Research And Design Of Greenhouse Environment Comprehensive Detector And Its Evaluation Method

With the rapid development of modern agriculture,greenhouse planting has become an important direction of agricultural production development in China with its characteristics of high-efficiency production in the off-season.Greenhouse environment provides convenient conditions for the growth and development of crops,so it is very necessary to test and evaluate the environmental quality of greenhouse.At present,the detection of greenhouse environment mostly stays in single factor detection,and the greenhouse environment is a complex multi factor comprehensive microclimate environment,single factor detection cannot meet the comprehensive evaluation of greenhouse environment quality.Most of the methods of greenhouse environmental quality assessment are based on expert experience,which provides some reference for greenhouse environmental quality assessment,but this method has some limitations.Therefore,it is very important to study the new methods of greenhouse environmental quality assessment and design and develop a complete greenhouse comprehensive detection equipment.In this thesis,the complete greenhouse environment comprehensive detector design scheme is given.The main control terminal based on STM32 and the mobile acquisition terminal based on 51 single chip microcomputer realize real-time collection of greenhouse environment related data.At the same time,the main control terminal is also the core of data analysis and storage,algorithm implementation.In addition,the equipment realizes the tasks of function selection and real-time display through human-computer interaction.As a data localization and visualization platform,the PC software provides a data source and analysis platform for the further research of greenhouse environment.In addition to the design,this thesis completed the complete system hardware and software.The research on the assessment method of greenhouse environmental quality grade is an important work of this dissertation.Based on the probabilistic neural network,this thesis uses particle swarm optimization algorithm to optimize the key parameter smoothing factor of the probabilistic neural network,and in view of the insufficient differentiation of single value smoothing factor mode,puts forward the optimization strategy of multi value smoothing factor by class,which further improves the accuracy of the greenhouse environmental quality assessment model.In view of the situation that the probability neural network will become too complex with the increase of the number of samples,the kmeans clustering algorithm is introduced to reduce the complexity of the model.On the basis of the clustering center,the selection method of representative samples is given,and the network model is rebuilt..On the basis of reducing the complexity of the model,the classification accuracy of the model is improved,making it more suitable for the practical problem of greenhouse environmental quality level assessment.