A low-cost multispectral device for in-field fruit ripening assessment

When assessing fruit ripeness, multispectral sensors offer a cheaper alternative to high-resolution spectroscopy. Additionally, they provide a more robust solution to environmental factors, in contrast to multi-spectral cameras. However, the low spectral resolution of multispectral devices proposed in the literature may not be sufficient to discriminate with acceptable accuracy a high number of different ripeness classes with machine learning techniques. Moreover, meaningful features to be given as input to the classification models are generally hand-crafted on the basis of the fruit to be evaluated, making the devices not versatile across other fruit categories. This paper aims at overcoming these limitations, by introducing a multispectral device for in-situ fruit-ripening evaluation that is i) affordable for a wide range of end-users, ii) robust to environmental factors, iii) capable of automatically finding the most meaningful features depending on the fruit categories to evaluate and iv) capable of discriminating a high number of fruit ripeness classes with high accuracy. The device integrates a broadband LED and a VIS-NIR multispectral sensor to gather information about the fruit’ ripeness levels through predictive machine learning algorithms. The proposed device was trained on a dataset of 450 spectral measurements acquired from tomatoes at six different ripeness stages. The results demonstrated the high capability of the proposed approach to recognize the tomatoes’ ripeness levels (average accuracy of 93.72%).