Design and Realization of IoT-Based Intelligent Environmental Control System for Tomato Production in Multi-Span Greenhouses

In the field of facility agriculture, intelligent transformation has become a key path to address labor shortages, low resource utilization efficiency, and the need for precise environmental regulation. This study focuses on developing a low-cost and highly scalable tomato greenhouse intelligent control platform, dedicated to achieving multi-source data fusion processing and unmanned production mode. The platform architecture relies on IoT sensing technology to build a perception layer, which collects environmental parameters in real-time through various sensors deployed in the greenhouse, such as temperature, humidity, and light intensity, utilizing a cloud edge collaborative control mechanism to ensure efficient data processing and rapid response, achieving precise control of the greenhouse environment. In terms of decision support, the neural network algorithm is introduced to conduct in-depth analysis and decision-making on collected data, optimize resource allocation, and improve tomato yield and quality. This platform proposes a dynamic fusion model of multi-source data, which can integrate multiple pieces of information from different sensors and historical databases, providing a comprehensive basis for precise regulation. The platform adopts a modular and scalable architecture, which facilitates flexible expansion of functional modules or integration of new sensor devices according to actual production needs, ensuring the long-term applicability and maintainability of the system. A set of resource optimization control strategies has been developed to minimize energy consumption and resource waste while ensuring a suitable tomato growth environment, and to improve the economic benefits and sustainable development capabilities of facility agriculture. The research results provide effective technical solutions for the intelligent upgrading of facility agriculture, promoting the development of tomato production towards high efficiency, precision, and intelligence.