Hyperspectral technology and machine learning models to estimate the fruit quality parameters of mango and strawberry crops.

ABSTRACT: Using chemical laboratory procedures to estimate the fruit quality parameters (biochemical parameters) of mango "Succarri" and strawberry "Florida" as indicators of ripening degrees in a large area presents challenges such as low throughput, labor intensity, time consumption, and the need for multiple samples. So, using spectral reflectance-based proximal remote sensing to quickly and accurately measure biochemical parameters in different fruits is important to find the best time to harvest, make food ripen faster, and the processing of food easier. This has significant economic and ecological advantages. The objective of this study was to evaluate the biochemical parameters of mango and strawberry fruits at various ripening stages. This was done by utilizing a combination of established a

SUBMITTER: Elsayed S 

PROVIDER: S-EPMC11813159 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature