Source: PENNSYLVANIA STATE UNIVERSITY submitted to
INTEGRATED SYSTEMS RESEARCH AND DEVELOPMENT IN AUTOMATION AND SENSORS FOR SUSTAINABILITY OF SPECIALTY CROPS
Sponsoring Institution
National Institute of Food and Agriculture
Project Status
NEW
Funding Source
HATCH
Reporting Frequency
Annual
Accession No.
1016510
Grant No.
(N/A)
Project No.
PEN04653
Proposal No.
(N/A)
Multistate No.
W-3009
Program Code
(N/A)
Project Start Date
Oct 1, 2018
Project End Date
Sep 30, 2023
Grant Year
(N/A)
Project Director
Choi, DA.
Recipient Organization
PENNSYLVANIA STATE UNIVERSITY
208 MUELLER LABORATORY
UNIVERSITY PARK,PA 16802
Performing Department
Agri & Biological Engineering
Non Technical Summary
The U.S. specialty crop industry faces on-going challenges of addressing increasing production costs and a declining workforce while satisfying consumer demands for high-quality crops produced with minimal environmental impacts. Automation and precision management technologies offer the potential to minimize labor requirements and increase production efficiency. This 5-year project will address state-of-the-art sensing technologies for monitoring an agricultural field and mechanized solutions and field robotics including the human-robot collaboration for field operations. Approval of this project will be beneficial to a range of robotics applications in agriculture including pruning, fruit thinning, training, and harvesting. Wide adoption of robotics technology in agriculture will reduce labor demand substantially and reduce production costs so that the specialty crop industry remains economically and socially sustainable in the long term.
Animal Health Component
0%
Research Effort Categories
Basic
10%
Applied
90%
Developmental
(N/A)
Classification

Knowledge Area (KA)Subject of Investigation (SOI)Field of Science (FOS)Percent
4021119202080%
2051119106020%
Knowledge Area
205 - Plant Management Systems; 402 - Engineering Systems and Equipment;

Subject Of Investigation
1119 - Deciduous tree fruits, general/other;

