石斛农事实时监测数据

通过采集大棚种植的温度、湿度、气压、光照强度等数据，并实时分析管理和监控温度、湿度、光照强度等参数，对大棚种植石斛进行预警，并结合定期监测的数据，对石斛大棚种植技术和工艺进行调整和管理。该铁皮石斛的种植数据能作为标准数据，直接应用于其他地块种植参数的参考，从棚内温度、湿度的数据对照能够考量目前铁皮石斛的长势是否有影响，对其他铁皮石斛种植企业有重要的参考价值。数据采集自申请人自己的石斛种植棚，可实时采集棚内空气温度、空气湿度数据。
1.采集各个种植大棚中各季度平均温度和各季度平均湿度，并计算得到全年平均温度和全年平均湿度，同时控制大棚透光率处于同一或相近条件。
2.根据历史各季度平均温度和平均湿度和全年平均温度和全年平均湿度以及历史三年期铁皮石斛产量，自变量是空气温度和空气湿度，因变量是亩产量，回归模型为:Y=a+bT+cH，Y为每亩产量（kg），T是空气温度（℃），H是空气湿度（%）。
3.通过监测设备实时监测棚内温湿度，通过监测设备实时监测棚内温湿度，长期记录棚内空气温度和空气湿度数据，将空气温度和空气湿度数据带入回归模型得到预估产量（即数据结构中字段10产量），通过比较预估产量与历史产量来调控棚内温湿度，达到最佳产量。

This dataset is compiled from multi-dimensional monitoring data including air temperature, humidity, atmospheric pressure and light intensity collected from greenhouse cultivation scenarios. It supports real-time analysis, management and monitoring of core parameters such as temperature, humidity and light intensity to deliver early warnings for Dendrobium officinale cultivation in greenhouses. Combined with regularly acquired monitoring data, it can be utilized to adjust and optimize the technical protocols and management measures for Dendrobium officinale greenhouse planting.

The collected Dendrobium officinale planting data can be directly employed as standard reference data for formulating planting parameters of other cultivation plots. By comparing the recorded indoor temperature and humidity data, the potential impact on the current growth status of Dendrobium officinale can be assessed, which possesses significant reference value for other Dendrobium officinale cultivation enterprises. All data in this dataset is collected from the applicant's own Dendrobium planting greenhouses, which enables real-time acquisition of indoor air temperature and air humidity data.

1. Collect the quarterly average temperature and quarterly average humidity of each individual planting greenhouse, calculate the annual average temperature and annual average humidity, and maintain the light transmittance of all greenhouses at the same or similar levels.
2. Based on the historical quarterly average temperature, average humidity, annual average temperature, annual average humidity and three-year historical yield data of Dendrobium officinale, take air temperature and air humidity as independent variables, and yield per mu as the dependent variable. The established regression model is: Y = a + bT + cH, where Y refers to the yield per unit mu (kg), T represents air temperature (℃), and H stands for air humidity (%).
3. Monitor the temperature and humidity inside the greenhouse in real time through supporting monitoring equipment, conduct long-term recording of indoor air temperature and humidity data, substitute the collected temperature and humidity data into the aforementioned regression model to obtain the estimated yield (i.e., the yield field 10 in the data structure), and adjust and control the indoor temperature and humidity by comparing the estimated yield with historical yields to achieve the optimal cultivation yield.