Department of Electrical and Electronic Engineering, Tecnológico Nacional de México, Celaya 38010, Guanajuato, Mexico.
Cuerpo Académico de Ingeniería de Biosistemas, Universidad Autónoma de Querétaro, Queretaro 76010, Queretaro, Mexico.
Sensors (Basel). 2022 Jul 14;22(14):5275. doi: 10.3390/s22145275.
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 L. plants and calibrated using genetic algorithms. The model was validated with L. and Jacq. plants, achieving average errors of 3% in L. and 18.4% in Jacq. The error in Jacq. was due to the different experimental conditions. According to evaluation, all correlation coefficients () 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.
光合作用是地球生命的基础。对其的估算涉及多种变量的测量和通过数学模型进行处理。本文提出了一种用于估算净光合作用的黑盒数学模型及其数字实现。该模型使用叶片温度、叶片相对湿度和入射辐射等变量。该模型是根据 L. 植物获得的数据详细阐述的，并使用遗传算法进行了校准。该模型在 L. 和 Jacq. 植物上进行了验证，在 L. 上的平均误差为 3%，在 Jacq. 上的平均误差为 18.4%。Jacq. 的误差归因于不同的实验条件。根据评估，所有相关系数（）都大于 0.98，这是与 LI-COR Li-6800 设备比较的结果。数字实现包括用于数据采集和处理的 FPGA，以及用于物联网和现场接口的 Raspberry Pi，从而生成了一种具有非侵入式传感器的有用的净光合作用设备。本研究提出了一种创新的、便携式的、小规模的方法，用于使用非侵入式技术在体内、原位和体外估算光合作用过程。
