Identification of Leaf-Scale Wheat Powdery Mildew ( Blumeria graminis f. sp. Tritici ) Combining Hyperspectral Imaging and an SVM Classifier.

Abstract

Powdery mildew (PM, Blumeria graminis f. sp. tritici ) is a devastating disease for wheat growth and production. It is highly meaningful that the disease severities can be objectively and accurately identified by image visualization technology. In this study, an integral method was proposed based on a hyperspectral imaging dataset and machine learning algorithms. The disease severities of wheat leaves infected with PM were quantitatively identified based on hyperspectral images and image segmentation techniques. A technical procedure was proposed to perform the identification and evaluation of leaf-scale wheat PM, specifically including three primary steps of the acquisition and preprocessing of hyperspectral images, the selection of characteristic bands, and model construction. Firstly, three-dimensional reduction algorithms, namely principal component analysis (PCA), random forest (RF), and the successive projections algorithm (SPA), were comparatively used to select the bands that were most sensitive to PM. Then, three diagnosis models were constructed by a support vector machine (SVM), RF, and a probabilistic neural network (PNN). Finally, the best model was selected by comparing the overall accuracies. The results show that the SVM model constructed by PCA dimensionality reduction had the best result, and the classification accuracy reached 93.33% by a cross-validation method. There was an obvious improvement of the identification accuracy with the model, which achieved an 88.00% accuracy derived from the original hyperspectral images. This study can provide a reference for accurately estimating the disease severity of leaf-scale wheat PM and other plant diseases by non-contact measurement technology.

Plant phenotyping relevance

小麦葉の病害重症度をハイパースペクトル画像と画像解析・機械学習で定量推定する方法が研究の中心であり、技術手順と精度比較による検証も行っている。

Code and data availability

The article describes hyperspectral imaging of wheat powdery mildew with SVM/RF/PNN classifiers, but contains no data availability statement, no public dataset or image deposit, and no code availability or repository URL. The only URL present is the CC BY license link, which is not a paper-specific asset.

No evidence-backed public reproduction asset is currently recorded.