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

Author

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  • Alfonso Ramírez-Pedraza

    (Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional, Querétaro 76090, QRO, Mexico
    Secretaría de Ciencia, Humanidades, Tecnología e Innovación SECIHTI, IxM, Mexico City 03940, Mexico)

  • Juan Terven

    (Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional, Querétaro 76090, QRO, Mexico)

  • José-Joel González-Barbosa

    (Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional, Querétaro 76090, QRO, Mexico)

  • Juan-Bautista Hurtado-Ramos

    (Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional, Querétaro 76090, QRO, Mexico)

  • Diana-Margarita Córdova-Esparza

    (Facultad de Informática, Universidad Autónoma de Querétaro, Querétaro 76230, QRO, Mexico)

  • Francisco-Javier Ornelas-Rodríguez

    (Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional, Querétaro 76090, QRO, Mexico)

  • Raymundo Ramirez-Pedraza

    (Facultad de Contaduria y Administración, Universidad Autónoma de Querétaro, Querétaro 76017, QRO, Mexico)

  • Julio-Alejandro Romero-González

    (Facultad de Informática, Universidad Autónoma de Querétaro, Querétaro 76230, QRO, Mexico)

  • Sebastián Salazar-Colores

    (IA, Centro de Investigaciones en Óptica A.C., Loma del Bosque 115, León 37150, GTO, Mexico)

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.

Suggested Citation

  • Alfonso Ramírez-Pedraza & Juan Terven & José-Joel González-Barbosa & Juan-Bautista Hurtado-Ramos & Diana-Margarita Córdova-Esparza & Francisco-Javier Ornelas-Rodríguez & Raymundo Ramirez-Pedraza & Jul, 2025. "Overview of Artificial Intelligence Applications in Roselle (Hibiscus sabdariffa) from Cultivation to Post-Harvest: Challenges and Opportunities," Agriculture, MDPI, vol. 15(16), pages 1-46, August.
    • Handle: RePEc:gam:jagris:v:15:y:2025:i:16:p:1758-:d:1725948
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    References listed on IDEAS

    as
    1. Gyung Doeok Han & GyuJin Jang & Jaeyoung Kim & Dong-Wook Kim & Renato Rodrogues & Seong-Hoon Kim & Hak-Jin Kim & Yong Suk Chung, 2021. "RGB images-based vegetative index for phenotyping kenaf (Hibiscus cannabinus L.)," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-15, September.
    2. Abdoudramane Sanou & Kiessoun Konate & Roger Dakuyo & Kaboré Kabore & Hemayoro Sama & Mamoudou Hama Dicko, 2022. "Hibiscus sabdariffa: Genetic variability, seasonality and their impact on nutritional and antioxidant properties," PLOS ONE, Public Library of Science, vol. 17(3), pages 1-15, March.
    3. Dania Tamayo-Vera & Xiuquan Wang & Morteza Mesbah, 2024. "A Review of Machine Learning Techniques in Agroclimatic Studies," Agriculture, MDPI, vol. 14(3), pages 1-19, March.
    4. José-Joel González-Barbosa & Alfonso Ramírez-Pedraza & Francisco-Javier Ornelas-Rodríguez & Diana-Margarita Cordova-Esparza & Erick-Alejandro González-Barbosa, 2022. "Dynamic Measurement of Portos Tomato Seedling Growth Using the Kinect 2.0 Sensor," Agriculture, MDPI, vol. 12(4), pages 1-24, March.
    5. David A. Vargas & Nathaly Vargas & Andrea M. Osorio-Doblado & Juan A. Ruano-Ortiz & Fábio G. M. de Medeiros & Roberta T. Hoskin & Marvin Moncada, 2024. "Valorization of Hibiscus Flower ( Hibiscus sabdariffa L.) Anthocyanins to Produce Sustainable Spray-Dried Ingredients," Sustainability, MDPI, vol. 16(13), pages 1-12, June.
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