Fruit Tracking Over Time Using High-Precision Point Clouds

Abstract

Monitoring the traits of plants and fruits is a fundamental task in horticulture. With accurate measurements, farmers can predict the yield of their crops and use this information for making informed management decisions, and breeders can use it for variety selection. Agricultural robotic applications promise to automate this monitoring task. In this paper, we address the problem of monitoring fruit growth and investigate the matching of fruits recorded in commercial greenhouses at different growth stages based on data recorded from terrestrial laser scanners. This is challenging as fruits appear highly similar, change over time, and are subject to severe occlusions. We first propose a fruit descriptor, which captures the topology of the fruit surroundings to facilitate the matching between different points in time. We capture and describe the relationship between a fruit and its neighbors such that our descriptors are less affected by the growth over time. Furthermore, we define a matching cost function and use an optimal assignment algorithm to match the fruit observations taken in different weeks. The experiments show that our descriptor achieves a high spatio-temporal matching accuracy, which is superior to the commonly used geometric point cloud descriptors.