基于深度学习的水培植物病虫害防护系统的设计与实现OA

To meet the personalized demand for pest and disease protection in hydroponic plants,a hydroponic plant pest and dis-ease protection system based on deep learning and IoT technology was designed and implemented.The system consists of modules for plant status monitoring,environmental monitoring,and regulation maintenance.First,OpenCV was used to control the camera for captur-ing plant images and monitoring the condition of leaves in real time.Then,sensors were employed to monitor the plant growth environ-ment continuously.Next,deep learning methods were applied for preprocessing,feature extraction,and pest identification from plant dis-ease images.Finally,a servo mechanism was activated for pest control,while environmental data,pest images,and processing results were fed back to maintenance personnel.Test results demonstrate that the system achieves a pest identification accuracy of 92.3%with an average response time of less than 1.5 seconds,significantly improving the efficiency and accuracy of pest detection while effectively re-ducing labor costs.The system exhibits strong practical value and promising application prospects.