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In this paper, we present a computer vision-based approach to measure thesizes and growth rates of apple fruitlets. Measuring the growth rates of applefruitlets is important because it allows apple growers to determine when toapply chemical thinners to their crops in order to optimize yield. The currentpractice of obtaining growth rates involves using calipers to record sizes offruitlets across multiple days. Due to the number of fruitlets needed to besized, this method is laborious, time-consuming, and prone to human error. Withimages collected by a hand-held stereo camera, our system, segments, clusters,and fits ellipses to fruitlets to measure their diameters. The growth rates arethen calculated by temporally associating clustered fruitlets across days. Weprovide quantitative results on data collected in an apple orchard, anddemonstrate that our system is able to predict abscise rates within 3.5% of thecurrent method with a 6 times improvement in speed, while requiringsignificantly less manual effort. Moreover, we provide results on imagescaptured by a robotic system in the field, and discuss the next steps requiredto make the process fully autonomous.

Autonomous Apple Fruitlet Sizing and Growth Rate Tracking using Computer Vision