Design and Implementation of an Automatic Grading System of Diced Potatoes Based on Machine Vision

Abstract

Potato is one of the most important crops in the world. In recent years, potato and its processed products have gradually become important trade goods. As an important semi-manufactured product, diced potatoes need to be graded according to their three-dimensional (3D) size and shape before trading. 3D information inspection manually is a time-consuming and labor intensive work. A novel automatic grading system based on computer vision and near-infrared linear-array structured lighting was proposed in this paper. Two-dimensional size and shape information were extracted from RGB images, and height information was measured in NIR images combined with structured lighting. Then, a pair of pseudo-color and gray level height map images fusing with 3D size and shape information was constructed. Finally, diced potatoes were classified into either regular or irregular class according to their 3D information and criteria required by the industry. The grading system and proposed algorithm were testified by a total of 400 diced potatoes with different size and shapes. The test results showed that the detection error was in the range of about 1 mm, and the classification accuracy was 98%. The results indicated that the system and algorithm was efficient and suitable for the 3D characteristic inspection of diced potatoes.

Multi-Input Convolutional Neural Network for Flower Grading

“…Arakeri and Lakshmana [1] proposed a computer vision based automatic system for tomato grading using ANN (artificial neural network). Wang et al [2] also proposed an automatic grading system of diced potatoes based on computer vision and near-infrared lighting. Although these systems are successful, both of them focused on binary classification.…”