Design, Integration, and Field Evaluation of a Robotic Blossom Thinning System for Tree Fruit Crops

Abstract

The US apple industry relies heavily on semi-skilled manual labor force for essential field operations such as training, pruning, blossom and green fruit thinning, and harvesting. Blossom thinning is one of the crucial crop load management practices to achieve desired crop load, fruit quality, and return bloom. While several techniques such as chemical, and mechanical thinning are available for large-scale blossom thinning such approaches often yield unpredictable thinning results and may cause damage the canopy, spurs, and leaf tissue. Hence, growers still depend on laborious, labor intensive and expensive manual hand blossom thinning for desired thinning outcomes. This research presents a robotic solution for blossom thinning in apple orchards using a computer vision system with artificial intelligence, a six degrees of freedom robotic manipulator, and an electrically actuated miniature end-effector for robotic blossom thinning. The integrated robotic system was evaluated in a commercial apple orchard which showed promising results for targeted and selective blossom thinning. Two thinning approaches, center and boundary thinning, were investigated to evaluate the system ability to remove varying proportion of flowers from apple flower clusters. During boundary thinning the end effector was actuated around the cluster boundary while center thinning involved end-effector actuation only at the cluster centroid for a fixed duration of 2 seconds. The boundary thinning approach thinned 67.2% of flowers from the targeted clusters with a cycle time of 9.0 seconds per cluster, whereas center thinning approach thinned 59.4% of flowers with a cycle time of 7.2 seconds per cluster. When commercially adopted, the proposed system could help address problems faced by apple growers with current hand, chemical, and mechanical blossom thinning approaches.

Figures (13)

• Figure 1: Overview of the proposed robotic blossom thinning system. The system consisted of three major components a machine vision system, a manipulator and motion planning system, and an end-effector system. Mask R-CNN based deep learning algorithm was investigated to identify and delineate cluster boundaries followed by cluster pose (position and orientation) estimation. The cluster visit sequence was determined and motion plan for the robotic manipulator was developed to navigate end effector to desired thinning position and orientation to conduct thinning.
• Figure 2: The experimental orchard (Envy apple variety) used for evaluating the robotic blossom thinning system. The trees in this experimental site were trellis-trained to create a V-shaped fruiting wall orchard.
• Figure 3: Electrically actuated end-effector design with spindle string. Strings used were the pieces of off-the-selves grass trimming lines with grooved edges.
• Figure 4: Integrated system during the field evaluation in V-trellised fruiting wall architecture commercial apple orchard in Prosser, WA. The UR5e robotic manipulator was attached rigidly at a back of a utility vehicle.
• Figure 5: Hardware components used during field test and communication protocol. Dell Alienware15R4 laptop was used as the central processing system of the integrated system. The laptop communicated with the Intel RealSense camera and Arduino via USB connection while LAN connection was established between the UR5e controller and the laptop to control the UR5e manipulator.