<![CDATA[Rice, the staple food for the majority of Indonesia's population, faces significant production threats from leaf diseases, which can decrease yields and jeopardize national food security. Traditional manual identification of these diseases is a major challenge for farmers, as it is often subjective, prone to misdiagnosis leading to incorrect treatments, time-consuming, demands specialized expertise, and is difficult to implement widely for effective real-time early prevention, allowing diseases to spread and significantly impact crop yields. This research addresses these challenges by developing an automated and easily accessible rice leaf disease diagnosis system. The system is manifested as a mobile application that integrates a Convolutional Neural Network (CNN) model, specifically utilizing the EfficientNetB0 architecture, for the classification of rice leaf images and leverages key Firebase services such as its Realtime Database for data synchronization and Cloud Storage for image management to ensure a scalable and responsive backend. The methodology involved several key stages. Firstly, the CNN model was developed by employing a transfer learning approach on the pre-trained EfficientNetB0 architecture. Secondly, the model underwent comprehensive testing using a dataset of 1,000 new rice leaf images, which were independently validated by agricultural experts. The results demonstrated that the developed CNN model achieved a global accuracy of 85.9%, with an average precision of 86.1% and recall of 85.9% (macro-average) in the expert validation testing phase with the 1,000 new images. However, the study also identified variations in the model's performance across different disease classes, highlighting areas that require further optimization to enhance detection effectiveness for specific types of rice leaf diseases. The primary benefit of this research is the provision of a practical rice leaf disease diagnosis tool that is readily accessible to farmers via a mobile application, empowering them with timely and accurate information for effective crop management. This can lead to reduced crop losses, improved yield quality, and contribute significantly to national food security. Furthermore, this research contributes to the field of applied machine learning and mobile computing in resource-constrained agricultural environments, offering valuable insights for the development of impactful informatics solutions.]]>
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