A vision-based closed spirulina (a. platensis) cultivation system with growth monitoring using artificial neural network

• Main Authors: Aquino, Aaron U., Bautista, Ma. Veronica L., Diaz, Camille H., Valenzuela, Ira C., Dadios, Elmer P.
• Format: text
• Published: Animo Repository 2019
• Subjects: Spirulina; Microalgae; Neural networks (Computer science); Manufacturing
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1920
• Institution: De La Salle University

This paper presents the development of a closed algal cultivation system that automatically monitors and controls significant bio-environmental parameters to optimize the growth of the microalgae Spirulina platensis, which is normally used as fish feeds. The system is composed of three major parts: the detection system, which monitors the pH level, temperature and dissolved oxygen (DO) level; the correction system, which maintains the important parameters for optimum growth of the culture, 29 to 32 degree Celsius for temperature and 8.5 to 11 for pH; and the vision system which measures the cell density of the culture using an artificial neural network (ANN) model. The ANN model measures the cell density of the culture based on the RGB and lux values from the vision. The system then gives out a notification when the culture has reached its mature phase and is ready for harvesting. Based on the results of the experimental setups performed, the culture system was able to reach its matured phase on its 5th day for controlled and on the 7th day for uncontrolled. Furthermore, based on the regression analysis performed, the growth coefficient for the controlled set-up is 0.0519 and 0.0372 for the uncontrolled setup; the growth of Spirulina platensis has increased by 39.52% when the culture parameters are controlled. © 2018 IEEE.