DEVELOPMENT AND OPTIMIZATION OF A DEEP-LEARNING-BASED EGG-COLLECTING ROBOT

Manual collection of floor eggs in cage-free hen housing systems is time-consuming and laborious. The objectives of this study were to (1) develop a robot to automatically collect floor eggs and (2) optimize the performance of recognizing and picking eggs with the robot. The robot consisted of a deep-learning-based egg detector, a robot arm, a twofinger gripper, and a hand-mounted camera. The deep-learning model, You Only Look Once (YOLO) V3, was embedded in the vision system to detect and locate eggs on a simulated litter floor in real-time. Image processing algorithms (e.g., cropping, erosion, etc.) were implemented for the detection and provided the robot with centroid coordinates, orientation, and axis lengths of the detected eggs, so that the gripper could be manipulated with an appropriate angle and opening width to grasp the detected eggs. For optimization, the YOLO V3 model was retrained with a dataset of floor eggs and achieved >93% performance in detecting and locating eggs. The kernel size of 65. 65 pixels for erosion and dilation in image processing assisted in extracting the geometry features of eggs with the least remaining noises. Among the tested materials, soft grouting sponge attached to the gripper had the highest success rates for egg picking. The robot achieved 92% to 94% success in picking white and brown eggs. In sum, the developed egg-collecting robot can be relied on for picking floor eggs to assist in precision management of cage-free hen housing systems.

Automated summary

The study developed a robot for automated collection of floor eggs in cage-free hen housing systems and optimized its performance in recognizing and picking eggs. By using deep learning model YOLO V3 and image processing algorithms, the robot achieved high accuracy in detecting and locating eggs. The use of a gripper with soft grouting sponge attachment proved to be effective in enhancing the success rates of picking white and brown eggs.