Field Of Science
2020 - Engineering; 1060 - Biology (whole systems);
Goals / Objectives
Study interactions between machinery and crop to provide basis for creating optimal mechanical and/or automated solutions for specialty crop production Design and evaluate automation systems which incorporate varying degrees of mechanization and sensors to assist specialty crop industries with labor, management decisions, and reduction of production costs Adapt biological concepts associated with specialty crop production, harvest, and postharvest handling into quantifiable parameters which can be sensed Develop specialty crop architectures and systems that are more amenable to mechanized production Develop sensors and sensing systems which can phenotype and measure and interpret quality parameters Develop collaboration and work in partnership with equipment and technology manufacturers to commercialize and implement the outcomes of this project
Project Methods
As the labor shortage grows, the past decade has seen the rapid development of mechanized solutions for manual tasks for orchard crop production. Our team has been developing various mechanized systems in orchard management such as tree pruning, thinning, and fruit harvesting. These mechanized systems could be further enhanced by the development of crop-specific smart sensors, field machinery, and robots.In agriculture, biological systems are continuously changing states with highly dynamic behaviors spanning spatial and temporal dimensions. Such environments require collecting accurate and reliable agricultural information in a high spatiotemporal resolution. Also, uncertainties related to weather and climate remain a challenging issue in agricultural productivity.A conventional problem-solving approach based on modeling may be insufficient to respond to complex biological systems in uncontrolled environments. Due to advancements in technologies, field robotics can be combined with a high-end computer in the field for those applications requiring high-performance computing. Also, site-specific sensors will be needed to support various orchard tasks such as orchard decision-making surrounding production and marketing to enable specialty crop producers to manage human and environmental resources more sustainably.Fully automated technology may not be feasible in all operations in the near future. In some operations, the cost of replacing human dexterity and complex decision-making capability with equipment is not justified. Farm workers still need to be engaged in these operations. Therefore, it is essential to introduce a robotic solution with human-robot interaction for semi-automated device assisting human labor.Finally, in order to gain broad impact for the proposed works, we will invite growers and the general public to demonstrations in both research orchards and local commercial orchards. The results and outcomes from this project will be used for both research and extension publications. Below is the statement about what each PI will be doing for each objective they selected.Dr. Choi will develop sensors related to Obj. #3, #4, and #5. To achieve human-like qualities in specialty crop management, agricultural robots must heavily rely on sensing with many uncertainties related to the environment, position information and execution of the task itself. For Obj. #3, PI Choi will investigate the levels of canopy density to ensure optimal sensing performances while preserving crop yields. An intelligent sensing system for mushrooms to evaluate crop maturity and diseases will be developed for Obj.#4. In Obj. #5., wireless/wearable sensors will be developed to provide an interface between human workers and agricultural machinery.Dr. He will be involved with activities in Obj. #3 and #5. For Obj. #3, PI He's research will mainly focus on the development of new conceptual end-effectors for branch pruning and training with better accessibility to the branches. Also, the optimal path for the end-effector to reach the destination location will be studied. In addition, safe navigation in an orchard environment and automation of mushroom removal dynamics will be investigated. In Obj. #5, four major studies on design and evaluation of automation systems will be carried out: 1) Mechanical tree fruit harvesting system; 2) Robotic tree fruit crop management, including pruning, thinning, and training; 3) Automated irrigation system for tree fruit orchards; and 4) Autonomous orchard platform for assisting multiple orchard operations.Dr. Heinemann will be involved with activities in Obj. #3 and #5. In Obj. #3, PI Heinemann will investigate the canopy structures, distribution of fruit, and other physiological characteristics to help with the design of mechanical units. For Obj. #5, mechanical and automated thinning, harvest assist, pruning, and other production units for tree fruit production will be designed and developed. Also, PI Heinemann will participate in designing automated frost protection systems for tree fruit.Dr. Liu - Penn State researchers have been working on low-cost mechanical apple harvest-assist devices. One of the top challenges is the stability of the platforms when pickers stand on them, especially for hilly orchards in Northeast USA. PI Liu's research will be examining the design concept of an automated inclination-adjustable or "auto-leveling" base for apple mechanical harvest-assist devices. Research activities will focus on examining design concepts, which is to use inclinometers to detect the ground slope changes and use solenoid hydraulic control valves and cylinders to maintain the platform level with respect to the ground slope change. Dynamic response and accuracy of electro-hydraulic systems caused by pressure change and flexibility of system components could be extended research activities based on funding opportunities.Dr. Schupp will be assisting the engineers with the Obj. #1, #2, and #3.Dr. Baugher will be helping PI Schupp with his commitments relative to #2. Also, her role will be to test new tree architectures with grower stakeholders in order to obtain industry feedback.

Progress 10/01/19 to 09/30/20

Outputs
Target Audience:Specialty crop growers and machinery manufacturers as well as academic community for research founding distribution. Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided?Research opportunities for undergraduate, graduate, and postdocs were provided throughout multiple research projects. How have the results been disseminated to communities of interest?A total of seven extension publications and magazine articles were published to distribute research findings to stakeholders; and, atotal of eight extension presentations were provided to stakeholders in the tree fruit industry. What do you plan to do during the next reporting period to accomplish the goals?We will continue to conduct research and field trials as well as extension servicesin the below areas: 1) Intelligent sensing system and mechanized solutions for precision crop management in apple orchard 2) Robotic tree fruit crop management including pruning and thinning 3) Automated irrigation system for tree fruit orchards 4) Autonomous orchard platform for assisting multiple orchard operations including harvesting 5) Automated frost control system using drone and autonomous ground vehicle 6) In-field root phenotyping system for maize roots 7) Black cherry flower stage evaluation system for developing a phenology model

