Integrating colorimetry and machine learning: an approach for optimizing fruit selection in Licania tomentosa seedling production

Abstract

Licania tomentosa is a widely distributed species in Brazil, commonly used in urban landscaping and environmental restoration. Despite its potential, understanding the relationship between fruit maturation and seedling quality remains limited. This study aimed to evaluate the relationship between maturation stages - classified by epicarp coloration - and seedling performance through RGB colorimetric analysis, fruit morphometry, and the application of machine learning algorithms. Fruits were collected from mother trees and classified into four color stages based on the Munsell color chart. Digital images were analyzed to extract RGB values and morphometric parameters of the fruits using ImageJ® software. Subsequently, seedling emergence, biometric attributes, biomass accumulation, and the Dickson Quality Index (DQI) were evaluated. Yellow-Red fruits produced seedlings with higher emergence rates, greater shoot and root biomass accumulation, and higher DQI values, indicating greater seedling vigor. In contrast, Greenish Green-Yellow fruits resulted in less vigorous seedlings. The Red band was the main indicator of changes in the fruits. Morphometric parameters alone were insufficient to discriminate the maturation stages. Linear Discriminant Analysis correctly classified 90.48 % of the fruits according to their maturation stage. The integration of colorimetric data with machine learning proved to be an effective, non-destructive, and low-cost approach for optimizing seed selection. To enhance the predictive accuracy of the model it is recommended to expand the dataset under natural conditions and explore alternative color systems and complementary fruit traits.