An Automated Egg Incubator with Raspberry Pi-Based Camera-Assisted Candling and R-CNN-based Maturity Detection

Abstract

This study focuses on developing an automated egg incubator with a camera-assisted candler for egg maturity detection of balut and penoy commercial duck eggs. The incubator is a four-layer chamber installed with a heater, fan, and DHT11 sensors. DHT11 sensors are interfaced with a Raspberry Pi 4 to observe and maintain the optimal parameters inside the incubator. Trays with built-in candlers made from fluorescent bulbs are placed per layer with a capacity of 20 eggs positioned on rollers. These rollers are programmed to drive every 8 hours for 5 minutes for the egg turning which is essential in incubating eggs. Cameras are installed to capture the images of the candled eggs on their 1st, 10th, and 18th day. The result will be shown on a monitor with a user-friendly GUI which will help the vendor to determine the condition and maturity of the eggs inside the incubator. A region-based convolutional neural network (R-CNN/RCNN) was used as the classifier algorithm for balut, penoy, and fresh eggs. The classification accuracy of the proposed system is 80.5%.