Measuring the Ripeness of Fruit with Hyperspectral Imaging and Deep Learning
This work addresses fruit quality assessment for agriculture and food industries, but it is incremental as it applies existing deep learning methods to a new dataset.
The authors tackled the problem of measuring fruit ripeness by developing a system using hyperspectral imaging and a deep neural network, which outperformed baseline models on avocados and kiwis, with a public dataset released.
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.