2025 Final Report

2025 Final Report (to NIFA) of the Precision Crop Load Management for Apples SCRI project

Precision Crop Load Management of Apples: USDA-NIFA-SCRI SREP 2020-51181-32197. 09/30/2020 – 08/31/2025.

Accomplishments over the 5 years of the project
Through this project we developed strategies and technologies to manage apple crop load precisely to maximize crop value and ensure return bloom. The effort involved physiological field and lab studies, field validation of digital tools for assessing and managing crop load, development

Accomplishments Objective 1: Develop and disseminate user-friendly computer-based models and comprehensive crop load management strategies to achieve optimal crop load and maximize crop value.

We conducted coordinated physiology trials on crop load management at 4 locations in the USA to establish the physiological basis for determining the optimum number of apples per tree. We conducted field studies to develop improvements to the pollen tube growth model to guide blossom chemical thinning, the carbohydrate model to guide chemical fruitlet thinning and the fruit growth rate model to assess the effectiveness of chemical thinning sprays. We developed a universal pollen tube growth model by applied modeling and a new model to predict thinning efficacy. We developed a fruit set prediction model using a portable Vis/NIR spectrometer with ~80% accuracy in predicting chemical thinner responses when measured 3 days after chemical thinner application. We developed improved blossom-thinning strategies including ACC, lime sulfur at multiple rates, ammonium thiosulfate, potassium bicarbonate, Regalia, and mineral oil.

Significant discoveries: Objective 1

Determined optimal flower bud numbers for Honeycrisp and Gala at 4 location in the USA and over multiple years.
Have accurately quantified the variability of bud, blossom, fruitlet, and fruit number at orchard and tree-level using estimates from digital technologies for researchers and growers
Partnered with 5 digital ag companies to capture images of apple trees at various stages during the season and then process the images to develop task maps to guide human workers and to guide variable rate sprayers.
Developed an improved, timesaving, accurate predictive fruit set model was developed and tested alongside the current model and disseminated to stakeholders.
Developed bud pruning indices for Honeycrisp and Gala and provided to stakeholders, reduced thinning pressure by mitigating under-thinning.
Using a NIR/Vis spectrometer, critical wavelengths and fruit characteristics to predict fruitlet abscission were identified.
For most apple cultivars, 800-900 Growing Degree Hours (Base 4 ºC) are required for pollen tubes to grow from the stigma to the base of the style
Calculating Growing Degree Hours provides a simple method for tracking pollen tube growth rates. A logistics regression model fit the dataset with a 90% prediction accuracy
Several new pollen tube models have been programmed into the Network for Environment and Weather Applications mesonet platform for field testing with additional apple cultivars.
Demonstrated that both the PTGM approach and a 20% open-bloom followed by a 48-hour reapplication method reliably reduced fruit set and improved fruit size.
Found that with ‘Gala,’ double applications of ACC during bloom reduced crop density and whereas the ACC + lime sulfur combination was consistently ineffective. In ‘Red Delicious,’ lime sulfur—particularly at 2–5%—reliably reduced fruit set across years. ‘Honeycrisp’ responded only to Regalia and ammonium thiosulfate + oil, while none of the tested materials produced significant blossom thinning in ‘Cripps Pink.’
Fruit set of WA-38 (Cosmic Crisp) was affected by pollen source with differences between ‘Indian Summer’, ‘Snowdrift’, and ‘Mt. Blanc’s
Bee-exclusion netting significantly reduced fruit set and increased average fruit mass by 36 g compared with un-netted controls, demonstrating effective non-chemical crop load regulation.

Accomplishments Objectives 2 and 3: Develop, demonstrate, and deploy machine vision and robotic tools to accurately count reproductive structures during dormancy, bud break, bloom, and at the fruitlet stage and georeference the information to facilitate precision management at the tree level; Develop autonomous vehicles and end effectors designed for crop load management that can precisely measure and adjust crop load during dormancy, bud break, bloom, and fruitlet stage.

The engineering group developed 3D computer vision for apple tree reconstruction and characterization to guide pruning. We developed a machine vision system and different AI models including branch, flower buds, and fruitlets detection and localization. The machine vision system was then integrated with various robotic platforms for automation. The practices for robotic crop load management were investigated from robotic pruning to bud thinning until green fruit thinning, including robotic cutting mechanisms for branch pruning, flower bud detection and robotic bud removal system, green fruitlets detection and removal, and integrated chemical thinning with considering crop load on individual tree and precision spraying.

