An Automated Farm Field Unit Identification Framework for Large-Scale Farms Based on Remote Sensing Image Segmentation and Parameter Optimization

Farm field units are the fundamental management units of modern precision agriculture, and accurate identification of their geographic boundaries is essential for the data management of farmland soil and nutrients. However, traditional manual vectorization methods are inefficient and costly. To address this issue, this study proposes an automated farm field unit identification framework based on remote sensing image segmentation and parameter optimization. Starting from the framework of Geographic Object-Based Image Analysis (GEOBIA), the framework employs the multiresolution segmentation (MRS) algorithm combined with intra-segment homogeneity and inter-segment heterogeneity indicators, and iteratively determines the optimal scale parameters through three unsupervised segmentation parameter optimization (USPO) methods, namely the methods proposed by Espindola et al. (EUSPO), Zhang et al. (ZUSPO), and Johnson et al. (JUSPO), to achieve automatic identification of farm field information. The framework was evaluated on eleven production teams of the Tenihe Farm, a large-scale intensive farm in Hulunbuir, China, using two scenes of Landsat 8 OLI imagery acquired on 18 September 2019 (multispectral bands pan-sharpened to 15 m, with the red, green, blue, and near-infrared bands used for segmentation), taking field-investigation-verified farm field vector maps as the reference. The results show that the identification accuracy of farm field units generally reaches over 80%. The main contribution of this study lies in extending USPO methods from segmentation quality evaluation to automated farm field vector extraction in precision agriculture, and in providing a systematic horizontal comparison of three USPO combination strategies under large-scale heterogeneous agricultural landscapes, which reveals that the JUSPO method, with its F-measure fusion strategy, achieves the most robust performance in most teams. These findings verify the effectiveness and application potential of the proposed framework.