A Multi-Head UNet++ Framework with Fractional Differential Output Refinement for UAV Multispectral Crop Stress Mapping

SUİÇMEZ Ç., YILMAZ C., KAHRAMAN H. T., SÖNMEZ Y.

Sensors, vol.26, no.10, 2026 (SCI-Expanded, Scopus)

  • Publication Type: Article / Article
  • Volume: 26 Issue: 10
  • Publication Date: 2026
  • Doi Number: 10.3390/s26103228
  • Journal Name: Sensors
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Compendex, EMBASE, INSPEC, MEDLINE, Directory of Open Access Journals, Academic Search Ultimate (EBSCO), Biomedical Reference Collection: Corporate Edition (EBSCO), Health Research Premium Collection (ProQuest)
  • Keywords: fractional anisotropic diffusion, multi-source dataset harmonization, multispectral semantic segmentation, physics-informed deep learning, precision agriculture, UAV-based remote sensing
  • Gazi University Affiliated: Yes

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.