Significant discoveries Objectives 2 and 3

Developed quality 3D point clouds of apple trees which were used in with geometry-based analysis methods to produce usable images of tree 3D structure.
Showed that AI-based point cloud completion is a viable solution to enhancing data quality of point clouds that are collected by even high-end instrumentation such as terrestrial laser scanners (TLS).
The real-to-sim (Real2Sim) approach leverages generative AI methods with procedural plant generation to solve data scarcity issues in domain-specific applications such as agriculture to significantly improve learning-based analytical tool performance.
The Real2Sim closed-loop framework offers the opportunity to achieve realistic “digital twin” that can be used to facilitate the development of AI-based analytical tools for tree characterization and understanding and of agricultural robots for operation.
Among the bud detection models tested, the highest precision of 86% was achieved with these tested models, also a machine vision-based branch diameter measurement indicated high accuracy by comparing to manual caliper measurement. Bud density was then calculated at branch level and the decisions for bud thinning were identified based on the bud density, bud distribution, and branch diameter.
•A machine vision system was developed for green fruit detection, with the Mask R-CNN model achieving AP scores of 83.4% and 38.9% on green fruits and stems, respectively. A green fruit removal end-effector was then developed and integrated with a 6 DoF robotic arm to conduct green fruit thinning. With the optimized path planning algorithm, the integrated system achieved 87.5% of the targeted fruit to be encapsulated and removed.
•An integrated machine vision system and precision chemical thinning system was evaluated in both research and commercial orchards, the results showed that precision chemical thinning based on detected crop density saved 18% – 45% chemicals while maintaining similar thinning performance and fruit production at harvest.

Accomplishments Objective 4: Evaluate and demonstrate the economic and sociological impacts of adopting precision crop load management for sustainable apple production.

We carried out a choice experiment survey for data collection to investigate apple growers’ preferences for precision crop load management technologies. Carried out data analysis to model the relationship between optimal crop load and crop profitability in Honeycrisp apples ensuring good return bloom. Currently we are writing a paper communicating results regarding the optimal crop load for Honeycrisp apples.

Significant discoveries Objective 4

Apple crop-load technology developers should prioritize crop-load-counting technology with local service support. This technological solution should not require certified training. Growers value services and are user-friendly.
The relationship between crop load and profit is curvilinear and concave, indicating a maximum profit range exists. Implication: New York Honey Crisp growers should target a 7.47-10.00 fruit/cm2 TCSA average fruit load for “Honeycrisp. WA, MI, and NC growers should aim for 8.1 -11.1 fruit/cm2 TCSA, respectively.
From the linear coefficient results presented above, we observe that the marginal impact of crop load treatment will vary between $4.11 and $5.06 per unit of fruit per TCSA.

How Have the Results Been Disseminated to Communities of Interest?
We have disseminated findings from this project regularly to our advisory committee with yearly reporting meetings. We have also extended our results to the wider apple grower community with articles written for apple growers, fruit grower schools, field days, on-farm testing as well as through the PACMAN website (https://pacman.extension.org/). Specific extension efforts for each cooperating location are:

NY – We organized a meeting in February 2022, of the most promising digital technologies from around the world and enlisted them to participate in ground-truth validation trials held at growers farm in 2022, 2023, 2024 and 2025. In the summer of each of the 5 years we hosted a summer field day featuring 2–3 of the following technology companies: Moog (Buffalo, NY), Pometa (MN), Green Atlas (Australia), Vivid (Ontario, Canada), Outfield (England), Munckhof/Aurea Imaging (Netherlands), and LaGasse (NY). Attendance at each summer tour stop averaged approximately 150–200 participants. We published 11 articles for growers, made 12 presentations for growers at winter fruit schools. In the spring of each year, we organized thinning meetings for NY growers in 3 regions of the state (Western NY, the Champlain Valley and Hudson Valley). Precision thinning was discussed at each meeting. We delivered crop load management information through extension channels, including 15 newsletter articles, 60 Fruit Facts recommendations (covering pruning and chemical thinning at bloom, petal fall, 10–12 mm, and 16–18 mm stages), five email blasts, 30 Zoom meetings, and over 30 recorded sessions posted on the CCE LOF YouTube channel.
The findings of this project were extended to scientific audiences through 22 peer-reviewed journal articles, 10 conference proceedings, and 2 arXiv articles. Additionally, we have submitted 5 more manuscripts under review by peer-reviewed journals. We also made 8 presentations at scientific conferences.

