树莓派自动灌溉系统：基于MobileNetV2模型的植物图像识别

This project utilizes the MobileNetV2 model for image recognition of plants, achieving automation in a Raspberry Pi automatic irrigation system. Throughout the implementation of this project, we utilized a range of tools and techniques, including OpenCV and Raspberry Pi GPIO control.

We encountered several technical challenges during the project. Firstly, we needed to choose appropriate sensors to detect air temperature and humidity, considering their accuracy and reliability. Secondly, we had to write code to control the water pump and analyze the sensor readings, as well as design a suitable circuit to connect the Raspberry Pi and water pump.

However, we faced these challenges head-on, learning on the go and continuously experimenting to solve problems. Firstly, we trained and recognized plant images using the MobileNetV2 model, which has high recognition accuracy and low computational complexity, making it suitable for deployment on embedded devices like Raspberry Pi. We deployed the MobileNetV2 model on the Raspberry Pi and used OpenCV to preprocess and recognize images captured by the camera.

Secondly, we utilized the GPIO control functionality of the Raspberry Pi to control the automatic irrigation system. When the system detects that the plant needs watering, it automatically turns on the water pump until the soil moisture reaches the set threshold, then automatically turns off the water pump. This effectively prevents overwatering and underwatering, improving plant growth efficiency and quality.

Finally, we tested and optimized the project. Through recognition tests of different plant species' images, we found the MobileNetV2 model to be highly accurate. We also tested and optimized the response speed and water pump control precision of the automatic irrigation system, ensuring its stability and reliability.

Overall, this project was a highly beneficial experience. Through practice, we learned how to build an automation system using Raspberry Pi and gained basic knowledge of sensors and circuits. We believe that the implementation of this project will greatly help us in our future studies and work.

This project fully showcases the advantages of the MobileNetV2 model on embedded devices, achieving an intelligent automatic irrigation system. Additionally, this project has some promotional value, providing useful references and inspiration to fields like agriculture production.