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

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.

Plant phenotyping relevance

UAVマルチスペクトル画像から作物の水ストレスおよびさび病状態を推定するセマンティックセグメンテーション手法の開発が中心であり、植物状態の取得・抽出に該当する。

Code and data availability

The supplied blocks describe UAV multispectral orthomosaic datasets and a WF-UNet++ segmentation framework, but contain no public dataset deposit, no author code/model release, and no availability statements or URLs. The datasets are cited via reference [39] (prior work) and no paper-specific public asset is actionable

No evidence-backed public reproduction asset is currently recorded.