杨凌农科城设施农业环境调控优化参数数据集

本数据集以杨凌农科城日光温室与塑料大棚为研究对象，整合温度、湿度、光照、CO₂浓度、土壤含水量、电导率等分钟级传感器数据及农事操作记录，经过多因子耦合与深度加工，形成“逆境场景—调控处方”型诊断数据集。每条记录包含光温协同类型、根际舒适度指数、水肥边际效益指数、CO₂增施需求等衍生指标，并附带边际产量响应判定与具体调控动作组合。数据集采用决策导向的编排逻辑，将原始环境参数转化为可直接支撑智能决策的调控单元，实现了从数据记录到知识驱动的本质转变。This dataset takes solar greenhouses and plastic greenhouses in Yangling Agricultural Science City as its research subjects. It integrates minute-level sensor data including temperature, humidity, light intensity, CO₂ concentration, soil moisture content, electrical conductivity, and farm operation records. After multi-factor coupling and in-depth processing, it forms a "stress scenario-regulation prescription" diagnostic dataset. Each record contains derived indicators such as light-temperature synergy type, rhizosphere comfort index, water-fertilizer marginal benefit index, and CO₂ supplementary application demand, and is accompanied by marginal yield response judgment and specific regulation action combinations. The dataset adopts a decision-oriented orchestration logic, converting original environmental parameters into regulation units that can directly support intelligent decision-making, thus realizing the essential transformation from mere data recording to knowledge-driven applications.

搜集汇总

背景与挑战

背景概述

该数据集聚焦于杨凌农科城的日光温室与塑料大棚，整合了分钟级的温度、湿度、光照、CO₂浓度、土壤含水量、电导率等传感器数据及农事操作记录，通过多因子耦合与深度加工，构建了'逆境场景—调控处方'型诊断结构。每条记录包含光温协同类型、根际舒适度指数等衍生指标，并附带产量响应判定与调控动作组合，实现了从原始环境参数到智能决策支撑的转化，为设施农业环境优化提供知识驱动解决方案。