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Abstract.

Data are the foundation of digital agriculture. Data from a wide variety of sensors in the soil, in machinery, or from remote sensing can inform decisions including site-specific land and crop management but capitalizing on these data requires metadata that captures the full story related to production. Answers to metadata questions such as who, what, where, when, and how are often unavailable when aggregated data are analyzed. These metadata are crucial for making accurate operation and management decisions and certainly for developing AI models. Since farmers and researchers exhibit human behavior of forgetting to take notes or entering incorrect information, even with digital means, missing and erroneous records are common. To address this issue, a metadata collection Android app for agricultural activities was created that automatically appends the operator‘s name, time, and space information to an in-field event, and provides a user-friendly interface to gather information with more details describing which activity was done and how. The developed app has four main modules, including user registration, geofence construction, accessed geofence recognition, and a chatbot for extensive activity data collection. By design, manual data input, with automatic validation, when possible, was used for information collection. To achieve this, the app facilitates less error-prone data entry methods, e.g., via dynamically constructed, interactive option lists. The collected data were stored in a Google Firebase database as central storage for multiple users. To facilitate data interoperability, stored data were made accessible in CSV and JSON format.