Rice pest control is one of the main challenges in the agricultural sector in Indonesia. Pests such as planthoppers, stem borers, and rats can cause significant losses to crop yields. Currently, many farmers have difficulty identifying pest types and how to control them quickly and effectively. Therefore, a technological solution is needed that can detect pests directly and provide recommendations for action. This study aims to design an Android-based application that is able to detect types of rice plant pests using visual images and provide recommendations for handling. This application is designed using digital image processing methods and is supported by a large pest database. This technology is expected to be an efficient and practical solution for farmers. The research method used in developing this application is a qualitative method, involving interviews with farmers, agronomists, and data collection related to pests and their damage patterns. This application utilizes AI-based pattern recognition technology to detect pests through photos taken directly by farmers. The results of the study showed that this application was able to detect several types of major pests with an accuracy of up to 85%. In addition, this application provides recommendations for handling steps based on guidelines from the Ministry of Agriculture. The trial showed that this application can help farmers identify pests faster than manual methods. The main contribution of this research is to create a technology-based solution to agricultural problems in Indonesia, especially in the rice sector. With this application, farmers can increase their yields through early identification and proper pest management. In future implementations, this application will continue to be developed to detect additional pests and expand its database. It is hoped that this application can be an important tool in supporting smart farming in Indonesia.
