春秋茬温室番茄光合速率预测模型通用性研究

Photosynthetic Rate Prediction of Tomato under Greenhouse Condition in Spring and Autumn Growth Period

基于无线传感器网络,建立了春秋茬温室番茄光合速率预测模型。在2014年秋季与2015年春季,采用无线传感器网络自动获取温室环境因子信息,包括空气温湿度、土壤温湿度、光强与CO_2浓度。同时采用LI-6400XT型光合仪测定植物的单叶净光合速率,利用叶室小环境来扩展数据范围。将采集到的温室环境信息作为输入参数,单叶净光合速率作为输出参数,利用神经网络建立番茄光合速率预测模型。为了提高模型的预测精度,首先使用Z分数对输入参数进行标准化,然后对标准化后的数据进行主成分分析;其次,根据各主成分的累积贡献率选取主成分,然后经过K折交叉检验后建立神经网络预测模型。结果表明,采用2014年秋季数据建立的预测模型,相关系数为0.99;2015年春季为0.95;用两季数据联合建立的通用模型,相关系数为0.85。利用春秋茬联合数据建立的温室番茄光合速率预测模型通用性较好,可以为日光温室CO_2气肥精细调控提供理论支持。

Photosynthesis is the basis of plant growth and photosynthetic rate directly affecting the quality of fruit.The quantity and quality of tomato can be improved with the application of the appropriate amount of CO_2,which is one of the principal raw material of photosynthesis.In this paper,photosynthetic rate prediction models under greenhouse condition in spring and autumn growth period were established respectively.The experimental data were collected during autumn of 2014 and spring of2015.WSN was used to monitor greenhouse environmental parameters in real time,including air temperature,air humidity,CO_2 concentration,soil temperature,soil moisture,and light intensity.An LI-6400 XT portable photosynthesis analyzer was used to measure the photosynthetic rate of tomato plants,and the environmental information of leaves was controlled by small chamber environment.In order to verify the universality of the established model,three models using the data from both spring and autumn growth period,data only from spring growth period,and the data only from autumn growth period were established.The photosynthetic rate prediction models of single leaf were established based on the back propagation(BP) neural network.The environmental parameters were used as input neurons and the photosynthetic rate was taken as the output neuron.In order to improve the prediction accuracy of the model,the input neurons were standardized using Z score method and then processed by principal component analysis.Principal components were selected according to the principal components 'cumulative contribution rate.The photosynthetic rate prediction models of single leaf were establishedafter principal components analysis and K-fold cross validation.The results indicated that the correlation coefficient of photosynthesis prediction model based on the data of spring 2015,autumn 2014 and the two seasons were 0.99,0.95 and 0.85 respectively.The results of the models indicated that the universality of the model built using data from both seasons,and it has great potential for CO_2 fertilizer control.