Digital phenotyping of Butia capitata fruits for genetic improvement: a case study

The analysis of genetic variability in germplasm banks is essential for the success of breeding programs. However, such analyses traditionally require time, labor, and high financial resources. With the growing accessibility of equipment and decreasing costs of image acquisition and processing, image phenotyping emerges as a promising tool for carrying out fast, precise, and highly accurate assessments. This study aimed to assess the efficiency of digital image-based phenotyping in characterizing the genetic diversity of Butia capitata genotypes contributing to breeding and conservation strategies. Digital image analysis was applied to assess fruits from 59 genotypes, estimating the following parameters: area, perimeter, average, minimum and maximum radius, largest diameter, eccentricity, and colorimetric indices (L, a*, and b*) – L* represents luminosity, and a* and b* indicate chromaticity. The regression analysis between measurements obtained with a digital caliper and those generated by image processing resulted in a high coefficient of determination (r2 = 0.861), confirming the methodology’s practicality and accuracy. The significant genetic diversity observed among genotypes highlights their potential for targeted selection in breeding programs. Genotypes GN55, GN3, GN25, GN1, GN10, GN59, GN43, GN18, and GN13 stood out for producing larger fruits.