陕西省商洛市丹凤县香菇种植环境分析数据

采集香菇种植的土壤湿温度、光照、二氧化碳、空气温湿度等数据，这些数据不仅反映了土壤的健康状况与肥力水平，还揭示了外部气象条件对香菇生长环境的直接影响。通过实时监测与综合分析，种植者能够精准调控温室环境，确保土壤湿度适中、养分均衡，同时优化光照、通风与灌溉策略，以应对不同天气条件对香菇生长的潜在影响。此外，这些数据还为病虫害预警、产量预测及品质提升提供了科学依据，助力香菇种植实现智能化、精细化管理，最终提升经济效益与生态效益。1.数据采集：本系统通过空气温湿度传感器、土壤温湿度传感器等物联网设备，结合4G/5G、Wi-Fi与有线网络，实时采集种植环境中的空气温湿度、土壤温湿度、土壤PH、光照、二氧化碳、PM值等多维数据。 2.算法规则：系统采用环境参数评分算法，对环境数据进行评分。基于作物生长理想条件（如空气温湿度、土壤温湿度、土壤PH、光照、二氧化碳、PM值等），并通过以下公式计算：环境参数评分=100-Σ(w_i×|当前值_i-理想值_i|/容差_i)其中，Σ表示对所有参数的累加，w_i是第i个参数的权重。当前值_i是第i个参数的实际测量值，理想值_i是第i个参数的理想值。容差_i是第i个参数的允许波动范围。权重、理想值和容差范围设定基于历史数据分析以及实际种植经验的确定。对作物生长影响较大的参数获得较高的权重，容差范围则考虑到环境因素的波动性，针对作物对不同环境变化的耐受性设定进行适当设定，环境参数偏离理想值越多，扣分越大，以空气湿度为例，其权重为2，理想值设定为75，容差范围为±5，扣分计算如下：空气湿度扣分=2×|95.9-75|/5=2×4.18=8.36。根据这些评分生成具体的环境优化方案。

This dataset collects multi-dimensional data including soil temperature and humidity, light intensity, carbon dioxide concentration, air temperature and humidity during shiitake mushroom cultivation. These data not only reflect soil health status and fertility level, but also reveal the direct impact of external meteorological conditions on the growth environment of shiitake mushrooms.
Through real-time monitoring and comprehensive analysis, growers can precisely regulate the greenhouse environment, ensure appropriate soil moisture and balanced nutrients, and optimize light, ventilation and irrigation strategies to cope with the potential impacts of different weather conditions on shiitake mushroom growth.
In addition, these data provide scientific basis for pest and disease early warning, yield prediction and quality improvement, helping shiitake mushroom cultivation realize intelligent and refined management, and ultimately improve economic and ecological benefits.
1. Data Collection: This system adopts IoT devices including air temperature and humidity sensors, soil temperature and humidity sensors, and combines 4G/5G, Wi-Fi and wired networks to collect multi-dimensional environmental data in real time, such as air temperature and humidity, soil temperature and humidity, soil pH, light intensity, carbon dioxide concentration, PM concentration and other indicators.
2. Algorithm Rules: The system adopts an environmental parameter scoring algorithm to score the collected environmental data. Based on the ideal growth conditions of the crop (including air temperature and humidity, soil temperature and humidity, soil pH, light intensity, carbon dioxide concentration, PM concentration, etc.), the score is calculated via the following formula:
Environmental parameter score = 100 - Σ(w_i × |current value_i - ideal value_i| / tolerance_i)
where Σ denotes the summation over all parameters, w_i is the weight of the i-th parameter, current value_i is the actual measured value of the i-th parameter, ideal value_i is the ideal set value of the i-th parameter, and tolerance_i is the allowable fluctuation range of the i-th parameter.
The weights, ideal values and tolerance ranges are determined based on historical data analysis and actual cultivation experience. Parameters with greater impacts on crop growth are assigned higher weights. The tolerance ranges are appropriately set considering the volatility of environmental factors and the crop's tolerance to different environmental changes. The more the environmental parameters deviate from the ideal values, the more points will be deducted. Taking air humidity as an example, its weight is 2, the ideal value is set to 75, and the tolerance range is ±5. The deduction for air humidity is calculated as follows:
Air humidity deduction = 2 × |95.9 - 75| / 5 = 2 × 4.18 = 8.36.
Specific environmental optimization schemes are generated based on the calculated scores.