A Knowledge-Guided Multi-Task Framework for Robust and Interpretable Rice Disease Diagnosis in Open-Field Scenarios

Abstract

Abstract Deep learning has achieved remarkable success in rice disease diagnosis; however, existing methods often suffer from limited interpretability and poor robustness against open-world environmental noise. To address these challenges, this study proposes the Knowledge-Guided Multi-Task Rice Network (MTRNet) built upon a ResNet-50 backbone. Unlike conventional "black-box" models, MTRNet employs expert knowledge injection via a phytopathological matrix to explicitly disentangle disease features into Shape, Color, and Location attributes within a multi-head architecture. Furthermore, to mitigate false positives in complex field scenarios, a non-parametric Cascade Inference System (CIS)—comprising a biological grayscale filter and a visual consistency check—is introduced for robust Out-of-Distribution (OOD) detection and anomaly rejection. Experiments on a benchmark dataset of 5,932 field images, which primarily comprises four main rice diseases (Rice Leaf Blast, Brown Spot, Bacterial Leaf Blight, and Tungro), demonstrate that MTRNet achieves a diagnostic accuracy of 99.83%. Crucially, in an open-world robustness evaluation involving 1,000 non-agricultural noise samples, the proposed system achieved an 81.80% OOD rejection rate. By balancing diagnostic accuracy with structural transparency, this framework effectively narrows the gap between laboratory benchmarks and real-world agricultural applications.

Plant phenotyping relevance

イネ病害の画像から病徴を診断する深層学習・OOD検出手法を提案し、実画像データで性能評価しており、植物の病害状態の取得・推定が研究の中心である。

Code and data availability

The article describes a rice disease image dataset (5,932 images) and a 1,000-image noise dataset, plus the MTRNet model, but contains no public deposit, repository, or availability statement for the datasets, images, code, or trained checkpoints. No paper-specific public asset is actionable.

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