Impacts
What was accomplished under these goals? Goals 1, 2, 3 (Heinemann, He) Orchard temperature profiles were measured utilizing a propane heater in April 2020. This effort was part of the project on automated frost protection. Air temperatures were measured in horizontal grid and vertical grid patterns to determine the distribution of heat. This will help determine the movement patterns of autonomous ground vehicles carrying the heaters. Goals 1, 3, 5 (Heinemann, Choi) The RootRobot unit, part of the DOE ARPA-E(Department of EnergyAdvanced Research Projects Agency-Energy)DEEPERproject, a root-depth model, was further tested in the laboratory, then brought to the field in September 2020 to begin tests on actual corn plantings. The RootRobot excavates and prepares corn stalks for phenotyping through imaging. Goals 1, 2, 3, 4 (He, Schupp) A three-rotational (3R) degrees of freedom (DoF) end-effector was designed and integrated with a cartesian manipulator by considering maneuvering, spatial, mechanical, and horticultural requirements. Simulations and a series of field work were conducted to test the performance of the system. The field tests validated the simulation results, and the end-effector successfully cut branches up to ~25 mm diameter at wide range of orientations. Meanwhile, a simulation study focused on investigating the branch accessibility of a six-rotational (6R) degrees of freedom (DoF) robotic manipulator with a shear blade type end-effector. With the developed algorithms, a collision-free path was created to the targeted branches in the virtual environment. Goals 1, 2, 3 (He) An unmanned ground-based canopy density measurement system was developed to support precision spraying in apple orchards. A data processing and analysis algorithm was also developed to measure point cloud indices from a 3D LiDAR sensor to describe the distribution of tree canopy density. Finally, a canopy density map was generated to provide a graphical view of the tree canopy density in different sections. Goals 1, 2, 3 (He) An IoT-based precision irrigation system with LoRaWAN technology was developed and evaluated in both vegetable field and tree fruit orchards. Soil moisture sensors were installed in the field, and the data was sent to a cloud-based platform in real-time (10 minutes interval). The irrigation system was automatically operated with controlling of the solenoid valves to apply water to the field based on the soil moisture thresholds. Goals 1, 2, 3 (Choi) Heating requirement maps were created utilizing unmanned aerial vehicle (UAV)-based thermal and red, green, blue (RGB) cameras, computer vision technique, and artificial flower bud critical temperatures to simulate orchard heating demands during frost events. The results demonstrated the feasibility of the proposed orchard heating requirement determination methodology, which has the potential to be a critical component of an autonomous, precise frost management system. Goals 1, 2, 3 (Choi) A 3D machine vision system was developed to detect individual mushrooms among highly clustered crops. A practical 3D mobile imaging platform for mushroom production beds acquired RGB-D images from various angles, and created high-resolution composite point cloud images using an iterative closest algorithm. Mushroom detection accuracies using various point cloud image resolutions were compared to investigate an optimal level of image details for both processing time and accuracy of the system. Goals 1, 2, 3 (Choi) An external lighting system using LEDs by over-current was developed to produce a powerful flash as a viable active lighting source for daytime imaging. The system was deployed to an apple orchard to take images on both sunny and cloudy days on different canopy structures. The results indicate a substantial improvement over using a camera's auto-exposure setting for outdoor imaging regarding both color consistency and motion blur effects on images.