MI – An Advisory Committee (AC) consisting of 7 commercial growers from major apple growing regions in the state was formed in March 2025. A meeting was convened on August 29 to develop a list of priorities and a technology roadmap with project PIs, collaborators, commercial growers, and industry partners. Field days and grower/industry/research visits were conducted by project personnel featuring precision crop load management technologies with over 200 attendees. Field research & demonstration trials were established featuring precision crop load management protocols. This research evaluated the AI-driven tool (AFS method) to measure fruit growth rates and yield estimates compared to traditional methods of measuring fruit growth rates and crop load by hand. Several on-farm research precision crop load management trials were completed resulting in actionable chemical thinning recommendations and yield estimates. Results from this project were disseminated in a total of 7 annual regional winter horticulture meetings throughout Michigan (Hart, Benton Harbor, Traverse City) and two in Ontario Canada (Ontario Fruit and Vegetable Convention). Two oral presentations and two poster presentations were made at the Michigan State Horticulture Society’s winter annual conference (GLEXPO) held in Grand Rapids, Michigan. Results were also presented each spring (4 meetings in total) during grower breakfast meetings (Sparta) in advance of spring bloom. Results were also presented in summer field day tours in NY and MI. Two academic presentations were given at the annual conference of the American Society for Horticultural Science and four symposia talks at congresses of the International Society for Horticultural Science (3 as keynote presentations). Trade journal/grower publications include one article in the Fruit Quarterly journal and another in the Good Fruit Grower. Three ACTA Horticulturae proceedings articles and one peer-reviewed (HortScience) articles were also published. One additional peer review article is under review (Scientia Horticulturae). A podcast (Emily Lavely, MSU) was also developed as well as communication with growers (in-person and via email; Lavely and Einhorn).

NC – In NC, 24 presentations related to this project were delivered to stakeholders and 24 presentations at scientific conferences (48 presentations total). NC led publication of 10 scientific products and co-authored an additional 11 products during the course of the project.

WA – We organized the six statewide field days from 2022-2025. We also participated in regional field days (growers and industry partners as targets) held during the 2024 growing season, and a discussion on crop load and visual technology was promoted. We made presentations for growers at the WTFRC field evaluation of digital vision systems at WA State Tree Fruit Association Annual Meeting (Yakima, WA – Dec 2023) and at the International Fruit Tree Association Annual Meeting technology workshop prior to meetings featuring multiple tech vendors (Yakima, WA – Feb 2024). We wrote an article in WSU Fruit Matters newsletter on digital vision system testing (May 2025) We also made numerous presentations of PACMan project at winter industry meetings and events throughout WA

MA – Pacman.extension.org is a permanent Extension deliverable with 49 Published “Posts” and 8 Published “Pages” with 9,000 total “Views” and 4,100 “Visitors” over the course of this Project (2021-2025) 28 YouTube videos from Developers Conference for Precision Crop Load Management of Apples, Show and Tell Progress Towards Precision Apple Cropload MANagement, and Dr. Terence Robinson linked on PACMAN website.

PA – We have published eight journal articles (two more under review) and three conference proceedings based on the outcomes from this project. Meanwhile, the PIs and the graduate students presented this project at various conferences and professional meetings (nine oral presentations and two poster presentations), which attracted lots of interest from the society. We have actively participated in some grower meetings and many other extension events to present the results from this project to the grower community. Among PIs and graduate students, we gave a total of six talks during Annual Mid-Atlantic Fruit and Vegetable Conventions, Penn State Winter Fruit Schools and more. Also we have demonstrated the developed robotic crop load management systems to growers and other stakeholders four times during grower field days and on-site visits. We hosted the 2023 Penn State Precision Agriculture Field Day, which featured the project and demonstrated the prototypes and invited a commercial crop monitoring company. We also presented and demonstrated the system to general public during different events, such as Penn State Ag Progress Day and more.

