Study On Growth Environmental And Pathological Monitoring Of Patchouli Based On Internet Of Things And Deep Learning

Under the background of biology and medicine,more efforts to promote the large-scale cultivation of medicinal plants has greater practical significance.Using computer technologies such as Internet of Things(Io T)and deep learning to realize automatic management and cultivation of medicinal plants is an effective way to optimize the cultivation of medicinal plants.Since the concept of the Io T was proposed,it has gained extensive attention from various industries,providing a foundation for the development of Io T in many fields.In Plant monitoring,Io T has covered more and more fields,including greenhouse planting,climate factor collection,climate prediction.The development of the Io T also offers solutions for the medicinal plant field in terms of increasing yields and increasing industry benefits.In addition,the improvement of the hardware level also provides the conditions for Io T and various computer technologies including deep learning to be combined with Biomedical field.In this paper,a monitoring method for the growth of medicinal plant Patchouli was proposed In biology and medicine.Based on Io T technology(Zig Bee)and deep learning technology(YOLOv5),a feasible and comprehensive monitoring scheme for the growth of patchouli was designed.In the whole monitoring system,the hardware part mainly relies on CC2530 core and ESP8266 WIFI module,and carries out data transmission,storage and peripheral control based on Zig Bee wireless transmission protocol,TCP/IP and other network protocols.The monitoring system carries out visual data transmission through PC and One NET cloud platform,and realizes automatic management functions such as temperature and humidity compensation and light compensation.In addition,an early warning system for patchouli spot blight was designed based on multiple-level data fusion and multiple loss optimization of YOLOv5 s.The system monitors the leaves of patchouli through deep learning technology and notifies the management personnel by means of buzzer alarm and log upload.The experiment proved that the improved early warning model could predict the situation of patchouli spot blight more effectively.After that,this paper tested the related functions of the designed system and verified its effectiveness in the monitoring of patchouli planting.Finally,based on the design and implementation of the growth monitoring solution of patchouli,this paper summarizes the basic content of the solution and looks forward to the development prospect of computer technology in the planting and cultivation of medicinal plants...