Robots for Kiwifruit Harvesting and Pollination

TL;DR

This paper presents the development and testing of mobile robots for kiwifruit harvesting and pollination, including novel fruit detachment mechanisms, artificial pollination techniques, and navigation systems using lidar and computer vision in complex orchard environments.

Contribution

The study introduces new robotic mechanisms for kiwifruit detachment and pollination, along with advanced lidar and vision-based navigation methods tailored for pergola structured orchards.

Findings

Kiwifruit detachment mechanism reached over 80% of fruit in cluttered canopies.

Lidar-based navigation system successfully performed over 30 km of autonomous driving.

Computer vision algorithms matched lidar performance in row following.

Abstract

This research was a part of a project that developed mobile robots that performed targeted pollen spraying and automated harvesting in pergola structured kiwifruit orchards. Multiple kiwifruit detachment mechanisms were designed and field testing of one of the concepts showed that the mechanism could reliably pick kiwifruit. Furthermore, this kiwifruit detachment mechanism was able to reach over 80 percent of fruit in the cluttered kiwifruit canopy, whereas the previous state of the art mechanism was only able to reach less than 70 percent of the fruit. Artificial pollination was performed by detecting flowers and then spraying pollen in solution onto the detected flowers from a line of sprayers on a boom, while driving at up to 1.4 ms-1. In addition, the height of the canopy was measured and the spray boom was moved up and down to keep the boom close enough to the flowers for the spray to reach the flowers, while minimising collisions with the canopy. Mobile robot navigation was performed using a 2D lidar in apple orchards and vineyards. Lidar navigation in kiwifruit orchards was more challenging because the pergola structure only provides a small amount of data for the direction of rows, compared to the amount of data from the overhead canopy, the undulating ground and other objects in the orchards. Multiple methods are presented here for extracting structure defining features from 3D lidar data in kiwifruit orchards. In addition, a 3D lidar navigation system -- which performed row following, row end detection and row end turns -- was tested for over 30 km of autonomous driving in kiwifruit orchards. Computer vision algorithms for row detection and row following were also tested. The computer vision algorithm worked as well as the 3D lidar row following method in testing.