An overview of visual servoing for robotic manipulators in digital agriculture

Abstract

Innovations in terms of robotic manipulator control in digital agriculture have advanced considerably in the last decade with the aim of reducing costs and increasing efficiencies. The availability of compact imaging sensors such as digital cameras that can perceive depth information besides the flexibility of open-source image processing software that can be trained for different applications has played significant roles in accelerating this sector. Preliminary studies have shown that the majority of available robot manipulators in agriculture are using Image-Based Visual Servo (IBVS) control to reach a target position. The presented study provides an overview of different redundant manipulators that are controlled by means of visual servoing for automating various field tasks in digital agriculture including (i) pruning, thinning, and trimming, (ii) harvesting, and (iii) inspection and target spraying. The reviewed works suggest that developing optimal tree shapes and planting techniques is necessary to improve the performance of visual servo control and automate farming operations with robots. In addition, selection of the right imaging sensors, graphics processing units, and training of the computer vision algorithms with more fruits and plants datasets have been highlighted as the three main elements for improving the functionality of IBVS in manipulator control for agricultural applications.