VA – In total, the project generated approximately 25 winter fruit school presentations, around 16 orchard or field-day meetings, 6 invited regional or national conference presentations, and at least 24 project-related blog articles, alongside frequent dissemination of carbohydrate-model and PTGM updates through the Tree Fruit Horticulture Updates blog and the Virginia Tech Tree Fruit Extension Facebook page. Altogether, these outreach efforts reached well over 1,000 apple growers, Extension agents, and agricultural consultants, ensuring broad and sustained communication of the project’s findings and recommendations throughout the entire funding period. A statewide survey in VA in 2022 showed strong extension impacts, with more than 67% of growers rating the program as “beneficial” or “very beneficial” for improving their knowledge of fruit thinning and return bloom, and over 56% reporting similar benefits from using the MaluSim carbohydrate model; in contrast, only 29% viewed the PTGM as advantageous—with 27% rating it “not beneficial”.

Products during the 5th year.

Scientific Publications

Gonzalez Nieto, L., Einhorn, T., Kon, T. M., Musacchi, S., Serra, S., & Robinson, T. L. 2026. Impact of Crop Load on Fruit Size, Color and Return Bloom of ‘Honeycrisp’ Apple across Four US Locations. HortScience, 61(1), 1–13. https://doi.org/10.21273/HORTSCI19058-25

Hillmann, L., Nieto, Kon, T., Larson, J., Musacchi, S., Robinson, T., Serra, S. and Einhorn, T. 2025. An apple fruit set prediction model from distributions of fruitlet mass accurately estimates abscission in four disparate regions of the United States. HortScience, 60(11), 2007-2017. DOI: https://doi.org/10.21273/HORTSCI18854-25

Hillman, L., Sharkey, T., Robinson, T., Kon, T., Musacchi, S., Serra, S., Nieto, L., Larson, J. Einhorn, T. Accepted 2025. Physiology of apple fruit set and abscission: Effect of flower position and growth on fruitlet carbohydrate status and predicted fruit set. Acta Horticulturae

Larson, J.E., T. Zuber, and T.M. Kon. 2025. Apple fruitlet physiological characteristics and their influence on diffuse Vis/NIR reflectance spectroscopy. Annals of Botany. 136:733-744. DOI: https://doi.org/10.1093/aob/mcaf124

Guerra, M., Gómez, M.I., Gonzalez Nieto, L., Einhorn, T., Kon, T., Musacchi, S., Serra, S., and Robinson, T.L. 2025. The economic impact of adopting precision crop load management in apple production. Scientia Hort. (submitted)

Guerra, M., Gómez, M.I., Schmidt, T., Clements, J., Lewis, K., Kahlke, C.J., Miranda Sazo, M., Robinson, T.L. 2025. Apple Growers’ Crop-Load Technology Preferences Scientia Hort. (submitted)

Clements, J. 2024. Precision Apple Cropload MANagement (PACMAN): Optimizing Profitability by Optimizing Apple Crop Load. New England Vegetable & Fruit Conference, Manchester, NH. December, 2024. https://newenglandvfc.org/wp-content/uploads/2025/01/Tree-Fruit-Tech_PACMANforNEVFC1224-Jon-Clements.pptx.pdf

Fallahi, E, Kiester, MJ, Fallahi, B, Cheng L. 2024. Effects of tree spacing and branch configuration on production, fruit quality, and leaf minerals of ‘Aztec Fuji’ apple trees in a Tatura trellis system over five years. Journal of the American Society for Horticultural Science https://doi.org/10.21273/JASHS05444-24

Pawikhum, K., Yang, Y., He, L., & Heinemann, P. (2025). Development of a machine vision system for apple bud thinning in precision crop load management. Computers and Electronics in Agriculture, 236, 110479

Pawikhum, K., He, L., Heinemann, P., & Bock, R.G. (2025). Design of end-effectors for thinning apple in the green fruit stage. Journal of the ASABE, 68 (3), 465-476.

Arthur, L., Mahnan, S., He, L., Hussain, M., Heinemann, P. & Brunharo, C. (2025). YOLOv7-CBAM and DeepSORT with pixel grid analysis for Real-Time weed localization and Intra-Row density estimation in apple orchards. Computers and Electronics in Agriculture, 239, p.111071.

Pawikhum, K., He, L., Heinemann, P., & Kumar, S. (2025). Decision support framework for robotic apple bud thinning based on bud density and spatial distribution. Biosystems Engineering (under review).

Pawikhum, K., He, L., & Heinemann, P. (2025). Machine vision-guided multi-DoF cutting end-effector for early apple crop load management. Journal of the ASABE (under review).

Arthur, L., Mahnan, S., He, L., Hussain, M., Heinemann, P., & Brunharo, C. (2025) Real-time Weed Species Localization and Tracking for IntraRow Weed Density Estimation in Apple Orchards. ASABE Annual Meeting Paper No. 2500681. ASABE, St. Joseph, MI.

T. Qiu, A. Zoubi, N. Spine, L. Cheng and Y. Jiang, “(Real2Sim)−1: 3D Branch Point Cloud Completion for Robotic Pruning in Apple Orchards,” 2024 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), Abu Dhabi, United Arab Emirates, 2024, pp. 23-30, https://doi.org10.1109/IROS58592.2024.10803058.

T. Qiu, R. Du, N. Spine, L. Cheng and Y. Jiang, “Joint 3D Point Cloud Segmentation Using Real-Sim Loop: From Panels to Trees and Branches,” 2025 IEEE International Conference on Robotics and Automation (ICRA), Atlanta, GA, USA, 2025, pp. 7131-7138, https://doi.org/10.1109/ICRA55743.2025.11128189.

Jiang, Y., Qiu, T., Robinson, T., Cheng, L., Kuehn, K., & Xu, K. (2024). 3D Characterization of Apple Tree Architecture for Precision Pruning and Crop Load Management. Fruit Quarterly, Fall 2024.

Jiang, Y., Lin, Y., Bates, O., Lawrence, T. B., Bates, T., & Robinson, T. (2025). Affordable RTK GPS Solutions to Precision Management of Perennial Crops. Fruit Quarterly, 32(3): 28-31.

González Nieto, L., P. Francescatto and T.L. Robinson 2025. Thinning efficacy of the new chemical thinner Brevis in New York state. Fruit Quarterly 32(2): 3-7.

Lawrence, B.T., Y. Jiang, and T.L. Robinson. 2025. Technologies in the box for precision orchard management: 2-Year update on crop load monitoring and mapping. Fruit Quarterly 32(2): 28-32.

Basedow, M., A. Galimberti, G. Peck, and T. Robinson. 2024. Assessing the pollen tube growth model in Northern New York apple orchards. Fruit Quarterly 32(2):23–29.

Kon, T. 2025. Apple thinning update: 04.18.25. Southern Appalachian Apples. https://apples.ces.ncsu.edu/2025/04/apple-thinning-update-04-18-25/

Sherif, S.M. (2025). Thinning Update: Week of May 6 – Winchester and Central Virginia. https://tree-fruit-horticulture.vaes.vt.edu/2025/05/06/thinning-update-week-of-may-6-winchester-and-central-virginia/

Sherif, S.M. (2025). Ideal Conditions for Apple Fruit Thinning This Week: Model Results for Winchester and Central Virginia. https://tree-fruit-horticulture.vaes.vt.edu/2025/04/28/ideal-conditions-for-apple-fruit-thinning-this-week-model-results-for-winchester-and-central-virginia/

Sherif, S.M. (2025). Apple Carbohydrate Thinning Model Outputs – Central Virginia – Roseland, VA. https://tree-fruit-horticulture.vaes.vt.edu/2025/04/24/apple-carbohydrate-thinning-model-outputs-central-virginia-roseland-va/

Abstracts of Presentations at Scientific Meetings 2025

González Nieto, L., T.L. Robinson, E. Torres, G. Àvila, C. Gonzalez Noguer, L. Asin, J. Bonany and J. Carbó. 2025. Review of Crop load Management in Apples with PGR’s under American Northeast and Spain conditions-Part 1, Flowering. International symposium on Plant Growth Regulators in Chicago IL. (Abstr.)

Hillmann, L, T. Sharkey, T. Einhorn, T. Robinson, T. Kon, S. Musacchi, S. Serra, L. Gonzalez Nieto, J. Larson. 2025. Physiology of apple fruit set and abscission: Effect of flower position and growth on fruitlet carbohydrate status and predicted fruit set. International symposium on Orchard Systems, Rootstocks and Physiology in Napier NZ. (Abstr.)

Larson, J., T. Kon T. Robinson, T. Einhorn, L. Gonzalez Nieto, L. Hillman. 2025. Development of a reflectance spectroscopy model to predict chemical thinner efficacy in the Eastern United States. International symposium on Orchard Systems, Rootstocks and Physiology in Napier NZ. (Abstr.)

