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 THE FRUITLET SIZE DISTRIBUTION (FSD) MODEL: A HOW-TO GUIDE (2024 update)

 LAURA HILLMANN AND TODD EINHORN, Michigan State University einhornt@msu.edu 

 Fruit set prediction models aim to produce timely estimates of fruitlet abscission after thinner applications to guide precision crop load management. The time to generate a prediction after an application is important to facilitate grower decisions to re-apply thinners while they are still efficacious, avoiding expensive hand thinning operations. The fruitlet growth rate (FGR) model, developed by Dr. Duane Greene, is a powerful tool that can accurately predict the percentage of fruitlets that will set in an orchard. Although a Excel data template and App are available to run the FGR model via computer and smartphone, respectively, adoption has been limited by the measurement-intensive procedure. A new approach, termed the ‘Fruitlet Size Distribution (FSD) Model’, described herein, was developed to produce predictions of apple fruit set comparable to the FGR model but achievable with less time investment. The principle underlying both models is the same: the relative growth rate or size of a fruitlet is compared to the most rapidly growing or largest fruitlet within the sample date to determine if it will abscise. Most predictions can be made within 8 days from thinner applications, though the duration of time depends on climatic, biological and horticultural factors. To optimize the FSD model we suggest beginning the model three days after the average fruitlet diameter of the orchard is 6 mm. Thus, the model partners well with thinning applications between bloom and 6 mm. For example, if, a prediction can be achieved by 8 days, assuming an average growth of ~0.8 mm per day, then fruitlets will be ~ 12 mm if another application is needed; 12 mm fruitlets are very sensitive to many thinning chemistries. 

HOW-TO-GUIDE (2024 update includes new procedure to flag 120 flower cluster between pink and full bloom)

XLSM file for FSD Predict here. Please read HOW-TO-GUIDE above first. Note the XLSM file will need to be downloaded and run locally on your computer. (Click the Download icon in upper right after viewing FS Predict in Dropbox link above.) Macros have to be enabled.

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Dialing in crop load data with machine-learning management

International Fruit Tree Association meeting dives into sensor systems making progress on providing accurate, actionable crop load data. March 15, 2024 Good Fruit Grower

The dream of managing crop load with sensors and smart sprayers moves closer to reality with technological innovation every season. 

But it’s a complex dream, with evolving vision systems, new spray technologies and a different way to make crop load management decisions. 

“This is a really challenging thing,” said Tory Schmidt of the Washington Tree Fruit Research Commission. He led a session during the precision orchard technology workshop that preceded the International Fruit Tree Association’s annual conference in Yakima in February to highlight the companies in the “rapidly evolving landscape” of drone, tractor and smartphone imaging sensors that promise to track crop load at varying stages from bud to bin. 

Read more here…https://www.goodfruit.com/dialing-in-crop-load-data-with-machine-learning-management/

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Researchers publish results on crop load management of cider apples

Dr. Gregory Peck and his team at Cornell Univesity of David Zakalik, Michael Brown, and Craig Kahlke recently published several papers on the results of their research on crop load management of cider apples. Some cider apple varieties are notorioulsy biennial bearing which “exacerbates supply chain issues for cidermakers in North America.” Their results suggest that summer applications of plant growth regulators do not promote return bloom or reduce biennial bearing in seven cider apple varieties, however, fruitlet thinning did reduce biennial bearing and improve juice quality. These results should be of interest to all cider apple growers. The full papers can be viewed below…

Fruitlet Thinning Reduces Biennial Bearing in Seven High-tannin Cider Apple Cultivars
David Zakalik, Michael G. Brown, and Gregory M. Peck
https://doi.org/10.21273/HORTSCI17455-23

Fruitlet Thinning Improves Juice Quality in Seven High-tannin Cider Cultivars
David L. Zakalik, Michael G. Brown, and Gregory M. Peck
https://doi.org/10.21273/HORTSCI17096-23

Summer Applications of Plant Growth Regulators, Ethephon And 1-Naphthaleneacetic Acid, Do Not Promote Return Bloom or Reduce Biennial Bearing in Seven High-Tannin Cider Apple Cultivars
David L. Zakalik, Michael G. Brown, Craig J. Kahlke,
and Gregory M. Peck
Journal of the American Pomological Society 77(2): 75-92 2023

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2023 SCRI Annual Report: Cornell Cooperative Extension – Lake Ontario Fruit Program | Extension Activities 2023