Publications

  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Zhang, X., He, L., Zhang, J., Whiting, M. D., Karkee, M., & Zhang, Q. (2020). Determination of key canopy parameters for mass mechanical apple harvesting using supervised machine learning and principal component analysis (PCA). Biosystems Engineering, 193, 247-263.
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Zeng, L., Feng, J., & He, L. (2020). Semantic segmentation of sparse 3D point cloud based on geometrical features for trellis-structured apple orchard. Biosystems Engineering, 196, 46-55.
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: 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), 847-856.
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Fu, H., Karkee, M., He, L., Duan, J., Li, J., & Zhang, Q. (2020). Bruise Patterns of Fresh Market Apples Caused by Fruit-to-Fruit Impact. Agronomy, 10(1), 13.
  • Type: Journal Articles Status: Awaiting Publication Year Published: 2020 Citation: Zhang, X., He, L., Karkee, M., Whitting, M., & Zhang, Q. (2020). Field Evaluation of Targeted Shake-and-Catch Harvesting Technologies for Fresh Market Apple. Transactions of the ASABE. [In press].
  • Type: Journal Articles Status: Awaiting Publication Year Published: 2020 Citation: Zahid, A., Mahmud, M., He, L., Choi, D., Schupp, J., & Heinemann, P. Development of an Integrated 3R End-effector with a Cartesian Manipulator for Pruning Apple Trees. Computers and Electronics in Agriculture. [In press].
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Kon, T. M., M. A. Schupp, H.E. Winzeler and J. R. Schupp. 2020. Screening thermal shock as an apple blossom thinning strategy. I. Stigmatic receptivity, pollen tube growth, and leaf injury in response to thermal shock temperature and timing. HortScience 55:625-631.
  • Type: Journal Articles Status: Published Year Published: 2020 Citation: Kon, T. M., J. R. Schupp, M. A. Schupp, and H.E. Winzeler. 2020. Screening thermal shock as an apple blossom thinning strategy. II. Pollen tube growth and spur leaf injury in response to thermal shock temperature and duration. HortScience 55:632-636.
  • Type: Other Status: Published Year Published: 2020 Citation: Choi, D., J. Schupp, T. Baugher, and L. He. Evaluation of effective canopy depths of apple trees for optimal machine sensing performance  Year2/2. PA Fruit News 100(1):39-41.
  • Type: Other Status: Published Year Published: 2020 Citation: He, L. (2020). Drip Irrigation and Sensor-Based Precision Irrigation. In the Penn State Tree Fruit Production Guide. (2020-2021), (pp. 426-430).
  • Type: Other Status: Published Year Published: 2020 Citation: He, L., & Weber, D. (2020). Updates on Soil Moisture-Based Irrigation for Orchards. Pennsylvania Fruit News.
  • Type: Other Status: Published Year Published: 2020 Citation: He, L., D. Choi, J. Schupp and T. Baugher. 2020. A sensor-based irrigation test system for apple orchards (Final report). PA Fruit News 100(1):24-26.
  • Type: Other Status: Published Year Published: 2020 Citation: He, L., J. Schupp, D. Choi and D. Weber. 2020. Branch and fruit accessibility for mechanical operations with various tree canopies (Year 1 report). PA Fruit News 100(1):22-24.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Huang, M., He, L., Jiang, X., Choi, D., & Pecchia, J. (2020). Hand-picking Dynamic Analysis for Robotic Agaricus Mushroom Harvesting. Paper No. 2000415. 2020 ASABE Annual International Meeting.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Jiang, X., He, L., & Tong, J. (2020). Investigation of Soil Wetting Pattern in Drip Irrigation using LoraWAN Technology. Paper No. 2000419. 2020 ASABE Annual International Meeting.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Mahmud, M. S., & He, L. (2020). Measuring Tree Canopy Density Using A Lidar-Guided System for Precision Spraying. Paper No. 2000554. 2020 ASABE Annual International Meeting.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Mirbod, O., Choi, D., Heinemann, P., & Marini, R. (2020). Towards image-based measurement of accurate apple size and yield using stereo vision cameras. 2020 ASABE Annual International Meeting, Paper No. 2001115, July 12- 15, 2020. (pp. 1-6).
  • Type: Other Status: Published Year Published: 2020 Citation: Schupp, J., H. E. Winzeler and M. Schupp. 2020. Blossom Thinning Pennsylvania Apples Using the Pollen Tube Growth Model. PA Fruit News 100 (1):46-47.
  • Type: Other Status: Published Year Published: 2020 Citation: Schupp, J., H. E. Winzeler and M. Schupp. 2020. Development of a High Density, Highly Mechanized, Pedestrian Peach System. PA Fruit News 100 (1): 43-44.
  • Type: Other Status: Published Year Published: 2020 Citation: Schupp, J., L. He, H. E. Winzeler, M. Schupp and M. Clowney. 2020. Improving orchard performance with terrain analysis using drone technology and Geographical Information Systems. PA Fruit News 100 (1):45-46.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Zahid, A., He, L., Choi, D., Schupp, J., & Heinemann, P. (2020). Collision free Path Planning of a Robotic Manipulator for Pruning Apple Trees. Paper No. 2000439. 2020 ASABE Annual International Meeting.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2020 Citation: Zhang, H., He, L., Di Gioia, F., Choi, D., & Heinemann, P. (2020). Internet of Things (IoT)-based Precision Irrigation with LoRaWAN Technology Applied to High Tunnel Vegetable Production. Paper No. 2000762. 2020 ASABE Annual International Meeting.


