Ricerc@Sapienza

In the context of Robotics in Agricultural Engineering there is an increasing interest in the use of data produced by sensors that go beyond the visible RGB spectrum, such as hyper and multi spectral cameras. This data can be correlated to quality related physical characteristics, such as the the content of specific chemical substances (sugars, flavonoids, etc). Hyperspectral sensors are used to find the spectral signature of a substance that is dissolved in the fruit or vegetable juices. The spectral signature tells how the substance responds to different light wavelengths, however it is usually measured in laboratory controlled environments. Since robotic platforms work on the field, where the lighting conditions are non ideal, it is not possible to know in advance how the spectral signature would be influenced by the environment conditions. In addition, hyperspectral sensors are extremely expensive, and usually not compatible with the costs of regular agronomic operations. Cheaper multispectral cameras, with lower accuracy and fewer wavelengths than the hyperspectral devices, are often used for other more forgiving estimation tasks, such as the NDVI estimation. We hypothesize that the multispectral cameras could be used for spectral signature estimation in the wild compensating through data analysis and machine learning the loss of information. We propose a systematic study of the environmental noise sources while collecting multispectral images in different lighting conditions in order to model the correlation between the ideal spectral signature and the real approximate data collected with less accurate sensors. We also aim to explore semi-supervised learning strategies to reduce the labeling effort and make the approach more generally applicable to the Agronomic industry. As a real case study, we consider the estimation of sugar content in table grapes, for which the ideal spectral signature has been studied, and for which new data could be easily acquired.