Simulation of Cross-Regional Orchard Pest Monitoring and Early Warning Based on Internet of Things
Cross regional orchard pest and disease images require processing a large amount of data,and the quality of orchard pest and disease images may be blurred,dim,and other phenomena due to environmental and e-quipment factors,which reduces the efficiency of cross regional orchard pest and disease image monitoring and warn-ing.Therefore,a cross regional orchard pest and disease image monitoring and warning based on the Internet of Things is proposed.Firstly,a platform for monitoring and early warning was constructed by IoT technology.At the network layer of the IoT,the dark primary color priority algorithm and Gamma correction were utilized to enhance the monito-ring images.Subsequently,the Relief-F feature selection algorithm was employed to optimize and extract the features related to pests and diseases from orchard monitoring images.Furthermore,the Radial Basis Function(RBF)neural network method was adopted to complete the identification of pest and disease images in cross-regional orchard,thus achieving the final monitoring and early warning.The experimental results prove that the proposed method provides ef-fective monitoring and early warning for pest and disease images of cross-regional orchard.Meanwhile,the identifica-tion rate is up to 97%,and overall application performance is better.
Internet of Things IoTCross-regional orchardsImage enhancementRBF neural networkDisease and pest warning
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