Award-winning Work Information & Description
Apple Pest Control Recommendation System Based on Knowledge Map
Description
This work contains two parts of the user side and the administrator side. The client provides search and browsing functions for information related to apple pests and diseases, as well as add, view and delete functions for popular recommendations and user messages. The administrator has the functions of viewing user lists, downloading user behaviors, adding, deleting, modifying and checking apple pest data, and viewing and deleting user messages. This work provides three functional modules: popular recommendations, pest database and user messages. The popular recommendation module is similar to the popular news recommendation in today's headlines, and recommends the items that users click on the most. Twelve modules are displayed each time, in the form of pictures and corresponding questions. The pest database module allows users to browse by disease or pest classification and jump to the corresponding detail page. The details page shows the name, type, picture, symptom, characteristic, control method and knowledge map picture of the disease or pest. Entities of interest to the user are displayed below the search box, and the user can click on the entity to jump to the details page of the corresponding apple pest. The user message module allows users to select the message type, message title and message content, and can add relevant pictures by uploading the picture mark, or delete the message by deleting the mark. The main function of the administrator is to manage the information of apple diseases and insect pests, and realize the real-time update of the database of apple diseases and insect pests. Message management module is used to manage user messages to prevent the publication of inappropriate speech. The user management module is used to manage users and view them regularly. By clicking on the user behavior, the administrator can download the user's click history in Excel file format, which is mainly used to train the improved DKN model.