  We present a system to measure the ripeness of fruit with a hyperspectral
camera and a suitable deep neural network architecture. This architecture did
outperform competitive baseline models on the prediction of the ripeness state
of fruit. For this, we recorded a data set of ripening avocados and kiwis,
which we make public. We also describe the process of data collection in a
manner that the adaption for other fruit is easy. The trained network is
validated empirically, and we investigate the trained features. Furthermore, a
technique is introduced to visualize the ripening process.
