemann, 2023, Computers and Electronics in Agriculture on p. 107734

Most-Cited Papers

The development of mechanical apple harvesting technology: A review

Zhao Zhang, Paul Heinemann, Jude Liu, James Schupp, Tara Baugher, 2016, Transactions of the ASABE on p. 1165-1180

Technological advancements towards developing a robotic pruner for apple trees: A review

Azlan Zahid, Md Sultan Mahmud, Long He, Paul Heinemann, Daeun Choi, James Schupp, 2021, Computers and Electronics in Agriculture

Development of a LiDAR-guided section-based tree canopy density measurement system for precision spray applications

Md Sultan Mahmud, Azlan Zahid, Long He, Daeun Choi, Grzegorz Krawczyk, Heping Zhu, Paul Heinemann, 2021, Computers and Electronics in Agriculture

On-tree apple fruit size estimation using stereo vision with deep learning-based occlusion handling

Omeed Mirbod, Daeun Choi, L He, Paul Heinemann, Paul Heinemann, Long He, Richard P. Marini, Richard Marini, 2022, Biosystems Engineering on p. 27-42

Development of an integrated 3R end-effector with a cartesian manipulator for pruning apple trees

Azlan Zahid, Md Mahmud, Long He, Daeun Choi, James Schupp, Paul Heinemann, Paul Heinemann, James Schupp, 2020, Computers and Electronics in Agriculture on p. 105837

Design and field test of a low-cost apple harvest-assist unit

Z. Zhang, P. H. Heinemann, J. Liu, J. R. Schupp, T. A. Baugher, 2016, Transactions of the ASABE on p. 1149-1156

Mask R-CNN based apple flower detection and king flower identification for precision pollination

Xinyang Mu, Long He, Paul Heinemann, James Schupp, Manoj Karkee, 2022, Smart Agricultural Technology on p. 10

Green fruit segmentation and orientation estimation for robotic green fruit thinning of apples

Magni Hussain, Long He, James Rawlinson Schupp, David Lyons, Paul Heinemann, 2023, Computers and Electronics in Agriculture on p. 107734

Development of a robotic end-effector for apple tree pruning

Azlan Zahid, L. He, Lihua Zeng, D. Choi, J. Schupp, P. Heinemann, 2020, Transactions of the ASABE on p. 847-856

Economic analysis of a low-cost apple harvest-assist unit

Zhao Zhang, Paul H. Heinemann, 2017, HortTechnology on p. 240-247

News Articles Featuring Paul Heinemann

May 23, 2025

Penn State agricultural technology event aimed at ‘closing the innovation gap’

Penn State’s College of Agricultural Sciences will convene leaders from academia, industry, government and stakeholder networks for a symposium focused on accelerating agricultural innovation through cross-sector collaboration.

Full Article

Oct 25, 2024

Research team gets grant to study precision chemical thinning of apple blossoms

To assist apple producers and mitigate environmental harm, the USDA has awarded a three-year, $601,125 grant to a team of Penn State researchers for the development of a robotic precision spraying system.

Full Article

Jan 26, 2023

Machine vision system developed capable of locating king flowers on apple trees

A machine vision system capable of locating and identifying apple king flowers within clusters of blossoms on trees in orchards was devised by Penn State researchers — a critical early step in the development of a robotic pollination system — in a first-of-its-kind study.

Full Article

Jul 18, 2022

Agricultural engineers design early step for robotic, green-fruit thinning

Penn State agricultural engineers have developed, for the first time, a prototype “end-effector” capable of deftly removing unwanted apples from trees — the first step toward robotic, green-fruit thinning.

Full Article

Aug 15, 2019

Automation on display at Ag Progress Days

A shrinking labor pool has forced farmers both locally and nationally to seek other measures to ensure high yields on their crops while also maintaining human-level efficiency. Researchers at Penn State are looking at ways to mitigate the dwindling labor workforce, and they’ve collaborated with local farmers through automation to help compensate.

Full Article