A computer vision system for in-field quality evaluation: preliminary results on peach fruit

In Italy, peaches are paid according to size, color and appearance. Real time fruit harvest quality information could support growers and the whole fruit chain improving segmented selection for consumers as well as to increase growers' income. In this study, a computer vision system was tested aiming to quantifying and sizing peaches in bins at harvest time. Two different depth cameras the Intel RealSense D435i and D455, and two different light conditions, natural and artificial, were tested, to assess potential issues and to achieve the most suitable set-up for future developments. Automated fruit detection appeared less difficult, while the system presents generally overestimation in fruit size. The D435i camera in artificial light condition obtained the best outcome with a RMSE of 17.91 mm, compared to the reference diameter of measured fruit. Although the results obtained are with low accuracy and precision, the vision systems technique seems promising and suggests solutions to further improvements. Future studies will focus on improving the system for sizing and color estimation, coupled to georeferenced data directly in the field with the aim of mapping field quality variability. The idea is to develop a low-cost tool that coupled to harvesting platforms connects fruit quality at the time of harvest to post-harvest operations.