Summary:
In January of 2023, a series of 3 virtual nationwide meetings was conducted by the PACMAN Research and Extension team to update growers about the current state of precision crop load management of apples. At the end of February and early March, CCE LOF conducted 3 Ag-tech sessions in person in Rochester, NY, and one statewide virtual session. More than 450 people in total attended the PACMAN virtual meetups and the 4 Ag-tech sessions offered by CCE LOF during the winter months of 2023. CCE LOF carried out one pruning severity study on ‘Honeycrisp’, compared and validated the use of two thinning prediction models (FSDM and the FGRM) on ‘Honeycrisp’ and ‘Gala’ with grower collaborators, and worked with several companies who are developing rovers or drones to count flowers and fruitlets (Pometa, Orchard Robotics, Vivid, and Outfield). In July, CCE LOF conducted a very successful fruit summer tour in Wayne County where several digital technologies were featured to more than 250 tour participants. In the 2023 growing season and in a few more orchards in the Lake Ontario Fruit region and at Cornell AgriTech in Geneva, the research, development, and first adoption of several digital technologies has been taking place to help improve the accuracy and labor efficiency of precision crop load management.

Cornell research and extension team: T.L. Robinson (Cornell AgriTech), L. Gonzalez (Cornell AgriTech), S. Howden (Cornell AgriTech), Kathy Campo (Cornell AgriTech), M. Miranda Sazo (CCE LOF), C. Kahlke (CCE LOF), L. Tee (CCE LOF), and D. Acquilano (CCE LOF).

Full PDF of the report download below…

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Developers Conference for Precision Crop Load Management of Apples

8-January, 2024. Hosted by Dr. Terence Robinson, Cornell University, via Zoom.

YouTube Playlist of all of the following presentations…or link below to individual video of presentation(s).


Digital needs identified by the PCLM University team Terence Robinson

Progress on digital imaging of tree structure Yu Jiang

Progress on flower/fruitlet/limb identification for robotic pruning, etc. Long He and Paul Heinemann

Presentation of progress by Orchard Robotics Charles Wu

Presentation of progress by Aurea Imaging Bert Rijk

Presentation of progress Vivid Machine Jenny Lemieux

Presentation of progress by Pometa Patrick Plonski

Presentation of progress by Outfield Oli Hilbourn

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Evaluations of Digital Technologies for Estimating Trunk Cross-sectional Area, Flower Cluster Number, Fruit Set and Yield of Apple

Luis Gonzalez Nieto1, Anna Wallis2, Jon Clements3, Mario Miranda Sazo4, Craig Kahlke4, Thomas M. Kon5 and Terence Robinson1

1Horticulture Section, School of Integrative Plant Science, Cornell University, Geneva, NY

2Michigan State University Extension, Grand Rapids, MI

3University of Massachusetts, Amherst, MA

4Cornell Cooperative Extension, Lake Ontario Fruit Program, Newark and Lockport, NY

5Mountain Horticultural Crops Research and Extension Center, Department of Horticultural Sciences, North Carolina State University, Mills River, NC 28759, USA

Paper presented by Luis Gonzalez at Precison Management of Orchards and Vineyards (PMOV) ISHS Symposium, Tatura, Victoria (Australia) on 8-December, 2023.

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Precision Crop Load Management of Apple Using Digital Technology

T.L. Robinson, L. Gonzalez, and Y. Jiang (Hort. Section, School of Integrative Plant Science, Cornell University, Geneva, NY)
M. Miranda Sazo and C. Kahlke (Cornell Cooperative Extension, Lake Ontario Fruit Team, Newark, NY)

Paper presented by Luis Gonzalez at Precison Management of Orchards and Vineyards (PMOV) ISHS Symposium, Tatura, Victoria (Australia) on 7-December, 2023.

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PACMAN Project Overview

Presentation by Tory Schmidt, Washington Tree Fruit Research Commission at the Northwest Hort Expo, Three Rivers Convention Convention Center in Kennewick, WA on December 5th, 2023. This during the Washington State Tree Fruit Association (WSTFA) 119th Annual Meeting.

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How AI is changing agricultural practices in India

The agriculture industry is hoping that more modern technology can make a big difference in productivity.

Listen on Marketplace Morning Report (December 27, 2023)

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Scale and speed bumps for technology in tree fruit

A case study in crop load management: The challenges encountered as new technology tried to find its footing in the fruit industry can illustrate the speed bumps to ag tech success.

December 2023 Issue

Kate Prengaman // December 11, 2023

The first time I watched a camera sensor scan an orchard row and size every apple, I was amazed. The second time, I wondered what the value of such data would be to the grower. 

Turns out, I’m not the only one wondering. 

Growers and tech developers don’t always know, either.

Read full story here from GoodFruit Grower here…