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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

A Modular Framework for RGB Image Processing and Real-Time Neural Inference: A Case Study in Microalgae Culture Monitoring

by
José Javier Gutiérrez-Ramírez
1,
Ricardo Enrique Macias-Jamaica
2,
Víctor Manuel Zamudio-Rodríguez
1,
Héctor Arellano Sotelo
1,
Dulce Aurora Velázquez-Vázquez
3,
Juan de Anda-Suárez
4 and
David Asael Gutiérrez-Hernández
1,*
1
División de Estudios de Posgrado e Investigación, Tecnológico Nacional de México/IT de León, León 37290, Gto, Mexico
2
Departamento de Ingeniería Química, Tecnológico Nacional de México en Celaya, Celaya 38010, Gto, Mexico
3
Facultad de Arquitectura, Universidad La Salle Bajío, Av. Universidad 602. Col. Lomas del Campestre, León 37150, Gto, Mexico
4
Departamento de Ingeniería Mecatrónica, Tecnológico Nacional de México/ITS de Purísima del Rincón, Purísima del Rincón 36400, Mexico
*
Author to whom correspondence should be addressed.
Eng 2025, 6(9), 221; https://doi.org/10.3390/eng6090221
Submission received: 1 July 2025 / Revised: 15 August 2025 / Accepted: 22 August 2025 / Published: 2 September 2025
(This article belongs to the Special Issue Artificial Intelligence for Engineering Applications, 2nd Edition)
Versions Notes

Abstract

Recent progress in computer vision and embedded systems has facilitated real-time monitoring of bioprocesses; however, lightweight and scalable solutions for resource-constrained settings remain limited. This work presents a modular framework for monitoring Chlorella vulgaris growth by integrating RGB image processing with multimodal sensor fusion. The system incorporates a Logitech C920 camera and low-cost pH and temperature sensors within a compact photobioreactor. It extracts RGB channel statistics, luminance, and environmental data to generate a 10-dimensional feature vector. A feedforward artificial neural network (ANN) with ReLU activations, dropout layers, and SMOTE-based data balancing was trained to classify growth phases: lag, exponential, and stationary. The optimized model, quantized to 8 bits, was deployed on an ESP32 microcontroller, achieving 98.62% accuracy with 4.8 ms inference time and a 13.48 kB memory footprint. Robustness analysis confirmed tolerance to geometric transformations, though variable lighting reduced performance. Principal component analysis (PCA) retained 95% variance, supporting the discriminative power of the features. The proposed system outperformed previous vision-only methods, demonstrating the advantages of multimodal fusion for early detection. Limitations include sensitivity to lighting and validation limited to a single species. Future directions include incorporating active lighting control and extending the model to multi-species classification for broader applicability.
Keywords: computer vision; machine learning; bioprocess monitoring computer vision; machine learning; bioprocess monitoring

Share and Cite

MDPI and ACS Style

Gutiérrez-Ramírez, J.J.; Macias-Jamaica, R.E.; Zamudio-Rodríguez, V.M.; Sotelo, H.A.; Velázquez-Vázquez, D.A.; de Anda-Suárez, J.; Gutiérrez-Hernández, D.A. A Modular Framework for RGB Image Processing and Real-Time Neural Inference: A Case Study in Microalgae Culture Monitoring. Eng 2025, 6, 221. https://doi.org/10.3390/eng6090221

AMA Style

Gutiérrez-Ramírez JJ, Macias-Jamaica RE, Zamudio-Rodríguez VM, Sotelo HA, Velázquez-Vázquez DA, de Anda-Suárez J, Gutiérrez-Hernández DA. A Modular Framework for RGB Image Processing and Real-Time Neural Inference: A Case Study in Microalgae Culture Monitoring. Eng. 2025; 6(9):221. https://doi.org/10.3390/eng6090221

Chicago/Turabian Style

Gutiérrez-Ramírez, José Javier, Ricardo Enrique Macias-Jamaica, Víctor Manuel Zamudio-Rodríguez, Héctor Arellano Sotelo, Dulce Aurora Velázquez-Vázquez, Juan de Anda-Suárez, and David Asael Gutiérrez-Hernández. 2025. "A Modular Framework for RGB Image Processing and Real-Time Neural Inference: A Case Study in Microalgae Culture Monitoring" Eng 6, no. 9: 221. https://doi.org/10.3390/eng6090221

APA Style

Gutiérrez-Ramírez, J. J., Macias-Jamaica, R. E., Zamudio-Rodríguez, V. M., Sotelo, H. A., Velázquez-Vázquez, D. A., de Anda-Suárez, J., & Gutiérrez-Hernández, D. A. (2025). A Modular Framework for RGB Image Processing and Real-Time Neural Inference: A Case Study in Microalgae Culture Monitoring. Eng, 6(9), 221. https://doi.org/10.3390/eng6090221

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