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首页> 外文会议>IFIP(International Federation for Information Processing) TC 12 International Conference on Computer and Computing Technologies in Agriculture(CCTA 2007); 20070818-20; Wuyishan(CN) >A REGRESSION MODEL OF DRY MATTER ACCUMULATION FOR SOLAR GREENHOUSE CUCUMBER
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A REGRESSION MODEL OF DRY MATTER ACCUMULATION FOR SOLAR GREENHOUSE CUCUMBER

机译：日光温室黄瓜干物质积累的回归模型。

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The objective of this study is to develop a regression cucumber dry matter production model with a minimum number of parameters. Cucumber (Cucumis sativus L.) was cultivated in a soilless system with drip irrigation. The substrate was peat mixed with vermiculite. Five experiments were fulfilled totally in 3 different places in Beijing of China from 2004 to 2005. Cucumber growth data (dry matter weight of leaf, stem, fruit and petiole) were measured and environmental data (temperature, light intensity and day length) were collected. Data collected from 1 experiment in solar greenhouse was used to build the model, which was further validated with the data collected from other 4 experiments in solar greenhouse. A regression model for cucumber dry matter production was established. Based on Logistic curve, the time state variable was expressed as a logistic function about effective temperature accumulation (ETA) and effective light intensity accumulation (ELIA). ETA was defined as the sum of the temperature that was higher than physiological zero point in certain period, and ELIA was defined as the sum of the light intensity that was higher than light compensation point multiplied with time in certain period. Temperature, light intensity and day length were synthetically considered. The model had less state variables, and provided the relationships between the cucumber dry matter accumulation (DMA) per plant and environmental data (temperature, radiation and day length). The result of simulation was satisfied, because RMSE value was less than 6, and the R~2 value of the results was 0.99. It indicated that the regression model for cucumber dry matter production was reasonable and feasible.
机译：这项研究的目的是建立具有最少参数的回归黄瓜干物质生产模型。黄瓜（Cucumis sativus L.）通过滴灌在无土系统中种植。将底物与p石混合泥炭。 2004年至2005年，在中国3个不同地区共进行了5个实验。测量了黄瓜的生长数据（叶，茎，果实和叶柄的干重），并收集了环境数据（温度，光照强度和日长）。 。使用从日光温室中的1个实验收集的数据来构建模型，并使用从日光温室中的其他4个实验收集的数据进一步验证该模型。建立了黄瓜干物质生产的回归模型。基于Logistic曲线，时间状态变量表示为关于有效温度累积（ETA）和有效光强度累积（ELIA）的逻辑函数。 ETA被定义为在一定时期内高于生理零点的温度之和，而ELIA被定义为在一定时期内高于光补偿点的光强度与时间之和。综合考虑温度，光强度和日长。该模型具有较少的状态变量，并提供了每株植物黄瓜干物质积累（DMA）与环境数据（温度，辐射和天数）之间的关系。由于RMSE值小于6，并且R〜2值为0.99，因此仿真结果令人满意。表明黄瓜干物质生产的回归模型是合理可行的。

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