(1) Background: Extra virgin olive oil production is strictly influenced by the quality of fruits. The optical selection allows for obtaining high quality oils starting from batches with different qualitative characteristics. This study aims to test a CNN algorithm in order to assess its potential for olive classification into several quality classes for industrial purposes, specifically its potential integration and sorting performance evaluation. (2) Methods: The acquired samples were all subjected to visual analysis by a trained operator for the distinction of the products in five classes related to the state of external veraison and the presence of visible defects. The olive samples were placed at a regular distance and in a fixed position on a conveyor belt that moved at a constant speed of 1 cm/s. The images of the olives were taken every 15 s with a compact industrial RGB camera mounted on the main frame in aluminum to allow overlapping of the images, and to avoid loss of information. (3) Results: The modelling approaches used, all based on AI techniques, showed excellent results for both RGB datasets. (4) Conclusions: The presented approach regarding the qualitative discrimination of olive fruits shows its potential for both sorting machine performance evaluation and for future implementation on machines used for industrial sorting processes.

At the CREA research facility of Treviglio (Bergamo, Italy), to provide farmers with valuable hints for the transition from conventional to precision agriculture, information on crop production dynamics (Maize and Triticale) has been obtained using real-time soil mapping (resistivity technique) and production quality and quantity monitoring with a commercial yield mapping apparatus. The geostatistical processing of data resulted in the same zoning for Triticale, meaning that the characteristics of soil influenced crop behavior more than the variability resulting from other factors, which suggests that improvements in product yields can be planned and achieved acting, for instance, on variable rate distribution of fertilizers. The importance of the acquired data can help farmers to manage factors that are external to their plots of land.