Approaches For Modeling Agricultural Hyperspectral Signature Objects

Hyperspectral imaging applied to fruits and vegetables has experienced significant growth in recent years. This technique involves collecting reflectance spectra containing valuable information for various applications, such as monitoring the nutritional and taste qualities of fruits and vegetables, detecting rotten samples, and classifying varieties. The potential impact of this technique is high; in fact, monitoring the spectral signatures of objects in fields or on trees allows farmers to assess pre-harvest gustatory and nutritional qualities by monitoring the supply of water, nutrients, and treatments.

Many physics-based models used in the literature have demonstrated potential for studying such objects, including Prospect, Farrell, MARMIT, and MPBOM. Among these, we are interested in two models: Prospect for the characterization of leaves and Farrell for the characterization of fruits and vegetables. In this article, we propose an initial theoretical approach based on the identification of input parameters for the spectra derived from these two models, complemented by an experimental validation using intermediate objects such as leeks and apple skins.