A resource-efficient framework for plant disease classification: integrating reduced-order modeling with treatment-based label engineering.

Abstract

Plant disease diagnosis in field settings is challenged by subtle symptomology, high inter-class visual similarity, and class imbalance, making automated detection particularly difficult. While deep learning models achieve high accuracy, traditional architectures impose prohibitive computational costs that hinder deployment on resource-constrained hardware. This paper proposes a novel Reduced Order Modelling (ROM) framework integrating a YOLOv8m backbone for spatially sensitive feature extraction, PCA-based compression to isolate the most discriminative features, and classical classification. A treatment-based label engineering approach was applied to consolidate the PlantWildV2 dataset from 115 to 11 agronomically relevant classes. Experimental results showed that a highly compressed feature space acts as a natural regularizer, with accuracy peaking at 100 principal components and declining beyond that threshold. The tuned SVC classifier achieved a test accuracy of 87.52% and a macro F1-score of 0.882, outperforming all other classifiers evaluated. The proposed ROM framework surpassed EfficientNet-B0 in accuracy (87.52% vs. 82.50%) while reducing training time from 5.8 hours on GPU to 30.8 seconds on CPU, a 670-fold efficiency gain, demonstrating the viability of Reduced Order Modelling for plant disease detection on low-resource hardware.

Plant phenotyping relevance

植物画像から病害状態を推定する分類手法の開発が研究の中心であり、圧縮特徴抽出・分類器・計算効率を評価しているため、植物フェノタイピング手法として採用。

Code and data availability

The paper uses the PlantWildV2 dataset (cited prior work, Wei et al. 2024) and the Ultralytics YOLOv8 library, but contains no authors' public code, data deposit, trained model, or supplement with paper-specific assets. No data/code availability statement is present.

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