Robust plant disease segmentation in complex field environments: an in-depth analysis and validation with STAR-Net

Abstract

Introduction Plant disease segmentation in real-world agricultural environments poses significant technical challenges, including complex backgrounds, diverse lesion morphologies, and extreme class imbalance. Methods In this paper, we propose an integrated solution, STAR-Net, which combines a novel network architecture with a dynamic training strategy. The architecture features an innovative Heterogeneous Branch Attention Aggregation (HBAA) module to robustly represent multi-scale and multi-morphology features. The training strategy employs a Dynamic Phase-Weighted Loss (DPW-Loss) to navigate the complexities of imbalanced data. Results Our method achieves a state-of-the-art average mIoU of 93.36% on the NLB dataset. This result demonstrates its superior ability to precisely segment diseases with specific elongated morphologies. Furthermore, the model obtains a competitive average mIoU of 41.13% on the highly challenging PlantSeg dataset. This result validates its robustness in complex 'in-the-wild' scenarios. Discussion Our work presents a powerful, well validated, and synergistic solution for plant disease segmentation. It also paves the way for practical applications in precision agriculture.

Plant phenotyping relevance

植物病徴を画像からセグメンテーションする新規ネットワークと学習法を開発し、複数データセットで検証しているため、病害状態の表現型抽出が中心である。

Code and data availability

The supplied blocks describe the paper's methods and datasets (self-developed ADLD, plus public PlantSeg and NLB datasets from prior work), but contain no data availability statement, no authors' public code/model repository, and no deposited phenotype data or checkpoints. PlantSeg and NLB are cited third-party prior-d

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