eFarm: A Tool for Better Observing Agricultural Land Systems

Area of Science:

• Agricultural Science
• Geospatial Information Science
• Human-Computer Interaction

Background:

• Current agricultural land system (ALS) observations rely heavily on remote sensing for biophysical data, with limited integration of socioeconomic data due to cost and efficiency issues in social surveys.
• Existing methods struggle to combine biophysical and socioeconomic data for ALS at large spatial scales, hindering comprehensive analysis and applications.

Purpose of the Study:

• To introduce eFarm, a smartphone-based crowdsourcing and human sensing tool for collecting geotagged ALS information at the land parcel level.
• To demonstrate the potential of integrating human and land data for improved ALS studies.

Main Methods:

• Development of a smartphone application (eFarm) for crowdsourcing and human sensing of ALS data.
• Utilizing high-resolution remote sensing imagery as a base for data collection within the app.
• Implementing functionalities for map visualization, data management, and data sensing.

Main Results:

• Trial tests indicate that the eFarm system functions effectively.
• The app facilitates the collection of geotagged ALS information at the land parcel level.

Conclusions:

• eFarm enables the acquisition of integrated human-land information for agricultural land systems.
• The tool shows significant potential for improving the sensing, mapping, and modeling of ALS studies through broadly-covered and timely-updated data.