FPGA-based smart sensor for drought stress detection in tomato plants using novel physiological variables and...

Area of Science:

• Plant Physiology
• Agricultural Engineering
• Signal Processing

Background:

• Soil drought is a major threat to crop yield and quality, potentially leading to total crop loss.
• Early detection and quantification of drought stress are essential for effective crop management.
• Controlled drought can enhance specific plant characteristics, necessitating precise monitoring.

Purpose of the Study:

• To evaluate the efficacy of monitoring plant physiological processes via gas exchange methodology for drought stress detection.
• To develop and test a smart sensor system utilizing Field Programmable Gate Arrays (FPGAs) for real-time drought monitoring.
• To apply digital signal processing techniques, specifically Discrete Wavelet Transform (DWT), to analyze plant responses to drought.

Main Methods:

• Utilized a gas exchange methodology to monitor plant physiological processes under controlled drought conditions.
• Employed smart sensors based on Field Programmable Gate Arrays (FPGAs) for data acquisition.
• Applied Discrete Wavelet Transform (DWT) for digital filtering and analysis of physiological signals.
• Implemented an index-based methodology to mitigate spatial variations within the greenhouse environment.

Main Results:

• The developed system successfully detected drought stress conditions in plants.
• Discrete Wavelet Transform (DWT) effectively filtered high-frequency noise from physiological signals.
• The index-based methodology ensured that detected differences were independent of internal greenhouse climate variations.
• The monitoring system proved capable of quantifying the impact of drought stress.

Conclusions:

• Monitoring plant physiological processes using gas exchange and smart sensor technology is a viable method for detecting drought stress.
• Digital signal processing, particularly DWT, enhances the reliability of drought detection by noise reduction.
• The proposed system offers a robust solution for identifying and quantifying drought stress, aiding in crop management and potentially improving crop quality under specific stress conditions.