Design And Development Of Intelligent Orchard Cloud Platform Based On Internet Of Things Technology

Based on the Internet of Things technology,this article builds an orchard intelligent cloud platform,presenting Internet of Things data through visual screens with information such as environmental monitoring,disease and pest monitoring,and agricultural machinery management,so as to timely and accurately understand the overall situation of the park and effectively formulate follow-up response plans.The relevant research results are expected to provide technical reference for various forms of intelligent agricultural platforms.The specific research content of this article is as follows:(1)Research on integrated communication of Internet of Things devices.From a comprehensive consideration of data accuracy,compatibility,reliability,cost,and other aspects,select appropriate equipment for environmental monitoring,agricultural machinery positioning,pest detection,and develop integrated communication interfaces to achieve the collection of environmental sensing information,agricultural machinery positioning information,and camera monitoring information.Develop a data transmission method based on 5G and Wi Fi,and compress the transmitted data through the zlib library in Python software,Helping to reduce the time and bandwidth required for transmission,the priority setting method is used to optimize the transmission algorithm of the Internet of Things gateway,which can effectively improve the overall speed of the system and solve problems such as dynamic feedback lag in traditional agricultural mode information management platforms.(2)Intelligent identification model construction of typical orchard pests.In order to solve the problem of insect pests during the cultivation and growth of fruit trees and improve the quality of fruit,an intelligent identification model for pear fruit borers based on deep learning was developed,with the typical orchard pest,the pear fruit borer,as the research object.Based on the obtained pest samples,a dataset was constructed,and the Mask R-CNN model of convolutional neural networks was selected to identify the insect pests.The established model was trained,with the undetected rate and the misjudgment rate as evaluation indicators,Compared with the traditional Faster R-CNN model,Mask R-CNN has an average undetected rate of 0.3%,an average false positive rate of 0.76%,and an average accuracy of 98.87%.Compared with traditional methods,the accuracy rate has been improved by 9.27%,enabling more accurate identification of pests,and has significant advantages in pest detection.(3)Design and develop a smart orchard cloud platform.Based on the functional and performance requirements of the Smart Orchard cloud platform,the Java platform was selected as the software development environment,and the platform architecture was designed using the B/S mode.My SQL database was used as the data processing foundation.Functional modules for environmental monitoring,agricultural machinery and equipment management,and pest identification of the cloud platform were designed and developed.System maintenance function development and access permissions were set to ensure the safe operation of the system with high performance.