Rock Identifier: Stone ID by Next Vision Limited represents a specialized mobile tool for geological identification, leveraging image recognition technology to analyze rocks and minerals through smartphone photography. The application occupies a growing niche in educational technology, providing immediate access to geological knowledge that was previously limited to laboratory analysis or expert consultation. The core functionality centers on photographic identification, where users capture or upload images of geological specimens for algorithmic analysis. The system cross-references visual characteristics against an extensive database of thousands of rock types, providing identification results within seconds. Beyond basic recognition, the application delivers comprehensive educational materials including geological properties, formation processes, and practical applications of identified specimens. The collection feature allows users to build and organize personal geological libraries with searchable metadata. User experience demonstrates thoughtful design with intuitive camera integration and clean information presentation. The interface prioritizes accessibility through minimal clutter and logical navigation, though some users report variable identification accuracy depending on photographic conditions. Real-world usage patterns show particular utility during fieldwork, educational activities, and collection management, with the mobile platform enabling immediate identification without internet dependency after initial database download. User feedback indicates generally positive reception with specific appreciation for educational content and collection features. Mike Speer's August 2025 review highlights subscription model concerns, noting the free trial period transitions to paid access. Jim Kennedy's June 2025 review praises the detailed descriptions while acknowledging occasional identification corrections. Multiple reviewers including Gary Bate (June 2025) note the application performs best with high-quality photographs and serves both beginners and experienced enthusiasts effectively. The application demonstrates strongest performance as an educational companion rather than scientific instrument, with identification accuracy sufficient for casual use but requiring verification for professional applications. Primary limitations include subscription-based access model and photographic dependency, while strengths encompass extensive database coverage and well-organized educational content. Ideal use cases include amateur geology, educational programming, and field collection management where approximate identification suffices for exploratory purposes.