Optimization of photosynthetic rate parameters using adaptive neuro-fuzzy inference system (ANFIS)

• Main Authors: Valenzuela, Ira C., Baldovino, Renann G., Bandala, Argel A., Dadios, Elmer P.
• Format: text
• Published: Animo Repository 2017
• Subjects: Carbon dioxide; Photosynthesis; Fuzzy logic; Lettuce—Effect of temperature on; Manufacturing
• Online Access: https://animorepository.dlsu.edu.ph/faculty_research/1720; https://animorepository.dlsu.edu.ph/context/faculty_research/article/2719/type/native/viewcontent
• Institution: De La Salle University

Crop growth is greatly affected by light intensity, temperature and CO2 concentration. The combinations of these factors are considered in growing crops. In this study, a system was developed using adaptive neuro-fuzzy inference system for the prediction of the photosynthetic rate of lettuce crop based on the temperature, light intensity and CO2. A fuzzy inference system is designed to generate the rules for the fuzzy logic where inputs of these are from the output of the trained neural network. Based on the result, the system was able to predict the photosynthetic rate of the lettuce crop based on the three input parameters. The RMSE value for the ANFIS model was found to be 2.7843e-05. © 2017 IEEE.