Portioning Algorithm Using the Bisection Method for Slicing Food

Abstract

Food is an essential part of human life and plays a crucial role in maintaining good health and well-being. In various industries, such as food processing and packaging, it is essential to ensure that raw materials are divided equally to optimize the production process and reduce waste. However, traditional methods of food processing and packaging can be time-consuming and prone to errors. Hence, we are interested in developing a method for accurately portion materials into equal sizes using the Intel RealSense D435i 3D camera to capture point cloud images of object, which are then processed using Python code, running on a Raspberry Pi 4, to generate cutting planes. In the experiment on object size variations, three sizes of plasticine weighing 50 g, 150 g, and 250 g. resulting in errors of 10.2%, 8.8%, and 7.3%, respectively. In the experiment on the number of cutting plane variations, keeping the object weight fixed at 150 g at 150 g, and divided into 2, 3, 4, and 5 pieces. The resulting errors were 1.3%, 8.8%, 10.7%, and 18.2%, respectively, according to the number of pieces. Our algorithm can generate precise cutting planes to partition the volume of an object. The primary cause of errors is the shape resolution of the object's point cloud that the camera can collect and the use of human hands for cutting the object.