Orientation detection of fruits by means of convolutional neural networks and laser line projection for the automation of fruit packing systems

Sorting and quality inspection of fruits is still a time-consuming task because of the low level of automation. Natural products exhibit a greater variance compared to manufactured products regarding quality and dimensions, which complicates their automated processing. In our paper, we propose a method to determine the orientation of fruits by applying a specially trained Convolutional Neural Network to 2.5-dimensional image data generated by a combination of a color CMOS camera and a red line laser. We achieve an accuracy of the orientation prediction of approximately 5 degrees, which presents very promising results for the automation of fruit packing systems.