In order to increase the shelf life of fruits and vegetables and reduce post-harvest losses, it is necessary to develop more sophisticated analysis technologies that monitor their internal quality and detect the internal defects. These technologies can thus provide adequate support to decision-making throughout the production and distribution chain, mitigating food waste with gains for the whole range.
This project aims to develop an innovative optical system for automatic and non-invasive calibration of fruit through internal quality parameters and internal defect classification. This system will be integrated in Calibrafruta's lines and will allow us to separate the fruits through these properties, in association with other parameters already selected, such as caliber and external defects. Innovations introduced at the optical and processing algorithms should place our system beyond competing systems, giving Calibrafruta a clear advantage. In addition, the calibrator will store the data collected by its sensors together with metadata related to the conditions of storage and production of the fruits. The sensor data will then be analyzed in real time or over a period of time to detect trends in fruit maturation in the cold storage chambers or to relate crop management practices to the appearance of internal defects.
The central innovation of the system is based on the sensor fusion concept. The reflectance / transmittance spectroscopy measurements used in current systems will be combined with measurements obtained by multispectral imaging. Thus, our system predictions will be much more reliable than competing systems, which will support the 4.0 approach of the calibrator as a smart multi-sensor. The design is further supported by additional innovations at the level of the optical geometry employed in the spectroscopic component, such as the possibility of making 360-degree scans on the surface of the fruit to detect internal defects using only one spectrometer.