Y–Net: Identification of Typical Diseases of Corn Leaves Using a 3D–2D Hybrid CNN Model Combined with a Hyperspectral Image Band Selection Module

Jia, Yinjiang; Shi, Yaoyao; Luo, Jiaqi; Hong-min, Sun

doi:10.3390/s23031494

Cited by 7 publications

(8 citation statements)

References 31 publications

(35 reference statements)

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“…However, HSIs usually have high-dimensional features and large amounts of redundant information, inducing the Hughes phenomenon in the applications of hyperspectral classification [ 6 ]. Band selection that reduces redundant information by selecting a set of representative bands from an HSI is an effective method to tackle this problem [ 7 ].…”

Section: Introductionmentioning

confidence: 99%

Joint Learning of Correlation-Constrained Fuzzy Clustering and Discriminative Non-Negative Representation for Hyperspectral Band Selection

Wang

2023

Sensors

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Hyperspectral band selection plays an important role in overcoming the curse of dimensionality. Recently, clustering-based band selection methods have shown promise in the selection of informative and representative bands from hyperspectral images (HSIs). However, most existing clustering-based band selection methods involve the clustering of original HSIs, limiting their performance because of the high dimensionality of hyperspectral bands. To tackle this problem, a novel hyperspectral band selection method termed joint learning of correlation-constrained fuzzy clustering and discriminative non-negative representation for hyperspectral band selection (CFNR) is presented. In CFNR, graph regularized non-negative matrix factorization (GNMF) and constrained fuzzy C-means (FCM) are integrated into a unified model to perform clustering on the learned feature representation of bands rather than on the original high-dimensional data. Specifically, the proposed CFNR aims to learn the discriminative non-negative representation of each band for clustering by introducing GNMF into the model of the constrained FCM and making full use of the intrinsic manifold structure of HSIs. Moreover, based on the band correlation property of HSIs, a correlation constraint, which enforces the similarity of clustering results between neighboring bands, is imposed on the membership matrix of FCM in the CFNR model to obtain clustering results that meet the needs of band selection. The alternating direction multiplier method is adopted to solve the joint optimization model. Compared with existing methods, CFNR can obtain a more informative and representative band subset, thus can improve the reliability of hyperspectral image classifications. Experimental results on five real hyperspectral datasets demonstrate that CFNR can achieve superior performance compared with several state-of-the-art methods.

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Section: Introductionmentioning

confidence: 99%

Joint Learning of Correlation-Constrained Fuzzy Clustering and Discriminative Non-Negative Representation for Hyperspectral Band Selection

Wang

2023

Sensors

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“…Thus, hyperspectral images contain a large amount of information (≈1 Gb) making it possible to analyze a complex environment with minor components using machine learning approaches suitable for this type of data [30]. In this context, previous studies successfully used HSI analysis to classify leaves and plants as non-infected or infected by various diseases [33,34] or aphids [32,35]. These studies, respectively, used SVM [33] and 3D-2D hybrid CNN (Y-Net) [34] models on leaf patches, as well as CNN, achieving results similar to SVM [32], and least-squares SVM (LS-SVM) [35] for hyperspectral image classification.…”

Section: Introductionmentioning

confidence: 99%

“…In this context, previous studies successfully used HSI analysis to classify leaves and plants as non-infected or infected by various diseases [33,34] or aphids [32,35]. These studies, respectively, used SVM [33] and 3D-2D hybrid CNN (Y-Net) [34] models on leaf patches, as well as CNN, achieving results similar to SVM [32], and least-squares SVM (LS-SVM) [35] for hyperspectral image classification. Other methods aim at detecting the pests themselves as well as their symptoms on crops, such as larvae [36,37] or leaf miners over time and across different fertilizer regimes [38].…”

