Autonomous Apple Fruitlet Sizing and Growth Rate Tracking using Computer Vision

TL;DR

This paper introduces a computer vision system that accurately measures apple fruitlet sizes and growth rates using stereo images, significantly reducing manual effort and increasing speed compared to traditional methods.

Contribution

The paper presents a novel vision-based approach for measuring apple fruitlet growth, enabling faster, less labor-intensive, and more accurate tracking compared to manual caliper measurements.

Findings

Predicts abscise rates within 3.5% of current methods

Achieves a 6x increase in measurement speed

Requires significantly less manual effort

Abstract

In this paper, we present a computer vision-based approach to measure the sizes and growth rates of apple fruitlets. Measuring the growth rates of apple fruitlets is important because it allows apple growers to determine when to apply chemical thinners to their crops in order 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, time-consuming, and prone to human error. With images collected by a hand-held stereo camera, our system, segments, clusters, and fits ellipses to fruitlets to measure their diameters. The growth rates are then calculated by temporally associating clustered fruitlets across days. We provide quantitative results on data collected in an apple orchard, and demonstrate that our system is able to predict abscise rates within 3.5% of the current method with a 6 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 required to make the process fully autonomous.