Akademik Veri Yönetim Sistemi
SENSORS, cilt.26, sa.10, 2026 (SCI-Expanded, Scopus)
Highlights What are the main findings? First unified five-class pixel-level modeling of abiotic water stress and biotic rust disease using UAV-based multispectral orthomosaics. The proposed multi-head WF-UNet++ with FPDE refinement achieves 0.8623 mIoU and 0.8439 FG-IoU, outperforming strong baselines such as UNet, DeepLabV3+, and SegFormer. What are the implications of the main findings? Demonstrates the feasibility of harmonizing heterogeneous UAV multispectral datasets representing abiotic water stress and biotic rust disease within a common semantic segmentation framework. Provides a methodological basis for scalable multi-source crop stress mapping by combining multispectral data, multi-head segmentation, and fractional differential output refinement.Highlights What are the main findings? First unified five-class pixel-level modeling of abiotic water stress and biotic rust disease using UAV-based multispectral orthomosaics. The proposed multi-head WF-UNet++ with FPDE refinement achieves 0.8623 mIoU and 0.8439 FG-IoU, outperforming strong baselines such as UNet, DeepLabV3+, and SegFormer. What are the implications of the main findings? Demonstrates the feasibility of harmonizing heterogeneous UAV multispectral datasets representing abiotic water stress and biotic rust disease within a common semantic segmentation framework. Provides a methodological basis for scalable multi-source crop stress mapping by combining multispectral data, multi-head segmentation, and fractional differential output refinement.Abstract This study presents a unified semantic segmentation framework for UAV-based multispectral crop stress mapping, focusing on the integration of water stress and rust disease conditions within a common label space. Unlike conventional approaches that address individual stress factors independently, the proposed framework harmonizes heterogeneous datasets with different annotation schemes into a single multi-class segmentation problem. To achieve this, UAV multispectral orthomosaics are processed using a patch-based strategy and a multi-head UNet++ architecture incorporating segmentation, edge-aware, and Signed Distance Transform (SDT) branches. In addition, a physics-informed output-space refinement module based on fractional partial differential equations (FPDE) is introduced to enhance spatial coherence and boundary preservation in the predicted maps. Experimental results demonstrate the effectiveness of the proposed framework within the evaluated dataset setting, particularly in terms of boundary delineation, spatial consistency, and minority-class detection. The study highlights the feasibility of integrating heterogeneous stress conditions into a unified segmentation framework and provides a foundation for future research on scalable multi-source agricultural monitoring systems.