Section: Introductionmentioning

confidence: 99%

Detection of Aphids on Hyperspectral Images Using One-Class SVM and Laplacian of Gaussians

et al. 2023

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Aphids cause severe damage to agricultural crops, resulting in significant economic losses, and an increased use of pesticides with decreased efficiency. Monitoring aphid infestations through regular field surveys is time-consuming and does not always provide an accurate spatiotemporal representation of the distribution of pests. Therefore, an automated, non-destructive method to detect and evaluate aphid infestation would be beneficial for targeted treatments. In this study, we present a machine learning model to identify and quantify aphids, localizing their spatial distribution over leaves, using a One-Class Support Vector Machine and Laplacian of Gaussians blob detection. To train this model, we built the first large database of aphids’ hyperspectral images, which were captured in a controlled laboratory environment. This database contains more than 160 images of three aphid lines, distinctive in color, shape, and developmental stages, and are displayed laying on leaves or neutral backgrounds. This system exhibits high-quality validation scores, with a Precision of 0.97, a Recall of 0.91, an F1 score of 0.94, and an AUPR score of 0.98. Moreover, when assessing this method on new and challenging images, we did not observe any false negatives (and only a few false positives). Our results suggest that a machine learning model of this caliber could be a promising tool to detect aphids for targeted treatments in the field.

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“…bacterial leaf blight (BLB) in rice [78], grapevine vein-clearing virus (GVCV) in grapevines [79], and potato late blight (PLB) in potatoes [80]. We further explore the architecture of 3D-CNN-based models for identifying specific defects in crops, such as decay in blueberries [71], bruise and brown spots in fruits [81,82], heat stress in rice [83], as well as black, fermented, shell, and broken coffee defects in beans [7].…”

Section: Overview Of 3d-cnns-based Models Used In Hsi Classification ...mentioning

confidence: 99%

“…Moreover, in the context of detection of two similar crop diseases that are indistinguishable to the naked eyes, a recent study by Reference [82]…”

Section: Merged 2d-and 3d-cnn Architecturesmentioning

confidence: 99%

Exploring Hyperspectral Imaging and 3D Convolutional Neural Network for Stress Classification in Plants

Noshiri

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Hyperspectral imaging (HSI) has emerged as a transformative technology in imaging, characterized by its ability to capture a wide spectrum of light, including wavelengths beyond the visible range. This approach significantly differs from traditional imaging methods such as RGB imaging, which uses three color channels, and multispectral imaging, which captures several discrete spectral bands. Through this approach, HSI offers detailed spectral signatures for each pixel, facilitating a more nuanced analysis of the imaged subjects. This capability is particularly beneficial in applications like agricultural practices, where it can detect changes in physiological and structural characteristics of crops. Moreover, the ability of HSI to monitor these changes over time is advantageous for observing how subjects respond to different environmental conditions or treatments. However, the high-dimensional nature of hyperspectral data presents challenges in data processing and feature extraction. Traditional machine learning algorithms often struggle to handle such complexity. This is where 3D Convolutional Neural Networks (CNNs) become valuable. Unlike 1D-CNNs, which extract features from spectral dimensions, and 2D-CNNs, which focus on spatial dimensions, 3D CNNs have the capability to process data across both spectral and spatial dimensions. This makes them adept at extracting complex features from hyperspectral data. In this thesis, we explored the potency of HSI combined with 3D-CNN in agriculture domain where plant health and vitality are paramount. To evaluate this, we subjected lettuce plants to varying stress levels to assess the performance of this method in classifying the stressed lettuce at the early stages of growth into their respective stress-level groups. For this study, we created a dataset comprising 88 hyperspectral image samples of stressed lettuce. Utilizing Bayesian optimization, we developed 350 distinct 3D-CNN models to assess the method. The top-performing model achieved a 75.00\% test accuracy. Additionally, we addressed the challenge of generating valid 3D-CNN models in the Keras Tuner library through meticulous hyperparameter configuration. Our investigation also extends to the role of individual channels and channel groups within the color and near-infrared spectrum in predicting results for each stress-level group. We observed that the red and green spectra have a higher influence on the prediction results. Furthermore, we conducted a comprehensive review of 3D-CNN-based classification techniques for diseased and defective crops using non-UAV-based hyperspectral images.

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Y–Net: Identification of Typical Diseases of Corn Leaves Using a 3D–2D Hybrid CNN Model Combined with a Hyperspectral Image Band Selection Module

Cited by 7 publications

References 31 publications

Joint Learning of Correlation-Constrained Fuzzy Clustering and Discriminative Non-Negative Representation for Hyperspectral Band Selection

Joint Learning of Correlation-Constrained Fuzzy Clustering and Discriminative Non-Negative Representation for Hyperspectral Band Selection

Detection of Aphids on Hyperspectral Images Using One-Class SVM and Laplacian of Gaussians

Exploring Hyperspectral Imaging and 3D Convolutional Neural Network for Stress Classification in Plants

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