Evaluation of a hyperspectral imaging data processing pipeline for early rust disease diagnosis in grain crops applied to wheat, rye, and barley phenotyping

Abstract

Hyperspectral sensing data processing pipeline, originally developed for the early diagnosis of rust diseases in grain crops, was assessed for its applicability for the task of phenotyping of healthy plants of wheat Triticum aestivum, barley Hordeum vulgare, and rye Secale cereale. Hyperspectral images of healthy plants, obtained under laboratory conditions using a Cubert Ultris 20 camera (450–874 nm range, 106 channels), were utilized. The effectiveness of various preprocessing schemes was compared: full (including normalization, smoothing, calculation of derivatives, and identification of extreme features), reduced, and minimal. Machine learning models were exploited for classification: logistic regression, support vector machine, and gradient boosting, trained on averaged spectra. It is shown that the use of a full pipeline optimized for phytopathological diagnostics leads to reduced classification accuracy in phenotyping tasks. The best results (F1 = 0.97 ± 0.025) were achieved using the original averaged spectral curves without additional transformations. It is concluded that for healthy wheat, barley, and rye phenotyping, absolute reflectance levels are informative, whereas for disease diagnostics, changes in the shape of the spectral curve are more important. The obtained results clarify the applicability limits of pipelines developed for phytosanitary purposes and can inform the development of remote monitoring and phenotyping systems for cereal crops.

Plant phenotyping relevance

穀類の健全植物フェノタイピングに対するハイパースペクトル画像処理パイプラインの適用性を比較評価しており、前処理と分類性能の検証が研究の中心である。

Code and data availability

The supplied article blocks describe hyperspectral imaging of wheat, barley, and rye and a machine-learning classification pipeline, but contain no data availability statement, no public dataset deposit, and no author code repository or URL. The pipeline is described only via citation to prior work (Terentev et al., 5;

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