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Data collected through IoT sensors, integrated with experimental data from traditional agricultural research, practical experience data from agricultural experts, and historical planting records, collectively constitute AI big data for plant growth. Data gathered from these diverse sources complement each other, providing a comprehensive profile of plant growth and forming a crucial component of big data. AI-driven data analysis and mining techniques are employed to extract valuable insights from vast amounts of plant growth data. Using statistical analysis methods, correlations between plant growth and environmental factors can be identified, such as the effect of temperature on flowering time. Machine learning algorithms, including decision trees and neural networks, are utilized to construct predictive models for plant growth, forecasting growth trends and yields under various environmental conditions. Data mining can also uncover hidden patterns and rules, providing scientific support for regulating environmental factors at different growth stages of various plants and informing agricultural decision-making.

IoT sensors continuously collect vast amounts of data across various plant growth scenarios. The data cover multiple aspects: environmental data such as temperature, humidity, light intensity, and carbon dioxide concentration; soil data including soil moisture, pH levels, and nutrient content; and plant-specific data such as plant height, leaf area, and chlorophyll content. Through these sensors, various parameters of the plant growth environment can be monitored and recorded in real-time and with high accuracy, providing a foundation for subsequent data analysis and regulation.

The AI automatic control system for growth environment parameters primarily consists of four modules: the data acquisition module, the data analysis module, the decision-making module, and the execution module. The data acquisition module utilizes IoT sensors to collect real-time data on plant growth environment parameters and plant physiological status. The data analysis module processes and analyzes the collected data, employing AI algorithms combined with big data mining to identify patterns and rules within the data, thereby providing optimal environmental reference data for the decision-making module. Based on the analysis results and predefined targets, the decision-making module formulates the optimal adjustment plan for environmental parameters. The execution module then controls the corresponding environmental regulation equipment according to the decision instructions, such as heaters, ventilation fans, CO₂ enrichment systems (activated when concentration falls below the requirements of the current growth stage), irrigation systems, and smart lighting (dynamically adjusting the red-blue light ratio based on PPFD - Photosynthetic Photon Flux Density), thereby achieving automatic regulation of the growth environment.

Large-scale models for various plant growth environment parameters possess multiple functions. The first is the growth prediction capability, which can forecast future growth trends and yields of plants based on current environmental parameters and plant growth status. The second is the environmental adaptability analysis function, which assesses the adaptability of different plants under various environmental conditions, providing a basis for plant introduction and cultivation area planning. The third function involves regulating environmental data for different plants at various growth stages. By analyzing plant growth data and environmental data, the models proactively predict the required parameter adjustments for the plant's environment and implement regulation. The fourth is the decision support function. Through the integration of the model with actual production, it provides decision-making recommendations for agricultural producers in areas such as optimizing planting plans, formulating fertilization and irrigation strategies, and selecting the timing for agricultural product sales.