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Agriculture
Volume 15
Issue 16
10.3390/agriculture15161758
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Systematic Review

Overview of Artificial Intelligence Applications in Roselle (Hibiscus sabdariffa) from Cultivation to Post-Harvest: Challenges and Opportunities

by
Alfonso Ramírez-Pedraza
1,2,
Juan Terven
1,
José-Joel González-Barbosa
1,
Juan-Bautista Hurtado-Ramos
1,
Diana-Margarita Córdova-Esparza
3,*,
Francisco-Javier Ornelas-Rodríguez
1,
Raymundo Ramirez-Pedraza
4,
Julio-Alejandro Romero-González
3 and
Sebastián Salazar-Colores
5
1
Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional, Querétaro 76090, QRO, Mexico
2
Secretaría de Ciencia, Humanidades, Tecnología e Innovación SECIHTI, IxM, Mexico City 03940, Mexico
3
Facultad de Informática, Universidad Autónoma de Querétaro, Querétaro 76230, QRO, Mexico
4
Facultad de Contaduria y Administración, Universidad Autónoma de Querétaro, Querétaro 76017, QRO, Mexico
5
IA, Centro de Investigaciones en Óptica A.C., Loma del Bosque 115, León 37150, GTO, Mexico
*
Author to whom correspondence should be addressed.
Agriculture 2025, 15(16), 1758; https://doi.org/10.3390/agriculture15161758 (registering DOI)
Submission received: 18 June 2025 / Revised: 7 August 2025 / Accepted: 13 August 2025 / Published: 16 August 2025
(This article belongs to the Special Issue Computers and IT Solutions for Agriculture and Their Application)
Versions Notes

Abstract

Hibiscus sabdariffa (H. sabdariffa) is a high-value economic and functional crop, limited by agroclimatic conditions and low technological adoption. This systematic review examines the current state of artificial intelligence applications in agricultural management, analyzing 2111 records, selecting 82, and synthesizing 22 studies that meet the inclusion criteria. This review adopts a holistic framework aligned with three priority areas in agriculture—resource and climate management, crop productivity and quality, and sustainability—to explore how AI addresses key challenges in the cultivation and post-harvest processing of Hibiscus sabdariffa. The results show a predominance of classical machine learning techniques, with limited implementation of deep learning models. The most common applications include image classification, yield prediction, and analysis of bioactive compounds. However, limitations remain in the availability of open data, reproducible code, and standardized metrics. The narrative synthesis identified clear opportunities to integrate emerging technologies, such as deep neural networks and the Internet of Things (IoT), particularly in water management and stress monitoring. The review concludes that strengthening interdisciplinary research and promoting data openness is key to achieving a more resilient, sustainable, and technologically advanced crop.
Keywords: H. sabdariffa; artificial intelligence; deep learning; computer vision; precision agriculture; agricultural sustainability H. sabdariffa; artificial intelligence; deep learning; computer vision; precision agriculture; agricultural sustainability

Share and Cite

MDPI and ACS Style

Ramírez-Pedraza, A.; Terven, J.; González-Barbosa, J.-J.; Hurtado-Ramos, J.-B.; Córdova-Esparza, D.-M.; Ornelas-Rodríguez, F.-J.; Ramirez-Pedraza, R.; Romero-González, J.-A.; Salazar-Colores, S. Overview of Artificial Intelligence Applications in Roselle (Hibiscus sabdariffa) from Cultivation to Post-Harvest: Challenges and Opportunities. Agriculture 2025, 15, 1758. https://doi.org/10.3390/agriculture15161758

AMA Style

Ramírez-Pedraza A, Terven J, González-Barbosa J-J, Hurtado-Ramos J-B, Córdova-Esparza D-M, Ornelas-Rodríguez F-J, Ramirez-Pedraza R, Romero-González J-A, Salazar-Colores S. Overview of Artificial Intelligence Applications in Roselle (Hibiscus sabdariffa) from Cultivation to Post-Harvest: Challenges and Opportunities. Agriculture. 2025; 15(16):1758. https://doi.org/10.3390/agriculture15161758

Chicago/Turabian Style

Ramírez-Pedraza, Alfonso, Juan Terven, José-Joel González-Barbosa, Juan-Bautista Hurtado-Ramos, Diana-Margarita Córdova-Esparza, Francisco-Javier Ornelas-Rodríguez, Raymundo Ramirez-Pedraza, Julio-Alejandro Romero-González, and Sebastián Salazar-Colores. 2025. "Overview of Artificial Intelligence Applications in Roselle (Hibiscus sabdariffa) from Cultivation to Post-Harvest: Challenges and Opportunities" Agriculture 15, no. 16: 1758. https://doi.org/10.3390/agriculture15161758

APA Style

Ramírez-Pedraza, A., Terven, J., González-Barbosa, J.-J., Hurtado-Ramos, J.-B., Córdova-Esparza, D.-M., Ornelas-Rodríguez, F.-J., Ramirez-Pedraza, R., Romero-González, J.-A., & Salazar-Colores, S. (2025). Overview of Artificial Intelligence Applications in Roselle (Hibiscus sabdariffa) from Cultivation to Post-Harvest: Challenges and Opportunities. Agriculture, 15(16), 1758. https://doi.org/10.3390/agriculture15161758

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