Quality control of apples by means of convolutional neural networks - Comparison of bruise detection by color images and near-infrared images

Different stages of logistic process can affect apples, which often results in illusive bruises - making it extremely hard for the personal who has to sort out these fruits quickly before packing. We aim to provide a solution for bruise detection. In this contribution, we use state of the art convolutional neural network architectures for the task. Simultaneous input is taken from color CMOS and near-infrared (with a bandwidth filter) cameras, illuminated with a special light source. We achieved an accuracy above 97% for bruise detection in both cases: Colored and near-infrared images, indicating that both options are equally suitable.