Application of adaptive GA-BPNN based on weibull distribution for autonomous greenhouse ventilation.

The agricultural sector confronts escalating challenges, including population growth, climate change, and constrained land resources. Addressing these issues requires agricultural methods to evolve into more intelligent, sustainable, and efficient practices in order to satisfy the expanding global demand. In this context, the smart greenhouse plays a crucial role in contemporary smart agriculture by amalgamating diverse technologies and equipment to establish an optimal environment for plant cultivation. This paper aims to create an enhanced algorithm for ventilation control utilizing artificial intelligence technology. The goal is to achieve intelligent ventilation and air exchange in greenhouses while ensuring that optimal air conditions for crop growth are maintained consistently. Employing data from the Shouguang Vegetable High-Tech Demonstration Park, we collect and analyze historical and real-time data within a glass greenhouse. We establish the foundational algorithm for autonomous ventilation control using an adaptive genetic algorithm-back propagation neural network. A hybrid fitness scaling approach, combining linear and nonlinear fitness scaling, is proposed and implemented. In the pursuit of a well-fitted model, three models i.e., multiple regression (MR), back propagation neural network (BPNN), and genetic algorithm - back propagation neural network (GA-BPNN) are explored in experiments to fit greenhouse data. These models have been validated through extensive simulation experiments, and the results revealed that our method outperforms the investigated techniques in terms of errors. This paper concludes that effective ventilation control algorithms enable precise regulation of greenhouse environmental factors, including temperature, humidity, and CO2, thereby optimizing crop yield and quality.