Research On Diseases Monitoring And Recognition System Of Greenhouse Plant

Modern greenhouse,as an indispensable agricultural production technology for agricultural planting technology,plays an increasingly important role in the application of anti-season vegetable and fruit planting,so the research of greenhouse is indispensable.In addition,intelligent greenhouse can improve plants growth environment effectively,prevent crop diseases timely and improve crop yield.Therefore,it has a high practical value to research automatic monitoring and disease identification of intelligent greenhouse.In this paper,a greenhouse disease monitoring and disease identification system was constructed based on monitoring of plants disease.Nowadays many plant monitoring systems are 24-hour monitoring nowadays,but plant diseases do not always occur,so it is resulting in waste of memory resources.Therefore,this paper uses OpenMV intelligent monitoring module to design a system of multi-color threshold automatic recognition and acquisition of disease images.The system is controlled to keep turning by the steering engine controlled the platform continuously.When the disease image is monitored,the system starts to collect.This paper took tomato disease leaves as the research object,and constructed an intelligent monitoring and recognition system.classified and identified three tomato diseases(gray mold,powdery mildew,late blight)and normal leaves each 100 and a total of 400 images.Firstly,the clustering algorithm based on K-means initialization GMM model was used to remove the complex background of 400 diseases images consisted of three tomato diseases(gray mold,powdery mildew,late blight)and normal leaves.Then HSI transform was used to segment the lesion location by chroma range and color features and texture features were extracted.Finally,this paper combined Relief F to select good classification features,and aiming at the shortcomings of cuckoo search(CS)such as slow convergence rate and easy to fall into local optimum in the late stage,combining with the back propagation characteristics of BP algorithm,this paper proposed an adaptive step CS algorithm and BP cooperative search algorithm(ASCS-BPCA)to identify tomato plant diseases.The performance of ASCS-BPCA classifier was compared with that of traditional CS-BP algorithm,and the simulation experiment table was showed that the performance of the optimization algorithm was improved,and the validity of RF and the limitation of PCA in simplified model application in this paper were verified.the automatic monitoring system and the new technology cloud storage were combined to build the monitoring platform of the Internet of Things to realize the remote monitoring of disease images.The established disease identification model was effective and feasible,and could realize network remote sharing and function of seeking expert diagnostic type of diseases,meet the application requirements of remote monitoring and disease identification of modern greenhouse.