Citrus Huanglongbing Detection Based on Multi-Modal Feature Fusion Learning

Fan, Deng-Ping; Wang, Fengcheng; Yang, Dongzi; Lin, Shaoming; Chen, Xin; Lan, Yubin; Deng, Xiaoling

doi:10.3389/fpls.2021.809506

Cited by 18 publications

(13 citation statements)

References 32 publications

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“…The investigation of Nagasubramanian et al (2019) demonstrated that soybeans infected the charcoal rot are more sensitive than healthy soybeans in the wavelengths of visible spectra (400–700 nm). Yang et al (2021) have achieved good results in the Citrus Huanglongbing detection task by fusing hyperspectral data in CNNs using a multimodal approach. Recent research has shown that the transformer architecture is better suited for multimodal tasks ( Frank et al, 2021 ; Zhang et al, 2021 ).…”

Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Identification of Maize Leaf Diseases Is Improved by an Attention Mechanism: Self-Attention

et al. 2022

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Maize leaf diseases significantly reduce maize yield; therefore, monitoring and identifying the diseases during the growing season are crucial. Some of the current studies are based on images with simple backgrounds, and the realistic field settings are full of background noise, making this task challenging. We collected low-cost red, green, and blue (RGB) images from our experimental fields and public dataset, and they contain a total of four categories, namely, southern corn leaf blight (SCLB), gray leaf spot (GLS), southern corn rust (SR), and healthy (H). This article proposes a model different from convolutional neural networks (CNNs) based on transformer and self-attention. It represents visual information of local regions of images by tokens, calculates the correlation (called attention) of information between local regions with an attention mechanism, and finally integrates global information to make the classification. The results show that our model achieves the best performance compared to five mainstream CNNs at a meager computational cost, and the attention mechanism plays an extremely important role. The disease lesions information was effectively emphasized, and the background noise was suppressed. The proposed model is more suitable for fine-grained maize leaf disease identification in a complex background, and we demonstrated this idea from three perspectives, namely, theoretical, experimental, and visualization.

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Section: Discussionmentioning

confidence: 99%

Deep Learning-Based Identification of Maize Leaf Diseases Is Improved by an Attention Mechanism: Self-Attention

et al. 2022

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“…We consider transforming the optimized post reflection spectral curve into an image with rich feature information through certain methods, and then using deep learning to train image processing. Yang et al of Zhejiang University extended the two-dimensional spectrum horizontally into a three-dimensional map which is shown in Figure 3 and substituted it into the deep learning model training in 2021 [9] . The experiment achieved high accuracy, but the extra one-dimensional did not carry other characteristic information.…”

Section: The Proposed Methodsmentioning

confidence: 99%

“…Here is a sample illustration and caption for a multimedia file: Figure 3. Reflectivity cylindrical pseudo image [9] .…”

Section: The Proposed Methodsmentioning

confidence: 99%

SFPGRN: spectral detection method of plant diseases based on deep learning

Zhou

Zhang

Liu

et al. 2023

3rd International Conference on Artificial Intelligence, Automation, and High-Performance Computing (AIAHPC 2023)

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At present, most of the spectral detection methods with plant diseases are based on machine learning, we present a novel approach to spectral analysis using a spectral feature pseudo-graph-based residual network, referred to as SFPGRN, for data in the near-infrared band. The proposed method involves constructing a residual network model using a characteristic surface obtained via natural neighborhood interpolation based on preprocessed near-infrared spectral reflection signal and first-order differential spectral index. The input layer is a pseudo spectral map that is generated through surface projection of the characteristic surface. The generated spectral pseudo image contains a diverse set of spectral features that augment the representation capability of the pseudo-image while retaining the spectral band reflectance information. We evaluated the proposed method on the 2015 spectral dataset of apple leaf diseases and insect pests in China and found that it outperforms traditional machine learning algorithms, achieving a classification accuracy of 93.21%.

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“…On the other hand, CNN-based systems usually detect four classes, including HLB, healthy, mineral deficiencies (e.g., magnesium, nitrogen, and zinc) [18], [20], [22], and other diseases (e.g., canker and greasy spot) [21]. Moreover, only one CNN-based approach distinguishes between HLBpositive and HLB-negative classes in orange crops [30].…”

Section: Literature Reviewmentioning

confidence: 99%

A Huanglongbing Detection Method for Orange Trees Based on Deep Neural Networks and Transfer Learning

2022

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Huanglongbing (HLB) is one of the most threatening diseases for citrus production and it has caused significant economic damage worldwide. Hence, computer-vision systems that are based on convolutional neural networks (CNNs) can detect HLB accurately. Moreover, the detection system should be able to discriminate between HLB and other citrus abnormalities to ensure that any treatments are effective. Besides, the causal pathogen of HLB is usually detected and diagnosed by the quantitative realtime polymerase chain reaction (qPCR) test, which is costly. Consequently, it is difficult to collect large datasets to train CNN-based systems. In this case, transfer learning from pre-trained CNNs is a solution for building an HLB-detection system using small-sized datasets. This paper evaluates two kinds of CNN architectures: series network (represented by AlexNet, VGG16, and VGG19 models) and directed acyclic graph (DAG) network (represented by ResNet18, GoogLeNet, and Inception-V3 models). These pre-trained CNNs are fine-tuned to distinguish HLB, healthy cases, and 10 kinds of abnormalities of the Citrus sinensis species, which is commonly known as sweet orange. The dataset includes 953 color images, where the leaf samples were collected from orange groves in north Mexico. The 10-fold cross-validation results show that all the CNNs present a 95% or higher HLB sensitivity. However, the number of trainable parameters impacts HLB detection more than the network's depth. Specifically, VGG19, with 19 layers and 144 M parameters, reached a perfect sensitivity for all cross-validation experiments; whereas Inception-V3, with 48 layers and 24 M parameters, reached 95% sensitivity to HLB detection. This outcome happens because a higher number of parameters compensates for the limited number of HLB cases, so VGG19 can successfully transfer the learned characteristics to new cases. This study gives guidance when choosing an adequate CNN to efficiently detect HLB and other orange abnormalities. Besides, a detection scheme is proposed to be further implemented in a portable system to detect HLB in situ, potentially helping to reduce economic losses for small growers from low-income regions. INDEX TERMSHuanglongbing detection, Citrus sinensis orange, Convolutional neural networks, Transfer learning, Hand-crafted features. America, Asia, and South Africa. HLB's visible symptoms include blotchy mottle leaves, stunted growth, yellow shoot, reduced fruit size, corky veins, root decline, and dieback [3]. The suspicious causal pathogen of this disease is a bacterium of the genus Candidatus Liberibacter. Three bacterial species are known to cause HLB: Ca. L. asiaticus, Ca. L. africanus, and Ca. L. americanus. Transmission of the bacteria occurs through an insect vector. Both Ca. L. asiaticus and Ca. L. americanus are transmitted by the Asian citrus psyllid Diaphorina citri, whereas Ca. L. africanus is transmitted by

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Citrus Huanglongbing Detection Based on Multi-Modal Feature Fusion Learning

Cited by 18 publications

References 32 publications

Deep Learning-Based Identification of Maize Leaf Diseases Is Improved by an Attention Mechanism: Self-Attention

Deep Learning-Based Identification of Maize Leaf Diseases Is Improved by an Attention Mechanism: Self-Attention

SFPGRN: spectral detection method of plant diseases based on deep learning

A Huanglongbing Detection Method for Orange Trees Based on Deep Neural Networks and Transfer Learning

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