INTELLIGENT PLANT STATE RECOGNITION SYSTEM
The article deals with the development of intelligent systems for plant state recognition based on computer vision methods and tools. The article contains a review of automatic plant cultivation systems. It provides data on a number of practically applied systems, including Growing Underground, Spread, Click & Grow, foot containers, and Pascal monitoring solution. It also makes an overview of classes of image recognition methods. It describes the developed computer vision-based intelligent system. The system is designed to assess the effectiveness of the considered approach to plant state control automation. The system is a client-server system. To monitor the condition of plants, we developed an experimental sample of the installation, which is a rectangular frame with a camera and led strips for lighting control attached. The strips and camera are connected to the Wi-Fi module connecting the installation to the server. JavaScript programming language was chosen for implementation of the system software. To implement the internal logic of the client side, TypeScript is used. For user interface development, Electron and Vue.js frameworks were applied. The use of these technologies allows you to make a cross-platform application compilable for different operating systems, such as Windows, LINUX, and MacOS. The Visual Studio Code system was used as the development environment. In addition, for the system development, computer vision library OpenCV was used, containing implementation of algorithms and methods of image processing and recognition. To use the library under the Node.js platform we applied the opencv4node package. The hardware complex of the experimental sample of the system is based on ASUS n550jv laptop. The ESP8266 microcontroller is used to control the camera and transmit data via Wi-Fi. The system provides classification of plant images into two classes - «Healthy plants» and «Diseased plants». The normalized histograms of the color component H in the HSV model for shades of green are chosen as the features of images for recognition. The support vector machine or neural network can be used for classification. Experiments have shown that the neural network gives the best results in recognition accuracy.