Research On Multi-sensor Information Fusion Algorithm In Greenhouse Intelligent Control

In the intelligent control of greenhouse,in order to accurately judge the environmental conditions in the greenhouse,it is necessary to use multi-sensor information fusion technology to synthesize the greenhouse environmental information,In order to provide the greenhouse managers with accurate greenhouse environment information,assist the greenhouse managers to infer the crop growth state and the next greenhouse control measures,and then provide a more ideal growth environment for greenhouse crops,so as to improve the yield and quality of greenhouse crops.Therefore,in view of the problem that the environmental parameters collected by sensors are interfered by external factors,which leads to the non-uniform distribution of the collected data,the following work is mainly done with the multi-sensor information fusion algorithm in the greenhouse intelligent control as the focus:(1)To solve the problem of abnormal data acquisition caused by equipment failure,external environment interference or human factors,an improved neural network prediction algorithm based on the Grubbs criterion was designed.Firstly,the detection value obtained by the sensor is divided into normal value and abnormal value by the Grubbs criterion.Then the improved neural network prediction model is used to train the normal value,and the trained network model is used to predict and replace the abnormal value in the measurement.Finally,the greenhouse environment information in the temperature room is calculated by the arithmetic average method.The experimental results show that the MAE value,MSE value and RMSE value of the improved neural network prediction model are lower than those of the same kind of prediction algorithm,and more close to the real value;the standard deviation of the algorithm is also lower than that of the uncorrected abnormal data,and the fusion result after preprocessing the abnormal data is more accurate than that of the uncorrected abnormal data.(2)To solve the problem that heterogeneous multisensor lacks effective information fusion model,an improved D-S evidence theory algorithm is designed to fuse heterogeneous multisensor information.Firstly,the consistency coefficient is introduced to represent the consistency between evidences,and the weight matrix of each proposition is obtained.Then,BPA of each focal element is redistributed,and a new evidence source is obtained.Then,the concept of credibility is introduced,and the synthesis rules are improved by using the credibility of evidence and the average support of the focal element of evidence,and the fusion results are obtained.The experimental results show that:compared with other methods,this method can solve the problems of D-S evidence theory in dealing with highly conflicting evidence to a certain extent,and the fusion result is more reasonable and the convergence speed is faster.(3)The information fusion model of multi-sensor information fusion algorithm in greenhouse intelligent control is designed.The improved neural network prediction algorithm based on Grubbs criterion and the improved D-S evidence theory algorithm are regarded as the core algorithms of local and global fusion centers respectively.At the same time,monitoring the environmental factors of Cucumber in greenhouse,and using this algorithm to carry out information fusion,so as to accurately judge the environmental conditions of greenhouse,and implement the corresponding control measures,so that the greenhouse cucumber can grow in the appropriate environment.