| Autor: |
García-Rodríguez LDC; Department of Electrical and Electronic Engineering, Tecnológico Nacional de México, Celaya 38010, Guanajuato, Mexico., Prado-Olivarez J; Department of Electrical and Electronic Engineering, Tecnológico Nacional de México, Celaya 38010, Guanajuato, Mexico., Guzmán-Cruz R; Cuerpo Académico de Ingeniería de Biosistemas, Universidad Autónoma de Querétaro, Queretaro 76010, Queretaro, Mexico., Heil M; Centro de Investigación y de Estudios Avanzados, Instituto Politécnico Nacional, Irapuato 36824, Guanajuato, Mexico., Guevara-González RG; Cuerpo Académico de Ingeniería de Biosistemas, Universidad Autónoma de Querétaro, Queretaro 76010, Queretaro, Mexico., Diaz-Carmona J; Department of Electrical and Electronic Engineering, Tecnológico Nacional de México, Celaya 38010, Guanajuato, Mexico., López-Tapia H; Department of Electrical and Electronic Engineering, Tecnológico Nacional de México, Celaya 38010, Guanajuato, Mexico., Padierna-Arvizu DJ; Department of Electrical and Electronic Engineering, Tecnológico Nacional de México, Celaya 38010, Guanajuato, Mexico., Espinosa-Calderón A; Regional Center for Optimization and Development of Equipment, Tecnológico Nacional de México, Celaya 38020, Guanajuato, Mexico. |
| Abstrakt: |
Photosynthesis is a vital process for the planet. Its estimation involves the measurement of different variables and its processing through a mathematical model. This article presents a black-box mathematical model to estimate the net photosynthesis and its digital implementation. The model uses variables such as: leaf temperature, relative leaf humidity, and incident radiation. The model was elaborated with obtained data from Capsicum annuum L. plants and calibrated using genetic algorithms. The model was validated with Capsicum annuum L. and Capsicum chinense Jacq. plants, achieving average errors of 3% in Capsicum annuum L. and 18.4% in Capsicum chinense Jacq. The error in Capsicum chinense Jacq. was due to the different experimental conditions. According to evaluation, all correlation coefficients ( Rho ) are greater than 0.98, resulting from the comparison with the LI-COR Li-6800 equipment. The digital implementation consists of an FPGA for data acquisition and processing, as well as a Raspberry Pi for IoT and in situ interfaces; thus, generating a useful net photosynthesis device with non-invasive sensors. This proposal presents an innovative, portable, and low-scale way to estimate the photosynthetic process in vivo, in situ, and in vitro, using non-invasive techniques. |
