河南省荥阳市番茄种植环境分析数据

采集番茄种植的土壤湿温度、空气温湿度、二氧化碳、光照强度等数据，全面评估不同环境条件对番茄生长的具体影响，如确定适宜的温度区间以促进番茄果实更好地膨大，明确合适的光照强度和时长以提升光合作用效率，进而为番茄的健康生长提供有力保障。利用这些数据构建番茄品质与环境参数关联的深度网络模型，深入探索何种环境条件组合可以使番茄具有更高的甜度、更优的色泽和更好的口感等，为显著提升番茄品质奠定基础。基于数据驱动的方式，为番茄种植环境的智能调控提供有效的支持，依据实时数据自动调整灌溉量、通风强度以及遮阳程度等，为番茄创造适宜的生长环境，提高种植效率和产量。将优化后的环境调控经验推广应用至不同地区的番茄种植中，形成多维度细粒度的感知与控制模式，有力推动番茄种植产业朝着科学、高效、模式可移植的方向发展，全面提升整个产业的竞争力。1.数据采集：本系统通过空气湿温度传感器、光照传感器、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湿温度、光照、土壤湿温度、二氧化碳等多维数据。 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（如温度、湿度、光照等），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，以土壤湿度为例，其权重为2.5，理想值设定为70%RH，容差范围为±10%RH，扣分计算如下：土壤湿度扣分=2.5×|13.6-70|/10=2.5×5.64=14.1。根据这些评分生成具体的环境优化方案。

This dataset collects data such as soil temperature and moisture, air temperature and humidity, carbon dioxide, light intensity during tomato planting, to comprehensively evaluate the specific impacts of various environmental conditions on tomato growth. Specifically, it aims to identify suitable temperature ranges to promote better fruit enlargement of tomatoes, define appropriate light intensity and duration to enhance photosynthetic efficiency, thereby providing robust support for the healthy growth of tomatoes. Using these collected data, a deep neural network model correlating tomato quality and environmental parameters is built to further explore which combination of environmental conditions can endow tomatoes with higher sweetness, better color and superior taste, laying a solid foundation for significantly improving tomato quality. Adopting a data-driven approach, this work provides effective support for intelligent regulation of tomato planting environments, automatically adjusting irrigation volume, ventilation intensity, shading degree and other relevant parameters based on real-time data to create optimal growth conditions for tomatoes, and ultimately improve planting efficiency and yield. By promoting the optimized environmental regulation experience to tomato planting practices in different regions, a multi-dimensional and fine-grained perception and control model is established, effectively promoting the tomato planting industry to develop towards a scientific, efficient and mode-transplantable direction, and comprehensively enhancing the overall competitiveness of the industry.

1. Data Collection: This system collects multi-dimensional real-time data of the planting environment, including air temperature and humidity, light intensity, soil temperature and moisture, carbon dioxide and other relevant parameters, via IoT devices such as air temperature and humidity sensors, light sensors and soil temperature and moisture sensors, combined with 4G/5G, Wi-Fi and wired networks.

2. Algorithm Rules: The system applies an environmental parameter scoring algorithm to evaluate the collected environmental data. The calculation is based on the ideal growth conditions of crops (such as temperature, humidity, light intensity, etc.) using the following formula:
Environmental Parameter Score = 100 - Σ(w_i × |Current Value_i - Ideal Value_i| / Tolerance_i)
Where: Σ represents the sum 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 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 planting experience. Parameters that have 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 greater the deviation of an environmental parameter from its ideal value, the more penalty points will be deducted. Taking soil moisture as an example: its weight is 2.5, the ideal value is set to 70% RH, and the tolerance range is ±10% RH. The penalty points for soil moisture are calculated as follows:
Penalty Points for Soil Moisture = 2.5 × |13.6 - 70| / 10 = 2.5 × 5.64 = 14.1
Specific environmental optimization schemes are generated based on the calculated scores.