Challenges of autonomous in-field fruit harvesting and concept of a robotic solution
Tim Tiedemann, Florian Cordes, Matthis Keppner, Heiner Peters
Editors: Giuseppina Gini, Henk Nijmeijer, Wolfram Burgard, Dimitar Filev
In Proceedings of the 19th International Conference on Informatics in Control, Automation and Robotics, (ICINCO-2022), 2022, SciTePress, pages 508-515, 2022. ISBN: 978-989-758-585-2.
Since the beginning of humans cultivating plants in fields, agriculture underwent a continuous shift from purely manual labor over simple machinery to more and more automated processes. Autonomous driving with navigation and self localization in the field is state of the art. Also, automated machines for fruit processing are available as well. In cases where the fruit is damageable and varies in size and shape, automated processing is challenging. One example of such damageable fruits are strawberries. Size, weight, and shape at the optimal ripeness can vary a lot. Additionally, a change from ripe to overripe occurs relatively quick and is sometimes hard to recognize. A further challenge when harvesting strawberries is a dense leafage that can cover the fruits partly or completely. In this paper, a concept of an autonomous in-field strawberry harvesting robot for non-elevated but ground-raised strawberry plants, with or without a tunnel, is presented. The robot is supposed to use mul ti-spectral imaging and machine learning based ripeness classification. Besides the overall concept, first data of this early-stage project is shown, too.
Agricultural Robotics; Machine Learning; Autonomous Harvesting; Multi-spectral Imaging; Classification