Progress 10/01/18 to 09/30/19

Outputs
Target Audience: Commercial tree fruit growers Commercial mushroom growers Specialty crop growers and machinery manufacturers Researchers, scholars, engineers, and extension agentsacross the country Changes/Problems: Nothing Reported What opportunities for training and professional development has the project provided? An intelligent spraying technology workshop was hosted for the growers/stakeholders, with introducing the core technologies and participating in the field demonstration. How have the results been disseminated to communities of interest? Trade journals, Extension newsletters, Extension website, Extension hands-on workshops, Presentations and poster displays at trade meetings. Soil moisture sensors were installed in four commercial orchards to assist growers in making a decision for irrigation. What do you plan to do during the next reporting period to accomplish the goals? The RootRobot for plant excavation and phenotyping will be improved and field-tested. A sensor-based irrigation system will be further tested in apple orchards. A robotic pruning end-effector with two rotational mechanisms will be improved for apple trees. A machine vision system for early season apple yield estimation will be improved and tested in apple orchards. Prototypes of automatic mushroom harvester will be improved and tested in varying degrees of automation. Frost protection system using UAVs and UGVs will be tested in a lab setting.

Impacts
What was accomplished under these goals? 1. Accomplishments The RootRobot for plant excavation and phenotyping was fabricated and components were lab tested. (3) (5) A sensor-based irrigation system using soil-moisture, evapotranspiration, and plant stress was installed and tested in apple orchards. (1) (2) A robotic pruning end-effector with two rotational mechanism was developed for apple trees. (1) (2) (3) (4) A machine vision system for early season apple yield estimation was developed. (1) (2) (3) (4) Prototypes of automatic mushroom harvester were developed and tested. The system consisted of a vision system for maturity and size evaluation and an end-effector for pulling crops from production bed. (1) (2) (5) The relationship between canopy depths and machine sensing performances in a Tall Spindle apple orchard system was investigated. (1) (2) (4) 2. Impact Utilizing genetics and phenotyping, the project will result in maize plants that can access nutrients and water from deeper soil levels. The result will be large scale plantings of corn that will be more efficient in growth, increasing yields without using more land, and decreasing water and nutrient inputs. (3) (5) Soil moisture sensors were installed in 4 commercial orchards, and the irrigation events were suggested to the growers with the information from sensors. (1) (2) The developed pruning end-effector could be integrated with a three linear directional robotic manipulator to cut branch at any orientation with small spatial requirement.(1) (2) (3) (4) Utilizing the early season yield estimation can improve the quality of harvested fruit and increase crop yields in apple orchards by adopting precision agriculture technologies. (1) (2) (3) (4) A robotic mushroom harvester can save labors from intensive manual harvesting and potentially save time to train workers for selective harvesting. (1) (2) (5)

