Design And Research Of Intelligent Greenhouse Monitoring System Based On Hadoop

As the world's number one developing country,and a traditional agricultural power,China has made considerable progress in most areas.With the advent of the era of information technology development,traditional agriculture can no longer meet the needs of modern agriculture,and high human and material resources are all challenges for the agricultural industry.Therefore,how to improve the scientific planting of crops under the current circumstances and how to accurately obtain information on crop growth,and to mine scientific planting methods from the massive data generated during the crop growth process have also become current hot topics.In view of the above problems,the traditional planting industry can be combined with the current information technology to form an intelligent and scientific planting method,which can complete the collection and processing of data during sowing,planting,and collection,and scientifically and reasonably during the planting process.The use of automated equipment instead of traditional manual planting can greatly reduce the consumption of manpower and material resources.In this project,the monitoring and adjustment of the environment in the greenhouse is completed through an infinite sensor network,and the agricultural big data collection and historical data analysis of the planting process are completed through Hadoop distributed storage and processing.The reasonable planting production conditions are derived,and based on data mining The technology completes the association rules mining of historical data,and obtains a high-quality planting environment.This article introduces a monitoring system for the greenhouse environment based on the Hadoop distributed processing model.The overall structure mainly includes the information acquisition part,main controller part,control device part,wireless sensor network part,Hadoop architecture part,data processing part,and human-computer interaction.The system can monitor the greenhouse environmental parameters(such as temperature,humidity,light intensity,gas concentration,etc.)in real time,and can intelligently control related equipment automatically or through a software system according to the set upper and lower limits of the environmental indicators.The environmental parameters of the livestock and poultry houses are controlled within a set range to achieve the purpose of increasing crop yields and scientific production.In the distributed processing structure of Hadoop,a method based on the mapreduce architecture is designed to complete the data mining of production data through the apriori algorithm.In order to solve the problem that the original Apriori algorithm scans the entiredatabase on frequent itemsets to waste time and space,Frequent item set mining algorithm(FIM)to reduce the number of itemsets,and on the basis of optimization of parallel algorithms and methods for specific cluster partitions,experiments using greenhouse vegetable growth environment data and yield data,the improved algorithm is superior in terms of average time complexity Due to the original algorithm,the average running time of the improved algorithm is only 62.5% of the original algorithm.Experiments show that the new algorithm is suitable for large-scale data mining,especially when the number of candidates and the number of transactions is large,and the algorithm performance has been significantly improved.After various optimizations and system software design,the modified system has the characteristics of low power consumption,good stability,high migration,good adaptability and high degree of automation.The staff can complete the environmental monitoring,data collection and analysis in the greenhouse through the PC terminal,and adjust the various parameters to build a scientific planting environment,and initially realize agricultural modernization and agricultural precision.