<![CDATA[Bougainvillea is one of the most popular ornamental plants, featuring a variety of types with morphological characteristics that often appear very similar. This resemblance frequently complicates the conventional identification process, particularly for sellers and buyers at Rabiku Florist. This study aims to develop an Android application capable of automatically classifying different bougainvillea types using a Convolutional Neural Network (CNN) algorithm. The system is developed using the Rapid Application Development (RAD) methodology, leveraging the MobileNetV2 architecture and integrating it with the TensorFlow Lite framework to ensure compatibility with mobile devices. The application is designed to identify five types of bougainvillea using digital images captured via the device’s camera or selected from the user’s gallery. Based on implementation results, the system demonstrates strong classification performance and delivers accurate information to users. This application is intended to serve as a practical and user-friendly tool for both the general public and businesses in accurately identifying bougainvillea species.Keywords: Image Classification, Bougainvillea, Convolutional Neural Network, MobileNetV2, Android.]]>
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