October 2022 - 7
MSU develops apple fruit set prediction model
By Gary Pullano
An updated apple fruit set prediction
model is being developed by Michigan
State University (MSU) researchers.
The model was presented at the
Michigan Pomesters' Ridgefest field
day in late July, with participants
touring research trials at MSU's
Clarksville Research Center.
Researchers acknowledged the fruit
growth rate model for predicting
apple fruit set developed by retired
MSU Extension educator Phil
Schwallier is fairly accurate, but
it requires time-intensive fruitlet
diameter measurements and data
entry in limited adoption.
" We have developed a simple, time
efficient fruit set prediction model to
aid with thinner application decisions
and inform future image analysis
methods, " said Laura Hillmann,
a Ph.D. candidate in associate
professor Todd Einhorn's lab in MSU's
Department of Horticulture.
a value of an
" You get
your fruit set
many fruit are
less than 10%
size of your
biggest fruit in
the sheet that Phil Schwallier had
created for the fruit growth rate
model, " Hillmann said. " With that
approach, Initially, you have to select
100 spurs, tag them and mark down
the individual flowers in the spur so
that you can go back every three or
so days and re-measure the same fruit
so that you can create the repeated
measures of diameter and get your
growth rate for individual fruit.
You then put the data into the Excel
Sheet and get a fruit set prediction
for a specific day in your thinning
window. This can help guide thinning
decisions within that window. "
The current project seeks to make
the process more user friendly and
less time consuming.
" While we're basing everything
on the same assumptions, the 10%
biggest fruit, anything that is less
than half the size of that will fall
off, " Hillmann said. " It's the same
assumption, but the way we're setting
up ours is that you select 400 spurs.
Instead of repeatedly measuring, you
will sample 100 of those spurs, weigh
them out and put the data of those
weights into your Excel sheet and get
the same prediction. "
Hillmann said users can input the
resulting findings onto a scale, and
connect a laptop to the scale.
" You put the fruit on the scale and
print the (data) that will give you all
of your weights in the row, " she said.
Hillmann said researchers have
been testing both models side-by-side
in Clarksville for the past three years.
" At least for the first two years,
we could see how very well-aligned
they are, " she said. " They both had a
downward progression. Both models
are always very close with the fruit
set prediction. In 2020, actually, our
model had a little better prediction to
follow final fruit set. In 2021, we had
a good line, as well. "
Hillmann said the 2022 findings
" look a little wonky.
" I'm not sure exactly what happened.
I still have to tease out that data, " she
said. " We do still see this trend line in
our model that we're hoping to use to
create an equation so we can predict
the fruit set a little bit earlier. Right
now, when we look at the comparisons
between the two models we can see
that our model is a little bit late in
the fruit set prediction, or a little bit
behind the fruit growth rate model. "
Leading into the current season,
researchers have been testing the
model in central Michigan.
" We went in and tagged all the spurs
and came back to sample and weigh, "
she said. " The progression toward the
fruit set seemed to indicate that our
prediction model was on track pretty
well as we get along in the season. "
The newer model's main timesaving
feature is that researchers don't
have to go back to the original tree
and measure the same fruit repeatedly.
" Also, we can harvest them in the
morning and weigh them in the
afternoon whenever we have time, "
Hillman said. " That's kind of the main
positive point to this. "
Einhorn said that the approach
automatically puts the data in an
" You don't have to find the fruit,
you don't have to re-measure it every
time then enter all the data. We don't
have the time statistics, but I can tell
you from doing it, it's not even close, "
he said. " It's
about every 3-6
days that you
would do on
every three days.
By six days in
you can pick
up fruit drop.
You can pick
up a prediction
to know if you can put a repeat
" The key with this whole thing is we
do our first measurement when the
thinner's applied, and it's every three
days, " Einhorn said. " If developmentally
we're OK, as long as we're coming back
again around 12-14 millimeters with
the prediction, you can get another
application (of chemicals). "
Schwallier, one of the farm
cooperators on the project, said he is
impressed with the results thus far. FGN
FGN | OCTOBER 2022 | 7
Laura Hillmann, a Ph.D. candidate in associate professor Todd Einhorn's lab in Michigan
State University's Department of Horticulture, explains a new apple fruit set prediction
model. Photo: Gary Pullano
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