Publications

  • Type: Journal Articles Status: Accepted Year Published: 2019 Citation: Caliskan-Aydogan, O., H. Yi, J.R. Schupp, D. Choi, P. H. Heinemann, V. M. Puri. 2019. Changes in thermal properties of 'Gala' apple during the growing season. Trans. ASABE PRS-13417-2019 (in press).
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Marini, R. P., J. R. Schupp, T. A. Baugher and R. Crassweller. 2019. Relationships between fruit weight and diameter at 60 days after bloom and at harvest for three apple cultivars for three years in three Pennsylvania orchards. HortScience 54:86-91.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Marini, R. P., J. R. Schupp, T. A. Baugher and R. Crassweller. 2019. Sampling apple trees to accurately estimate mean fruit weight and fruit size distribution. HortScience 54:1017-1022.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Schupp, J. R., H. E. Winzeler and M. A. Schupp. 2019. Stub length and stub angle did not influence renewal shoot number or branch angle of tall spindle Gala'/ M.9 apple trees. HortTechnology29:46-49.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Shi, X., Choi, D., Heinemann, P., Lynch, J., and Hanlon, M. 2018. RootRobot: A field-based platform for maize root system architecture phenotyping. ASABE Paper No. 1900806. American Society of Agricultural and Biological Engineers. 6 pp.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: He, L., Zeng, L., and Choi, D. 2019. Investigation of sensor-based irrigation systems for apple orchards. NABEC Paper No. 19-013. American Society of Agricultural and Biological Engineers. ASABE: St. Joseph, MI.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Zahid, A., He, L. and Zeng, L. 2019. Development of a Robotic End Effector for Apple Tree Pruning. ASABE Paper No. 1900964. American Society of Agricultural and Biological Engineers. ASABE: St. Joseph, MI.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: He, L., Zhang, X., Ye, Y., Karkee, M., and Zhang, Q. 2019. Effect of shaking location and duration on mechanical harvesting of fresh market apples. Applied Engineering in Agriculture, 35(2), 175-183.
  • Type: Journal Articles Status: Published Year Published: 2019 Citation: Feng, J., Zeng, L., and He, L. 2019. Apple fruit recognition algorithm based on multi-spectral dynamic image analysis. Sensors, 19(4), p. 949.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Lee, C., Choi, D., Pecchia, J., He, L., & Heinemann, P. 2019. Development of A Mushroom Harvesting Assistance System using Computer Vision. 2019 ASABE Annual International Meeting, Paper No. 190050, page 1-5, July 7  July 10, 2019.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Jarvinen, T., Choi, D., Heinemann, P., Schupp, J., & Baugher, T. A. 2019. Tree trunk position estimation for accurate fruit counts in apple yield mapping. 2019 ASABE Annual International Meeting, Paper No. 1900918, page 1-7, July 7  July 10, 2019.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Jarvinen, T., Choi, D., Heinemann, P., & Baugher, T. A. 2018. Multiple object tracking-by-detection for apple fruit counting on a tree canopy. 2018 ASABE Annual International Meeting, Paper No. 1801193, page 1-8, July 29  Aug 1, 2018.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2018 Citation: Choi, D., & Jarvinen, T. 2018. "A video processing strategy using camera movement estimation for apple yield forecasting." Proceedings of the 9th International Symposium on Machinery and Mechatronics for Agriculture and Biosystems Engineering, page 1-5, Jeju, South Korea, May 28-30, 2018.
  • Type: Journal Articles Status: Published Year Published: 2018 Citation: Wang, C., Lee, W. S., Zou, X., Choi, D., Gan, H., & Diamond, J. 2018. Detection and counting of immature green citrus fruit based on the Local Binary Patterns (LBP) feature using illumination-normalized images. Precision Agriculture. ISBN/ISSN #/Case #/DOI #: https://doi.org/10.1007/s11119-018-9574-5. Online publication.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Jarvinen, T., D. Choi, P. Heinemann, J. Schupp and T. Baugher. Early season crop load estimation of apples using computer vision. Mid-Atlantic Fruit and Vegetable Conference, Hershey, PA. 29-31 Jan 2019, (poster).
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Schupp, J. 2019. Future directions of peach tree training systems. Mid-Atlantic Fruit and Vegetable Conference, Hershey, PA. 31 Jan 2019.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Schupp, J., R. Wiepz, M. Schupp and H. E. Winzeler. 2019. Pruning studies to manage crop load in Pennsylvania. In-Depth Fruit School on precision crop load management and plant growth regulator use in apples, Syracuse, NY. 26 Mar 2019.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Wiepz, R., J. Schupp, M. Schupp and E. Winzeler. 2019. Research and applications of artificial spur extinction in Pennsylvania orchards. Mid-Atlantic Fruit and Vegetable Conference, Hershey, PA. 30 Jan 2019.
  • Type: Conference Papers and Presentations Status: Published Year Published: 2019 Citation: Wiepz, R., J. Schupp, M. Schupp and E. Winzeler. 2019. Research and applications of artificial spur extinction in Pennsylvania orchards. Mid-Atlantic Fruit and Vegetable Conference, Hershey, PA. 29-31 Jan 2019. (poster)
  • Type: Other Status: Published Year Published: 2019 Citation: Choi, D., J. Schupp, T. Baugher and L. He. 2019. Evaluation of effective canopy depths of apple trees for optimal machine sensing performance. PA Fruit News 99(1):52-54.
  • Type: Other Status: Published Year Published: 2019 Citation: He, L., D. Choi, J. Schupp and T. Baugher. A sensor-based irrigation test system for apple orchards. PA Fruit News 99(1):43-45.
  • Type: Other Status: Published Year Published: 2019 Citation: Schupp, J., H. E. Winzeler and Melanie Schupp. 2019. Development of a High Density, Highly Mechanized, Pedestrian Peach System. PA Fruit News 99(1):24-25.
  • Type: Other Status: Published Year Published: 2019 Citation: Wiepz, R., J. Schupp, M. Schupp, and H. E. Winzeler.2019. Research and Applications of Artificial Spur Extinction in Pennsylvania Orchards. PA Fruit News 99(1):22-23.