## 1 Introduction
designed a digital PID controller to maintain constant hot water temperature in storage tanks [22], demonstrating significant improvements in control speed and accuracy through the reduction in random errors in temperature measurement. However, the tuning of PID controller parameters is critical to its performance. Conventional empirical or formula-based methods are often time-consuming and lack sufficient precision, particularly in adapting to complex environmental variations. Recent advancements in intelligent PID control have opened new research directions for parameter optimization. [PERSON] et al. integrated fuzzy control with PID algorithms (fuzzy PID) to develop a temperature control system for calf milk replacer solutions [23]. This hybrid approach effectively addressed system uncertainties and nonlinearities by combining the adaptive parameter tuning capability of fuzzy logic with the precision of PID algorithms. [PERSON] et al. implemented a fuzzy PID-based temperature controller in a multi-channel circulating biomass hot air furnace [24], achieving enhanced efficiency and quality in drying processes through fuzzy logic-driven adaptive PID parameter adjustment. However, the critical parameters of fuzzy controllers are typically determined through engineering experience and remain static post-calibration, leaving room for further optimization. [PERSON] et al. applied Particle Swarm Optimization (PSO) to fuzzy PID controllers (PSO-Fuzzy PID) in litchi steam preservation equipment [25], demonstrating superior performance in disturbance adjustment time and overshoot control compared to conventional methods. Nevertheless, the inherent tendency of PSO towards local optima compromises global optimization accuracy. [PERSON] et al. optimized fuzzy PID temperature control in vacuum annealing furnaces using Genetic Algorithms (GA-Fuzzy PID) [26], achieving notable improvements in control precision, response time, and overshoot reduction, albeit with compromised real-time performance due to GA's computational complexity. [PERSON] et al. proposed a Whale Optimization Algorithm (WOA)-enhanced fuzzy PID controller for compressor speed regulation in cabin temperature control systems [27], which exhibited exceptional control performance but faced challenges in convergence speed and sensitivity to initial parameter configurations. The Dung Beetle Optimizer (DBO), proposed by [PERSON] et al. in 2022 [28], represents a bio-inspired optimization algorithm based on the social behaviors of dung beetle populations. Distinct from conventional optimization algorithms, DBO demonstrates superior convergence speed, precision, and stability [29]. [PERSON] et al. devised an integrated renewable energy system for power generation and green hydrogen production [30], employing DBO to determine the optimal capacity configuration. The optimized system achieved a reduced levelized cost of energy, establishing its competitiveness for large-scale hydrogen production. [PERSON] et al. developed an automatic throttle control system to regulate well-head backpressure during drilling operations [31], where DBO was utilized to fine-tune PID controller parameters, thereby enhancing safety in oil and gas resource extraction. [PERSON] et al. integrated DBO with support vector machines (DBO-SVR) for soil nutrient prediction [32], demonstrating its adaptability to machine learning applications. Based on the above research, DBO has demonstrated significant potential across cost optimization, industrial control, and machine learning domains. However, its application to nutrient solution temperature control optimization remains underexplored, warranting further investigation to address this research gap. This study developed a nutrient solution temperature control system based on an adaptive DBO-fuzzy PID controller for summer hydroponic lettuce cultivation.