Genetic improvement of papaya (Carica papaya L.) and fruit quality: a review of integrated digital phenotyping, physicochemical, and sensory approaches

Abstract

Papaya (Carica papaya L.) is a crop of great economic importance. However, factors such as low genetic variability, abiotic stresses, and technological limitations compromise fruit productivity and quality. In this context, the use of modern tools, such as digital phenotyping, combined with genetic improvement, has stood out in the search for more productive cultivars with improved postharvest quality. The objective of this study was to conduct a comprehensive literature review on papaya cultivation, addressing its main agronomic aspects, as well as postharvest evaluation methods, with an emphasis on digital image-based phenotyping techniques applied to fruit quality analysis. The study was based on scientific publications selected from the Scopus, SciELO, and Google Scholar databases, mainly covering the period from 2014 to 2024. Studies related to papaya cultivation, genetic improvement, digital phenotyping, sensory analysis, and physicochemical evaluation of fruits were included, as well as classical references relevant to the topic. The literature indicates that papaya breeding depends on the exploitation of genetic variability present in germplasm banks, aiming at the development of cultivars with superior agronomic traits and improved fruit quality. Digital phenotyping stands out as an efficient tool for collecting and analyzing phenotypic data, allowing greater precision, cost reduction, and optimization of the selection process. The integration of digital phenotyping with physicochemical and sensory analyses enables the identification of genotypes with higher productive potential and fruits with better consumer acceptance.

Plant phenotyping relevance

デジタル画像ベースの植物表現型解析を果実品質評価に適用する方法を中心に扱うレビューであり、植物フェノタイピング手法レビューとして適格。

Code and data availability

This is a narrative literature review of papaya breeding and digital phenotyping. It presents no original phenotype datasets, images, code, models, or supplements, and contains no availability statements or URLs for paper-specific assets. All referenced works are cited prior publications.

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