Application and assessment of FPGA-based filtering algorithms for enhancing measurement accuracy and reliability in greenhouse monitoring systems

• FPGA-based filtering algorithms enhanced greenhouse data accuracy and reliability. • Kalman filter achieved 0.356% error and 14× higher accuracy than baseline. • Parallel systolic-array design boosted computation speed by 28× on FPGA. • FPGA-based LMS adaptive filter reduced harmonic distortion by up to 99.85%. • Proposed FPGA solutions improved precision, efficiency, and stability in smart farming. Greenhouse monitoring systems require accurate and reliable processing of heterogeneous measurement signals, including sensor measurement signals and electrical signals, which are often affected by time-varying noise and disturbances. To address this unified signal processing challenge, this study proposes and validates an FPGA-based framework that integrates multiple filtering strategies to enhance measurement accuracy, reliability, and computational efficiency. To address the precision and reliability challenges present in greenhouse environmental monitoring and plant growth monitoring systems, three filtering methods—mean filter, median filter, and Kalman filter—were implemented and evaluated. Experimental results demonstrated that all three filters significantly improved measurement accuracy, with the Kalman filter yielding the most notable enhancement. Specifically, the Kalman filter improved measurement accuracy to 0.356%, representing a 14.045-fold increase compared to the baseline, significantly outperforming the mean filter (3.005-fold improvement) and median filter (2.354-fold improvement). Additionally, after Kalman filtering, 91.100% of the measurement errors fell within a ±1% probability density, indicating superior robustness and stability. To optimize hardware implementation of the Kalman filter, this study introduced an approach based on systolic array matrix multipliers and parallel pipeline design, achieving a 28.197-fold increase in computational speed. On FPGA, a parallel implementation using single-precision floating-point arithmetic maintained the same computational accuracy (0.356%) as conventional serial double-precision implementations, while delivering significant advantages in processing speed and resource utilization. For energy efficiency monitoring in greenhouses, an LMS adaptive filtering algorithm and its FPGA implementation were also proposed. This method effectively suppressed harmonic distortion in current signals, especially at harmonic frequencies of 150 Hz and 250 Hz, where attenuation reached 99.85% and 99.69%, respectively. The filtered signal closely approximated the ideal fundamental wave in both time and frequency domains, and the current signal error was reduced from 0.182 to 0.010, substantially improving signal reconstruction accuracy. In summary, the proposed FPGA-based Kalman filtering and LMS adaptive filtering schemes significantly improved the accuracy, reliability, and computational performance of greenhouse monitoring systems, demonstrating strong potential for application in greenhouse monitoring and intelligent agriculture.