Lawrence, B.T., Yu, J. and Robinson, T.L. 2025. Improving apple crop load management using digital tools: matching data collection with accurate treatment application. HortScience 60(9) Supplement p312 (Abstr.) July 29, 2025

Kon, T.M., J.E. Larson, C.D. Clavet. 2025. Perspectives on apple crop load management. XV International Symposium on Plant Bioregulators in Fruit Production, Chicago, IL *Keynote Speaker – Invited International Presentation*

Larson, J. and T. Kon. 2025. Induction of a carbohydrate deficit to improve efficacy of a post-bloom thinner application. XV International Symposium on Plant Bioregulators in Fruit Production, Chicago, IL

Larson, J., T. Kon, T. Robinson, T. Einhorn, L. Gonzalez Nieto, L. Hillmann. 2025. Development of a reflectance spectroscopy model to predict chemical thinner efficacy in the eastern United States. XIII International Symposium on Integrating Canopy, Rootstock and Environmental Physiology in Orchard Systems, Napier, New Zealand.

Hillmann, L., T. Sharkey, T. Einhorn, T. Kon, S. Musacchi, S. Serra, L. Gonzalez Nieto, T. Robinson. 2025. Physiology of apple fruit set and abscission: Effect of flower position and growth on fruitlet carbohydrate status and predicted fruit set. XIII International Symposium on Integrating Canopy, Rootstock and Environmental Physiology in Orchard Systems, Napier, New Zealand (Invited keynote).

Kon, T. and C. Clavet. 2025. Floral bud inhibition of mature ‘Fuji’ with Arrange™. Northeast Plant Growth Regulator Working Group, Wilkes Barre, PA

Kon, T. and C. Clavet. 2025. Is there a relationship between Accede® spray volume and apple chemical thinner responses? Northeast Plant Growth Regulator Working Group, Wilkes Barre, PA

Kon, T. and C. Clavet. 2025. Comparison of 6-BA thinning products and programs on ‘Gala’ and ‘MAIA-1’. Northeast Plant Growth Regulator Working Group, Wilkes Barre, PA

Serra S., Md Jebu Mia, and Musacchi S. Effect of Pollinators Exclusion System on ‘WA 38’ Fruit Set In Washington State. Poster ASHS Annual Conference of the American Society for Horticultural Science (ASHS) (Orlando, FL) on August 4, 2023.

Musacchi S. and Serra S., (oral presentation), “Bee exclusion as a method to increase ‘WA 38’ bloom return in Washington State”, presented by Musacchi S. at EHC 2024 symposium S03 – Fruit production systems for sustainable and resilient development, at the session “Pre-harvest factors affecting post-harvest crop performance”, on 5/13/2024 in Bucharest, Romania

He, L., Pawikhum, K., Kang, C., Dou, H., Mu, X., Hussain, M., & Sahu, R. (October, 2025). Robotics and artificial intelligence for tree fruit crop load management. 2025 FIRA USA. Woodland, CA. (Poster)

Pawikhum, K., He, L., & Heinemann, P. (July 2025). Developing an integrated solution for apple bud thinning with computer vision and bud removal mechanisms. 2025 ASABE Annual International Meeting. Toronto, Canada.

Pawikhum, K., He, L., & Heinemann, P. (August 2025). Development of a machine vision system for apple bud thinning in robotic orchard management. 2025 NABEC Meeting. Ithaca, NY.

Kang, C. He, L. & Kumar, S. (August 2025). Integrated approach to green fruit thinning: combining computer vision and precision sprayers for effective chemical thinning. The 8th IFAC AgriControl Conference. Davis, CA.

Other Products
Yu Jiang 2025. A new software package for 3D reconstruction of apple trees in the orchard.

Yu Jaing 2025. A new robotic platform with multi-view imaging for high throughput imaging of apple trees in the orchard.

Oral presentations to growers 2025
Lawrence, B., Miranda Sazo, M. Yu., J. and Robinson, T.L. 2025. Accuracy of two variable rate sprayers for single-tree treatment of chemical thinners. WNY Fruit Growers Conference Feb 3, 2025, 200 people 1 hour.

Robinson, T.L. 2025. Progress on Precision Crop Load Management of Apples WNY Fruit Growers Conference Feb 4, 2025, 200 people 1 hour.

