Harry Freeman1, Mohamad Qadri1, Abhisesh Silwal1, Paul O’Connor2, Zachary Rubinstein1, Daniel Cooley2, and George Kantor1
1Carnegie Mellon University Robotics Institute, PA, USA
2University of Massachusetts Amherst Stockbridge School of Agriculture, MA, USA
Abstract – Measuring growth rates of apple fruitlets is important because it allows apple growers to determine when to apply
chemical thinners to their crops to optimize yield. The current
practice of obtaining growth rates involves using calipers to
record sizes of fruitlets across multiple days. Due to the number
of fruitlets needed to be sized, this method is laborious, timeconsuming, and prone to human error. In this paper, we present
a computer vision approach to measure the sizes and growth
rates of apple fruitlets. With images collected by a hand-held
stereo camera, our system detects, segments, and fits ellipses to
fruitlets to measure their diameters. To measure growth rates,
we utilize an Attentional Graph Neural Network to associate
fruitlets across different days. We provide quantitative results on
data collected in an apple orchard, and demonstrate that our
system is able to predict abscise rates within 3% of the current
method with a 7 times improvement in speed, while requiring
significantly less manual effort. Moreover, we provide results on
images captured by a robotic system in the field, and discuss the
next steps to make the process fully autonomous.
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