Abstract. Traditional autonomous agricultural systems face significant challenges in performing continuous operations within fragmented field regions. To address this issue, it is essential to upgrade these systems to automatically acquire high-precision field boundaries. This study tests the hypothesis that fragmented tobacco parcels can be reliably mapped using a cloud-based, multi-source remote sensing framework and that the resulting products can directly support autonomous field operations. Using Xuchang City, Henan Province, China, as a case study, we developed a cloud-edge-integrated tobacco mapping workflow on the Google Earth Engine (GEE) platform by fusing Sentinel-2 optical imagery, Sentinel-1 synthetic-aperture radar data, and topographic variables. A comprehensive feature set, including spectral bands, vegetation indices, radar backscatter, texture metrics, and terrain attributes, was used to train and compare three machine learning classifiers: random forest (RF), gradient boosting decision tree (GBDT), and classification and regression tree (CART). RF achieved the highest performance, with an overall accuracy of 93.08 % and a kappa coefficient of 0.92, outperforming GBDT (90.60 %, 0.89) and CART (87.60 %, 0.85). The RF-derived tobacco planting area showed the closest agreement with official statistics, with a consistency ratio of 94.12 %. Model robustness was further demonstrated by direct transfer to the adjacent Pingdingshan City without re-training, yielding a 97.70 % consistency with reported acreage. By shifting field-boundary extraction from manual delineation to automated cloud-based processing, this study provides a scalable solution for mapping fragmented tobacco fields and delivering parcel-level geospatial data to autonomous agricultural systems, with broader applicability to other cash crops in fragmented landscapes.
Zhao et al. (Fri,) studied this question.