PENGEMBANGAN RESTFUL API UNTUK APLIKASI KLASIFIKASI JENIS TANAH BERBASIS MOBILE PADA GOOGLE CLOUD - Firdaus, Muh; Afwani, Royana (Program Studi Teknik Informatika, Fakultas Teknik, Universitas Mataram, 2024)
Soil plays an important role and serves as the foundation of life for all living things. Determining soil type is very important in fields such as agriculture, land management, and environmental studies. Various methods, including soil content, or color classification methods, as well as conducting laboratory tests, can be used to identify soil types. However, these often require the involvement of experts and are time-consuming and expensive. To overcome these challenges, machine learning models with computer vision techniques can be used through mobile-based applications. This research aims to link a machine learning model, to automatically classify soils into four types: alluvial, red, black, and clay soils with a mobile app called Terralysis through the RESTful API. Utilizing the advanced capabilities of mobile device cameras, users can take pictures of the land, upload them to the app, and receive the results. Integration with RESTful APIs facilitates seamless communication between mobile applications and machine learning models running on servers. RESTful APIs enable efficient and easy-to-use communications, ensuring easy updates and improved performance without overwhelming the mobile application. This approach improves application responsiveness, while powerful servers perform heavy processing tasks. Terralysis provides an efficient, fast, and accessible way for users without access to a laboratory or soil specialist to classify soil types, significantly reducing time and costs accurately.