To achieve the chlorophyll fluorescence image acquisition system for plants grown in greenhouse and accurately predict the chlorophyll fluorescence kinetic parameters, this paper built a prototype plant chlorophyll fluorescence image acquisition system by: i) utilizing computer machine vision technology to test the RGB color component, HSV indexes and the GRAY value of the marked leaves, ii) then modelling these image characteristics with chlorophyll fluorescence kinetics parameters via a) artificial neural network (ANN), b) support vector machine (SVM) and c) partial least squares regression (PLSR) methods. We analyzed and compared the prediction accuracy of the three models to fluorescence kinetic parameters, respectively, with different inputs of RGB with GRAY and HSV with GRAY. Our results showed that the three models could accurately predict the Y (II), ETR, qL, NPQ and Fv/Fm parameters, and with input of RGB and GRAY, the prediction efficiency of the three models is generally superior to that with input of HSV and GRAY. It was the SVM model with input of RGB and GRAY that had the best prediction efficiency, with the correlation coefficient R of the Y (II), ETR, qL, NPQ and Fv/Fm between predicted values and the real values were: 0.935, 0.941, 0.994, 0.987 and 0.941, the mean square deviation RMSE were: 0.013, 0.100, 0.036, 0.023 and 0.025. Our study indicated that the chlorophyll fluoresces image acquisition system could quickly and efficiently obtain and predict the plant leaf chlorophyll fluorescence image information and the chlorophyll fluorescence kinetic parameters as well, resulting in the monitoring and forecasting of plant health, plant environmental adaptation and plant photosynthetic performance.

25 feature parameters were extracted from chlorophyll fluorescence image of pepper leaf, including 18 parameters which was significantly correlated with the nitrogen content at the 0.01 level. Principal component analysis (PCA) was used to extract the main parameters as input variables of genetic algorithm to optimize back–propagation artificial neural network (BPNN), generalized regression neural network (GRNN) and multiple linear regression (MLR), to establish the forecast model of hot pepper leaf nitrogen content, respectively. The correlation coefficient of three model set were 0.959 2, 0.963 3, 0.943 5, and correlation coefficient of prediction set were 0.914 5, 0.821 3, 0.774 1, respectively.

Nitrogen is a vital element in crops, and its management is a key measure to improve their yield and quality. Real-time monitoring of crop nitrogen content is needed to optimize products and crop production. In this paper we use chlorophyll fluorescence source to obtain pepper leaf image for machine vision and digital image processing and establish a greenhouse pepper leaf nitrogen content rapid non-destructive prediction model. Our results show: i) the 25 chlorophyll fluorescence image characteristic parameters of pepper seedling stage, flowering stage and fruiting stage were significantly related to 18 levels of nitrogen content and 0.01 characteristic parameters, ii) using principal component analysis (PCA) we extracted respectively the main components of the three periods for the model of input variables, iii) using genetic algorithm we optimized back-propagation artificial neural network (BPNN), partial least squares (PLS) and multiple linear regression (MLR) provision method to establish the prediction model of hot pepper leaf nitrogen content. Our conclusions include: i) the correlation coefficients of BPNN and MLR all reached 0.94, ii) the prediction set coefficient was over 0.8, which has a good effect, iii) the effect of PLS model and prediction is not better than MLR and BPNN, iv) optimal prediction model for BPNN model, the modeling correlation coefficient of seedling stage, flowering period and fruiting stage were 0.962, 0.978, 0.945, iv) the correlation coefficient of prediction were 0.908, 0.906 and 0.905, v) compared to the linear regression method like MLR and PLS, nonlinear regression method BPNN was more applicable to establish pepper leaf nitrogen content prediction model, vi) it can reach the desired accuracy and provides an important reference for greenhouse crop nutrition regulation.

针对春冬季节温室内CO2浓度低下的问题，采用风送式CO2气体补偿装置，调控温室环境CO2浓度，提高光合作用速率。以补偿时间为输入量，补偿效果和补偿速率为输出量，建立补偿时间和补偿量的线性关系，通过定时定压的“多次少量施放，超过上限停施”的方式向温室补充CO2，调控温室环境中的CO2含量，以达到精确补偿的效果。结果表明，向温室一次补偿5 min的CO2气体，室内CO2平均浓度在15 min内由205μmol/mol达到540μmol/mol，间歇补偿3次，室内 CO2平均浓度在45 min内由205μmol/mol 达到1200μmol/mol。说明补偿时间与其补偿量呈线性关系，标准偏差在0~3.03%范围内，实现了温室CO2快速精确补偿的功能。

[目的]考查在温室中采用不同水压下喷雾降温的效果.[方法]以可编程逻辑控制器、变频器、水泵、触控系统和多路传感器为硬件平台,设计了温室变频雾化装置.试验探索5个不同喷雾频率下,系统的实际降温效果,并在此基础上提出了适合试验温室的喷雾压力.[结果]喷雾压力低于110 kPa(对应喷雾频率为31 Hz)时,降温幅度随着压力的增加而升高,但当喷雾压力大于110 kPa时,随着压力的增加降温效果趋于平滑,而温室内湿度随着压力的增加不断增加,喷头的流量随着压力的升高而近似线性增加.[结论]综合考虑温度和湿度2个因素,该试验温室的最适应喷雾为压力为100～120 kPa.