<![CDATA[Pest attacks on crops are one of the main factors contributing to reduced agricultural productivity in Indonesia. Manual pest identification often requires specialized expertise and is time-consuming, making the need for a fast and accurate solution essential. This study develops a mobile application for identifying crop pests using image processing techniques. The application is designed to be used by farmers and agricultural extension workers in the field simply by photographing parts of the plant suspected to be affected by pests. The identification process consists of several stages, including image preprocessing, feature extraction, and classification using a machine learning model trained with a dataset of common crop-pest images. The system is equipped with a simple interface to ensure ease of use for non-technical users. Test results show that the application is capable of identifying pests with an accuracy level sufficient for early detection needs. In addition, the application provides appropriate control recommendations, helping users make decisions to reduce the impact of pest attacks. This study demonstrates that the use of mobile devices and image processing techniques can be a practical alternative to support efforts in improving agricultural productivity. Further development can be carried out by expanding the types of pests recognized and enhancing the quality of the classification model through a more diverse dataset.]]>
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