Research On Intelligent Agricultural Greenhouse System Based On Convolution Neural Network And OpenMV

Modern agricultural greenhouses can provide suitable growing environments for various crops,greatly improving their quality and yield.However,factors such as pests and diseases and environmental parameters can have a huge impact on crop yields,making it particularly important for agricultural workers to manage pests and diseases and provide suitable growing environments.Currently,there are deficiencies in crop pest and disease management in China’s greenhouse industry,and environmental parameter management is also relatively lagging.Additionally,the level of intelligent information technology is seriously inadequate.These complex tasks cannot be accomplished by manual labor alone and cannot meet the requirements of modernizing the agricultural industry.To address the pain points of disease recognition and environmental regulation in agricultural fields,this paper proposes a lightweight deep neural network model for crop disease recognition that can be applied to micro-embedded terminals.The model is deployed on the OpenMV embedded mobile terminal,and the disease recognition terminal is used in a smart monitoring system suitable for modern greenhouses.Ultimately,this approach achieves dynamic management of smart greenhouse diseases and environmental parameters.The main contributions of this paper are:(1)Based on deep neural network,a lightweight crop disease identification model is proposed.Two lightweight pre-trained neural network models,MobileNetV2 and Shuffle Net,are selected,and the modified plant-village enhanced dataset is used for transfer learning.By comparing the performance of three optimizers(SGD,RMSProp,Adam)and two learning rate methods(fixed learning rate or cosine annealing dynamic learning rate),it is verified that the MobileNetV2 learning model with Adam optimizer and cosine annealing decay learning strategy performs better.(2)Apply the MobileNetV2 lightweight model to the OpenMV embedded image processing module.The image recognition system based on color threshold is built through the image color threshold tracking function of the OpenMV terminal,and the position of the pan-tilt and camera is changed with the help of the steering gear to identify the disease when the disease image within the color threshold range is captured,so as to improve the recognition accuracy under complex background and reducing the terminal resource waste.(3)Based on the STM32F4 main control chip,the integration of various environmental factors collection sensors has been completed,and a set of electrical equipment that can change the environmental factors of agricultural greenhouses has been designed,and the visual interaction with the One NET Internet of Things cloud platform and applet has been realized by using the Guanghetong 4G module.(4)Develop a visual interactive interface based on the One Net Internet of Things cloud platform,and develop a multi-service identification and supervision system for agricultural greenhouses based on We Chat applet to realize dynamic supervision of agricultural greenhouses with multi-services,multi-scenarios and multi-ways.