Classifying Wheat Hyperspectral Pixels of Healthy Heads and Fusarium Head Blight Disease Using a Deep Neural Network in the Wild Field

Abstract

Classification of healthy and diseased wheat heads in a rapid and non-destructive manner for the early diagnosis of Fusarium head blight disease research is difficult. Our work applies a deep neural network classification algorithm to the pixels of hyperspectral image to accurately discern the disease area. The spectra of hyperspectral image pixels in a manually selected region of interest are preprocessed via mean removal to eliminate interference, due to the time interval and the environment. The generalization of the classification model is considered, and two improvements are made to the model framework. First, the pixel spectra data are reshaped into a two-dimensional data structure for the input layer of a Convolutional Neural Network (CNN). After training two types of CNNs, the assessment shows that a two-dimensional CNN model is more efficient than a one-dimensional CNN. Second, a hybrid neural network with a convolutional layer and bidirectional recurrent layer is reconstructed to improve the generalization of the model. When considering the characteristics of the dataset and models, the confusion matrices that are based on the testing dataset indicate that the classification model is effective for background and disease classification of hyperspectral image pixels. The results of the model show that the two-dimensional convolutional bidirectional gated recurrent unit neural network (2D-CNN-BidGRU) has an F1 score and accuracy of 0.75 and 0.743, respectively, for the total testing dataset. A comparison of all the models shows that the hybrid neural network of 2D-CNN-BidGRU is the best at preventing over-fitting and optimize the generalization. Our results illustrate that the hybrid structure deep neural network is an excellent classification algorithm for healthy and Fusarium head blight diseased classification in the field of hyperspectral imagery.

Variety identification of oat seeds using hyperspectral imaging: investigating the representation ability of deep convolutional neural network

“…15,16 In the eld of spectral analysis, DCNN was also gradually utilized in nitrogen concentration prediction of oilseed rape leaf, disease detection of wheat Fusarium head blight and crop classication in remote sensing images. [17][18][19] Our previous research has also conrmed that DCNN can achieve satisfying results in Chrysanthemum varieties discrimination. 20 However, all DCNNs ran in end-toend manner in these studies, that is to say, data representation and classication were concentrated in one system.…”

Identification of Fusarium Head Blight in Winter Wheat Ears Based on Fisher’s Linear Discriminant Analysis and a Support Vector Machine

“…The vegetation index method is a basic and commonly used information extraction technology in agricultural remote sensing monitoring research. Changes in plant pigment, moisture content, and internal structure occur when winter wheat is infected with pathogenic bacteria, and these changes can be reflected by spectral reflectance [17]. Based on prior knowledge, with reference to the application of various vegetation indices in the monitoring of plant diseases and insect pests, 16 vegetation indices based on hyperspectral data were selected and their applicability in assessing the severity of FHB was discussed.…”

“…Bauriegel et al (2011) used hyperspectral imagery and the derived head blight index (HBI), which uses spectral differences in the ranges of 665-675 nm and 550-560 nm, as a suitable outdoor classification method for the identification of head blight; the mean hit rates were 67% during the study period [16]. Jin et al (2018) classified wheat hyperspectral pixels of healthy heads and Fusarium head blight disease using a deep neural network in wild fields, with the classification accuracy reaching 74.3% [17]. Whetton et al (2018) implemented a hyperspectral line imager for online measurement of FHB wheat in the field, and RGB photos collected from the ground truth plots were used to assess crop disease incidence (the number of individual infected ears in relation to the healthy individuals).…”

Quantification of Hydrocarbon Abundance in Soils Using Deep Learning with Dropout and Hyperspectral Data

“…There are many types of deep learning architectures whose application have been proven to yield excellent results, the most common are Deep Believe Network (DBN), Convolutional Neural Network (CNN), Deep Convolutional Generative Adversarial Networks (DCGAN), Recurrent Neural Networks (RNN), etc. [47,48]. The application of deep learning techniques to hyperspectral data is relatively recent, for instance, in the work by the authors of [49], deep belief networks, and a novel texture enhancement algorithm were investigated for their suitability and practical application to hyperspectral image classification.…”