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.

Monitoring Wheat Powdery Mildew Based on Hyperspectral, Thermal Infrared, and RGB Image Data Fusion

“…The visible light and near-infrared regions are the sensitive bands for spectral identification of different crop diseases and insect pests; furthermore, the spectral sensitivity bands of different crops and different diseases vary. The sensitive bands of wheat powdery mildew are located at 490–780 nm [ 57 ], and wheat powdery mildew monitoring is mainly based on the sensitive band [ 58 , 59 ], and different forms of disease VI can be established according to the reflection characteristics of the disease [ 7 , 8 , 9 ]. Disease emergence involves gradual development that alters internal tissue physiology and biochemistry, and, in turn, the external morphological structure, and then manifests externally as disease that can be detected by remote sensing.…”

Detection of spring wheat plants affected by powdery mildew using hyperspectral survey data

“…Currently, active investigations are carried out to develop methods and approaches for plant diseases remote diagnosis based on using hyperspectral visualization of plants reflectivity [7,8]. It was demonstrated that hyperspectral visualization methods can be used in detection and diagnosis of powdery mildew [9,10]. Various spectral indicators of plants diseased leafs have been established.…”

Real-time plant health assessment via implementing cloud-based scalable transfer learning on AWS DeepLens

“…When a plant got infected by a specific disease, and then significant symptoms are shown on the leaves, which help in the identification and classification of that particular disease [7]. It is therefore essential to control and assess disease outspread [8]. A specific fungus or bacterium is frequently associated with the colour, scale, form, and margins of spots and blight (lesions).…”