Hyperspectral imaging and other optical techniques for (in-field/in-lab) physico-chemical attributes estimation of agri-food vegetal products

In the agri-food sector, measurement and monitoring activities contribute to high quality end products. In particular, considering food of plant origin, several product quality attributes can be monitored. Among the non-destructive measurement techniques, a large variety of optical techniques are available, including hyperspectral imaging (HSI) in the visible/near-infrared (Vis/NIR) range, which, due to the capacity to integrate image analysis and spectroscopy, proved particularly useful in agronomy and food science. Many published studies regarding HSI systems were carried out under controlled laboratory conditions. In contrast, few studies describe the application of HSI technology directly in the field, in particular for high-resolution proximal measurements carried out on the ground. Based on this background, the activities of the present PhD project were aimed at exploring and deepening knowledge in the application of optical techniques for the estimation of quality attributes of agri-food plant products. First, research activities on laboratory trials carried out on apricots and kiwis for the estimation of soluble solids content (SSC) and flesh firmness (FF) through HSI were reported; subsequently, FF was estimated on kiwis using a NIR-sensitive device; finally, the procyanidin content of red wine was estimated through a device based on the pulsed spectral sensitive photometry technique. In the second part, trials were carried out directly in the field to assess the degree of ripeness of red wine grapes by estimating SSC through HSI, and finally a method for the automatic selection of regions of interest in hyperspectral images of the vineyard was developed. The activities described above have revealed the potential of the optical techniques for sorting-line application; moreover, the application of the HSI technique directly in the field has proved particularly interesting, suggesting further investigations to solve a variety of problems arising from the many environmental variables that may affect the results of the analyses.

Unveiling the unknown of cool stars with high-resolution spectroscopy

Cool giant and supergiant stars are among the brightest populations in any stellar system and they are easily observable out to large distances, especially at infrared wavelengths. These stars also dominate the integrated light of star clusters in a wide range of ages, making them powerful tracers of stellar populations in more distant galaxies. High-resolution near-IR spectroscopy is a key tool for quantitatively investigating their kinematic, evolutionary and chemical properties. However, the systematic exploration and calibration of the NIR spectral diagnostics to study these cool stellar populations based on high-resolution spectroscopy is still in its pioneering stage. Any effort to make progress in the field is innovative and of impact on stellar archaeology and stellar evolution. This PhD project takes the challenge of exploring that new parameter space and characterizing the physical properties, the chemical content and the kinematics of cool giants and supergiants in selected disc fields and clusters of our Galaxy, with the ultimate goal of tracing their past and recent star formation and chemical enrichment history. By using optical HARPS-N and near-infrared GIANO-B high-resolution stellar spectra in the context of the large program SPA-Stellar Population Astrophysics: the detailed, age-resolved chemistry of the Milky Way disk” (PI L. Origlia), an extensive study of Arcturus, a standard calibrator for red giant stars, has been performed. New diagnostics of stellar parameters as well as optimal linelists for chemical analysis have been provided. Then, such diagnostics have been used to determine evolutionary properties, detailed chemical abundances of almost 30 different elements and mixing processes of a homogeneous sample of red supergiant stars in the Perseus complex.

Design and characterization of automated agricultural vehicle systems

Nowadays, technological advancements have brought industry and research towards the automation of various processes. Automation brings a reduction in costs and an improvement in product quality. For this reason, companies are pushing research to investigate new technologies. The agriculture industry has always looked towards automating various processes, from product processing to storage. In the last years, the automation of harvest and cultivation phases also has become attractive, pushed by the advancement of autonomous driving. Nevertheless, ADAS systems are not enough. Merging different technologies will be the solution to obtain total automation of agriculture processes. For example, sensors that estimate products' physical and chemical properties can be used to evaluate the maturation level of fruit. Therefore, the fusion of these technologies has a key role in industrial process automation. In this dissertation, ADAS systems and sensors for precision agriculture will be both treated. Several measurement procedures for characterizing commercial 3D LiDARs will be proposed and tested to cope with the growing need for comparison tools. Axial errors and transversal errors have been investigated. Moreover, a measurement method and setup for evaluating the fog effect on 3D LiDARs will be proposed. Each presented measurement procedure has been tested. The obtained results highlight the versatility and the goodness of the proposed approaches. Regarding the precision agriculture sensors, a measurement approach for the Moisture Content and density estimation of crop directly on the field is presented. The approach regards the employment of a Near Infrared spectrometer jointly with Partial Least Square statistical analysis. The approach and the model will be described together with a first laboratory prototype used to evaluate the NIRS approach. Finally, a prototype for on the field analysis is realized and tested. The test results are promising, evidencing that the proposed approach is suitable for Moisture Content and density estimation.