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

Alfonso Ramírez-Pedraza, 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
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Alfonso Ramírez-Pedraza: Centro de Investigación en Ciencia Aplicada y Tecnología Avanzada, Instituto Politécnico Nacional, Querétaro 76090, QRO, 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

Agriculture, 2025, vol. 15, issue 16, 1-46

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 (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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