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Volume 18
Issue 8
10.3390/su18083928
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

CARYPAR: A Multimodal Decision-Support Framework Integrating Satellite Bio-Environmental Reanalysis and Proximal Edge-Intelligence for Hylocereus spp. Health Monitoring

by
Carlos Diego Rodríguez-Yparraguirre
1,
Abel José Rodríguez-Yparraguirre
2,*,
Cesar Moreno-Rojo
2,
Wendy Akemmy Castañeda-Rodríguez
3,
Iván Martin Olivares-Espino
1,
Andrés David Epifania-Huerta
4,
María Adriana Vilchez-Reyes
5,
Dany Paul Gonzales-Romero
4,
Enrique Jannier Boy-Vásquez
6 and
Wilson Arcenio Maco-Vasquez
1
1
Graduate School, Universidad Nacional de Trujillo, Trujillo 130101, La Libertad, Peru
2
Department of Agroindustry and Agronomy, Faculty of Engineering, Universidad Nacional del Santa, Nuevo Chimbote 02712, Ancash, Peru
3
Doctoral Program in Agro-Industrial Engineering, Specialization in Advanced Processing of Andean Grains and Tubers, Universidad Nacional del Santa, Nuevo Chimbote 02712, Ancash, Peru
4
Graduate School, Universidad Nacional de Barranca, Barranca 15321, Lima, Peru
5
Facultad Ciencia de la Salud, Escuela Profesional de Enfermería, Universidad Católica Los Ángeles de Chimbote, Chimbote 02804, Ancash, Peru
6
Graduate School, Universidad Privada de Trujillo, Trujillo 13001, La Libertad, Peru
*
Author to whom correspondence should be addressed.
Sustainability 2026, 18(8), 3928; https://doi.org/10.3390/su18083928
Submission received: 7 March 2026 / Revised: 6 April 2026 / Accepted: 13 April 2026 / Published: 15 April 2026
(This article belongs to the Section Sustainable Agriculture)
Versions Notes

Abstract

Pitahaya (Hylocereus spp.) production is increasingly affected by climatic factors, as well as by phytopathogens and abiotic stress, leading to delays in agronomic interventions and reduced productivity. The objective was to design, implement, and validate a multimodal system (CARYPAR) that enables early disease detection and agile decision-making, characterized by low latency and reduced dependence on cloud connectivity. The methodology integrates climate reanalysis from NASA POWER, biophysical remote sensing variables derived from Sentinel-1/2, and proximal computer vision captured via mobile devices using a late fusion architecture and an optimized convolutional neural network, EfficientNet-V2B0, which discriminates between optimal and pathological conditions in vegetative tissues and fruit. The results of the experimental validation carried out in 160 georeferenced units achieved an overall accuracy of 80.0% and an F1 score of 0.8645 for Bad Fruit. The McNemar test and the operational agreement with agro-industrial experts yielded a Cohen’s Kappa index of κ = 0.6831, with an inference latency reduced to 22.00 ms. It is concluded that the multimodal integration of satellite bio-environmental data with edge computer vision achieves substantial agreement with agronomic expert judgment under heterogeneous field conditions (Cohen’s κ = 0.6831), supporting its role as a decision-support tool rather than a replacement for expert assessment. Therefore, its adoption can enhance real-time irrigation management and crop protection, while contributing to traceability and sustainable resource management in agricultural regions with limited connectivity.
Keywords: precision agriculture; deep learning; phytosanitary monitoring; climate reanalysis; SAR backscatter; vegetation indices; sustainable farming; digital transformation precision agriculture; deep learning; phytosanitary monitoring; climate reanalysis; SAR backscatter; vegetation indices; sustainable farming; digital transformation

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MDPI and ACS Style

Rodríguez-Yparraguirre, C.D.; Rodríguez-Yparraguirre, A.J.; Moreno-Rojo, C.; Castañeda-Rodríguez, W.A.; Olivares-Espino, I.M.; Epifania-Huerta, A.D.; Vilchez-Reyes, M.A.; Gonzales-Romero, D.P.; Boy-Vásquez, E.J.; Maco-Vasquez, W.A. CARYPAR: A Multimodal Decision-Support Framework Integrating Satellite Bio-Environmental Reanalysis and Proximal Edge-Intelligence for Hylocereus spp. Health Monitoring. Sustainability 2026, 18, 3928. https://doi.org/10.3390/su18083928

AMA Style

Rodríguez-Yparraguirre CD, Rodríguez-Yparraguirre AJ, Moreno-Rojo C, Castañeda-Rodríguez WA, Olivares-Espino IM, Epifania-Huerta AD, Vilchez-Reyes MA, Gonzales-Romero DP, Boy-Vásquez EJ, Maco-Vasquez WA. CARYPAR: A Multimodal Decision-Support Framework Integrating Satellite Bio-Environmental Reanalysis and Proximal Edge-Intelligence for Hylocereus spp. Health Monitoring. Sustainability. 2026; 18(8):3928. https://doi.org/10.3390/su18083928

Chicago/Turabian Style

Rodríguez-Yparraguirre, Carlos Diego, Abel José Rodríguez-Yparraguirre, Cesar Moreno-Rojo, Wendy Akemmy Castañeda-Rodríguez, Iván Martin Olivares-Espino, Andrés David Epifania-Huerta, María Adriana Vilchez-Reyes, Dany Paul Gonzales-Romero, Enrique Jannier Boy-Vásquez, and Wilson Arcenio Maco-Vasquez. 2026. "CARYPAR: A Multimodal Decision-Support Framework Integrating Satellite Bio-Environmental Reanalysis and Proximal Edge-Intelligence for Hylocereus spp. Health Monitoring" Sustainability 18, no. 8: 3928. https://doi.org/10.3390/su18083928

APA Style

Rodríguez-Yparraguirre, C. D., Rodríguez-Yparraguirre, A. J., Moreno-Rojo, C., Castañeda-Rodríguez, W. A., Olivares-Espino, I. M., Epifania-Huerta, A. D., Vilchez-Reyes, M. A., Gonzales-Romero, D. P., Boy-Vásquez, E. J., & Maco-Vasquez, W. A. (2026). CARYPAR: A Multimodal Decision-Support Framework Integrating Satellite Bio-Environmental Reanalysis and Proximal Edge-Intelligence for Hylocereus spp. Health Monitoring. Sustainability, 18(8), 3928. https://doi.org/10.3390/su18083928

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