Cyberinformatics tool for in-season crop-specific land cover monitoring: Design, implementation, and applications of iCrop

Abstract

Cyberinformatics tools have supported decision makings in agriculture through cutting-edge big data, artificial intelligence/machine learning (AI/ML), and high-performance computing technologies. An open and easy-to-use agricultural cyberinformatics tool based on the findable, accessible, interoperable, reusable (FAIR) data principle is essential for the efficient distribution of crop-specific land cover information. This paper introduces iCrop, a new cyberinformatics tool to enable in-season crop type monitoring for the Conterminous United States (CONUS). As a web-based geographic information system (GIS), iCrop not only delivers three sets of new ML-based field-level crop-specific land cover geospatial data, including pre-season crop cover maps, in-season crop cover maps, and Refined Cropland Data Layer (R-CDL), but also provides a suite of mapping and geoprocessing functionalities through the FAIR geospatial data standards, such as Web Map Service (WMS), Web Coverage Service (WCS), and Web Processing Service (WPS). Meanwhile, we outline several use cases to highlight iCrop's applications under various agricultural operation scenarios, its functionality for land use change analysis, and its interoperability with generic web-based and desktop GIS software (e.g., GeoPlatform and QGIS). Our experimental results show that the new cyberinformatics tool can provide timely and unique crop-specific land cover information through the geoprocessing functionalities to facilitate U.S. agricultural information management and decision support. Moreover, this paper can be used as a systematic guidance for the design and implementation of the cyberinformatics tool to disseminate agro-geoinformation based on the FAIR data principle.