Robinson, T.L. 2025. Honeycrisp Crop Load Management: 1. Pruning 2. Chemical Bloom and Fruitlet Thinning 3. Hand Thinning. 2025 Cornell In-Depth Fruit School Workshop “Honeycrisp & Other High Value Apple Cultivars” March 19, 2025. 150 people. 45 minutes

Robinson, T.L. 2025. Crop Load Management of NY1 (SnapDragon). 2025 Cornell In-Depth Fruit School Workshop “Honeycrisp & Other High Value Apple Cultivars” March 20, 2025. 150 people. 45 minutes

Robinson, T.L. 2025. Crop Load Management of Evercrisp. 2025 Cornell In-Depth Fruit School Workshop “Honeycrisp & Other High Value Apple Cultivars” March 20, 2025. 150 people. 45 minutes

Robinson, T.L. 2024. Thinning recommendations at bloom, petal fall, 12mm and 18mm for 2025. Webinar series for NY State apple growers. 6 webinars in May and June 2025. 500 people 45 minutes

Kon, T. and C. Clavet. 14 Aug 2025. Apple crop load and canopy management. 2025 Field Day, Mountain Horticultural Crops Research and Extension Center, Mills River, NC

Kon, T. and C. Clavet. 13 Feb 2025. Horticultural Update: Is there a relationship between Accede spray application volume and apple chemical thinner responses? Northwest Winter Tree Fruit Meeting Wilkes County Agricultural Center, Wilkesboro, NC

Serra S. (oral presentation), “Apple pollination, fruit set, and effects of environmental conditions during bloom”, at IFTA session “Precision Crop Load Management: Best Practices and Outlook” on February 12, 2024, Yakima, WA.

Clements, J. 2024. Precision Apple Cropload MANagement (PACMAN): Optimizing Profitability by Optimizing Apple Crop Load. New England Vegetable & Fruit Conference, Manchester, NH. December 2024.

Clements, J. 2025. Presentation on Precision Apple Cropload MANagement using Outfield and Vivid Machines to map bloom and fruit variability. Mississippi Valley Fruit Grower Meeting, Winona, MN. June 2025.

Clements, J. 2025. ‘Fun’ with Outfield year 3 (continued from years 1 and 2). Great Lakes Fruit Workers 2025 Meeting, Grand Rapids, MI. November 2025. https://pacman.extension.org/2025/11/24/fun-with-outfield-year-3/

Cheng, L. 2025. Honeycrisp Nutrient Management. Annual Western New York Fruit Growers Summer Tour, Wayne Counry, NY, July 31, 2025.

Cheng, L. 2025. Nutrient management for Honeycrisp trees on Geneva rootstocks. Agromillora and Quality Plant Nursery Conference. March 18, 2025.

Cheng, L. 2025. Honeycrisp nutrition and mitigation of bitter pit. Mid-Atlantic Fruit and Vegetable Convention. Hershey, PA, January 28, 2025.

Lavely E. Michigan talks and results were presented during 2 thinning meetings for commercial growers in Oceana and Kent Counties as well as Ridgefest in Kent County (2023) and the Farming for the Future field day in Oceana County (2025). These events reached over 250 attendees.

Lavely, E. Project updates were posted on the MSU Extension Facebook page for Emily Lavely and shared with growers through the MSU tree fruit grower listserv which reaches over 400 people.

He, L. (January 2025). How an agricultural technology innovation hub will benefit our growers. 2025 Mid-Atlantic Fruit and Vegetable Convention. Hershey, PA.

He, L. & Kumar S. (June 2025). Integrated precision chemical thinning system for crop load management. 2025 Penn State Fruit Research and Extension Center Grower Field Day. Biglerville, PA.

Pawikhum, K. (June 2025). Machine vision and robotics for apple flower bud thinning. 2025 Penn State Fruit Research and Extension Center Grower Field Day. Biglerville, PA.

Sherif, S.M. (2025). Reflections on Seven Years of Tree Fruit Horticulture Research at Virginia Tech: Lessons Learned and Future Horizons. Presented 4X: Winchester Regional Commercial Tree Fruit School, Rappahannock-Madison Area Fruit School, Central Virginia Commercial Tree Fruit Production School, and Carroll – Patrick Fruit Growers School.

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