Image processing for fruit maturity detection: A systematic review
DOI:
https://doi.org/10.37711/repiama.2025.2.1.3Keywords:
agricultura automatizada, agricultura inteligente, visión artificial, inteligencia artificial, análisis digitalAbstract
The present study offers a systematic review on maturity detection in fruits using Artificial Intelligence (AI). For this purpose, 15 original articles were selected from the Scopus database during the period 2019 and 2024. Multiple aspects were evaluated including the fruits studied, the physical characteristics considered, the AI algorithms employed, their accuracy and the challenges faced. It was found that, although apple is the most researched fruit, others such as orange, tomato and strawberry also receive significant attention. Coloration and texture emerge as primary indicators of maturity, supported by the common use of algorithms such as CNN, SVM and ANN that show high levels of accuracy. However, challenges such as variability in the characteristics of fruits and lack of labeled quality data persist. In addition, ethical and social concerns related to agricultural automation are identified. Key areas for future research are highlighted as mitigating variability in fruit characteristics, improving data quality and understanding the behavior of AI models.
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Copyright (c) 2025 Richard Injante, Gian Rios-Trigoso, Segundo Ramírez-Shupingahua, Katterine Tejada Shupingahua (Autor/a)
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