Black-Box Mathematical Model for Net Photosynthesis Estimation and Its Digital IoT Implementation Based on...

Area of Science:

• Plant Physiology
• Biophysics
• Agricultural Engineering

Background:

• Accurate estimation of net photosynthesis is crucial for understanding plant health and productivity.
• Traditional methods can be complex and require specialized equipment.
• Developing efficient and portable tools for photosynthesis measurement is an ongoing challenge.

Purpose of the Study:

• To present a black-box mathematical model for estimating net photosynthesis.
• To detail the digital implementation of this model for practical applications.
• To validate the model's accuracy using different plant species.

Main Methods:

• Development of a black-box mathematical model using leaf temperature, relative humidity, and incident radiation.
• Calibration of the model using genetic algorithms with data from Capsicum annuum plants.
• Validation of the model with Capsicum annuum and Capsicum chinense plants.
• Digital implementation using FPGA for data acquisition and Raspberry Pi for IoT interfaces.

Main Results:

• The model achieved an average error of 3% for Capsicum annuum and 18.4% for Capsicum chinense.
• High correlation coefficients (Rho > 0.98) were obtained when compared to LI-COR Li-6800 equipment.
• The digital implementation resulted in a portable device for non-invasive photosynthesis estimation.

Conclusions:

• The developed black-box model and its digital implementation provide an innovative, portable, and low-cost method for estimating net photosynthesis.
• The system is suitable for in vivo, in situ, and in vitro measurements using non-invasive sensors.
• Further validation under diverse experimental conditions is recommended for broader applicability, especially for species like Capsicum chinense.