Abstract: To address the prevalent issues of water resource wastage and extensive management in the irrigation of solar greenhouses in Shandong, a low-cost intelligent irrigation system for small-scale farmers was designed. The system used an STM32 microcontroller as its core controller and established a four-layer cooperative architecture encompassing perception, transmission, edge, and cloud layers. By integrating multi-source environmental sensing, edge computing, and cloud platform big data analytics, it achieved intelligent irrigation decision-making and precise integrated regulation of water and fertilizer based on crop growth models and real-time environmental status. Field trial results showed that the monitoring accuracies of the intelligent irrigation system for air temperature, relative humidity, soil temperature, and soil moisture were ±0.5 ℃, ±3%, ±0.5 ℃, and ±3%, respectively. Compared with traditional manual irrigation, the system reduced water usage by 32.4%, fertilizer application by 31.6%, labor cost by 81.5%, and electricity consumption by 6.5%. In terms of increasing yield and improving quality, tomato yield increased by 15.4%, while fruit sugar content and vitamin C content increased by 15.3% and 13.6%, respectively, and the rate of malformed fruit decreased by 50.0%. The intelligent irrigation system operated stably, was easy to deploy, and demonstrated good economic benefits and promotion value.

Key words: low-cost, internet of things, big data, solar greenhouse, intelligent irrigation system, design, application