Objectives
Nowadays, imaging is largely used as a decision support tool in the agricultural and agrofood sectors. It enables a better management of resources in the field of precision agriculture and quality control of products both at laboratory and at field levels as well as online in processing plants. In image analysis, images are usually obtained in the visible field, however due to large analytical improvements, images can be also obtained from techniques based on near-infrared sensors. Near infrared hyperspectral imaging allows to simultaneously acquire spatial (i.e. distribution) and spectral (thus composition) information from a sample. The hyperspectral imaging technology provides the ability to collect thousands of spectra from a sample or process in a non-destructive manner and without interfering with the sample composition or process. Powerful computers and image processing techniques, including statistical and chemometric tools are essential for the analysis of this type of data. In recent years, CRA-W has acquired a large expertise in this field through various projects and collaborations with public institutes or private companies, at regional, national or international level. It maintains this competence by (i) conducting feasibility studies on various applications, (ii) developing standardized methods and algorithms for the acquisition, processing and interpretation of NIR images and spectra as well as (iii) producing appropriate and optimized responses for a given agricultural and agro food problem.
