Minimalist AdaBoost for blemish identification in potatoes

We present a multi-class solution based on minimalist Ad- aBoost for identifying blemishes present in visual images of potatoes. Using training examples we use Real AdaBoost to rst reduce the fea- ture set by selecting ve features for each class, then train binary clas- siers for each class, classifying each testing example according to the binary classier with the highest certainty. Against hand-drawn ground truth data we achieve a pixel match of 83% accuracy in white potatoes and 82% in red potatoes. For the task of identifying which blemishes are present in each potato within typical industry dened criteria (10% coverage) we achieve accuracy rates of 93% and 94%, respectively

A prototype low-cost machine vision system for automatic identification and quantification of potato defects

This paper reports on a current project to develop a prototype system for the automatic identification and quantification of potato defects based on machine vision. The system developed uses off-the-shelf hardware, including a low-cost vision sensor and a standard desktop computer with a graphics processing unit (GPU), together with software algorithms to enable detection, identification and quantification of common defects affecting potatoes at near-real-time frame rates. The system uses state-of-the-art image processing and machine learning techniques to automatically learn the appearance of different defect types. It also incorporates an intuitive graphical user interface (GUI) to enable easy set-up of the system by quality control (QC) staff working in the industry