Crop leaf disease recognition based on Self-Attention convolutional neural network

Zeng, Weihui; Li, Miao

doi:10.1016/j.compag.2020.105341

Cited by 115 publications

(43 citation statements)

References 13 publications

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“…In their work, they mainly compared their results with those of deep-learning methods and showed that classification using SVM and RF classifiers with extracted features from the shallow CNN outperformed pretrained deeplearning models. A self-attention convolutional neural network (SACNN) was used in [42] to identify several crop diseases. To examine the robustness of the model, the authors added different noise levels in the test-image set.…”

Section: Deep-learning-based Identificationmentioning

confidence: 99%

Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach

et al. 2021

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The timely identification and early prevention of crop diseases are essential for improving production. In this paper, deep convolutional-neural-network (CNN) models are implemented to identify and diagnose diseases in plants from their leaves, since CNNs have achieved impressive results in the field of machine vision. Standard CNN models require a large number of parameters and higher computation cost. In this paper, we replaced standard convolution with depth=separable convolution, which reduces the parameter number and computation cost. The implemented models were trained with an open dataset consisting of 14 different plant species, and 38 different categorical disease classes and healthy plant leaves. To evaluate the performance of the models, different parameters such as batch size, dropout, and different numbers of epochs were incorporated. The implemented models achieved a disease-classification accuracy rates of 98.42%, 99.11%, 97.02%, and 99.56% using InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNetB0, respectively, which were greater than that of traditional handcrafted-feature-based approaches. In comparison with other deep-learning models, the implemented model achieved better performance in terms of accuracy and it required less training time. Moreover, the MobileNetV2 architecture is compatible with mobile devices using the optimized parameter. The accuracy results in the identification of diseases showed that the deep CNN model is promising and can greatly impact the efficient identification of the diseases, and may have potential in the detection of diseases in real-time agricultural systems.

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Section: Deep-learning-based Identificationmentioning

confidence: 99%

Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach

et al. 2021

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“…datasets offer close range annotated images of infected plant organs against a clean background, offering a resource for disease diagnosis and severity scoring in collected leaves ( Mohanty, 2016 ; Arsenovic et al, 2019 ; Chouhan et al, 2019 ; Krohling, 2019 ; Parraga-Alava et al, 2019 ; Rauf et al, 2019 ; Tian et al, 2019 ; Nakatumba-Nabende et al, 2020 ; Singh et al, 2020 ). Machine learning models using support vector machines, CNNs, and self-attention CNNs trained on similar datasets were published recently ( Abdu et al, 2020 ; El Abidine et al, 2020 ; Zeng and Li, 2020 ), some of which report increased efficiency when using segmented regions for pathogen identification ( Esgario et al, 2020 ; Karlekar and Seal, 2020 ). A comprehensive review on machine learning for disease assessment in crops was published by Hasan et al (2020) .…”

Section: Applications Of Htpmentioning

confidence: 99%

Resources for image-based high-throughput phenotyping in crops and data sharing challenges

et al. 2021

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High throughput phenotyping (HTP) platforms are capable of monitoring the phenotypic variation of plants through multiple types of sensors such as RGB cameras, hyperspectral sensors and computed tomography, which can be associated with environmental and genotypic data. Because of the wide range of information provided, HTP datasets represent a valuable asset to characterise crop phenotypes. As HTP becomes widely employed with more tools and data being released, it is important that researchers are aware of these resources and how they can be applied to accelerate crop improvement. Researchers may exploit these datasets either for phenotype comparison or employ them as a benchmark to assess tool performance and to support the development of tools that are better at generalising between different crops and environments. In this review, we describe the use of image-based HTP for yield prediction, root phenotyping, development of climate-resilient crops, detecting pathogen and pest infestation and quantitative trait measurement. We emphasise the need for researchers to share phenotypic data, and offer a comprehensive list of available datasets to assist crop breeders and tool developers to leverage these resources in order to accelerate crop breeding.

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“…ADCM [35] integrates dropout into the attention mechanism according to the idea of lightweight and improves CBAM [33]. In addition, many works use improved attention mechanisms to improve the effect of CNNs [36,37].…”

Section: Attention Mechanismmentioning

confidence: 99%

A Novel Malware Detection and Family Classification Scheme for IoT Based on DEAM and DenseNet

Wang

Zhao

Wang

et al. 2021

Security and Communication Networks

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With the rapid increase in the amount and type of malware, traditional methods of malware detection and family classification for IoT applications through static and dynamic analysis have been greatly challenged. In this paper, a new simple and effective attention module of Convolutional Neural Networks (CNNs), named as Depthwise Efficient Attention Module (DEAM), is proposed and combined with a DenseNet to propose a new malware detection and family classification model. Based on the good effect of the DenseNet in the field of image classification and the visual similarity of the malware family on images, the gray-scale image transformed from malware is input into the model combined with the DEAM and DenseNet for malware detection, and then the family classification is carried out. The DEAM is a general lightweight attention module improved based on the Convolutional Block Attention Module (CBAM), which can strengthen the attention to the characteristics of malware and improve the model effect. We use the MalImg dataset, Microsoft malware classification challenge dataset (BIG 2015), and our dataset constructed by the two above-mentioned datasets to verify the effectiveness of the proposed model in family classification and malware detection. Experimental results show that the proposed model achieves 99.3% in terms of accuracy for malware detection on our dataset and achieves 98.5% and 97.3% in terms of accuracy for family classification on the MalImg dataset and BIG 2015 dataset, respectively. The model can reliably detect IoT malware and classify its families.

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Crop leaf disease recognition based on Self-Attention convolutional neural network

Cited by 115 publications

References 13 publications

Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach

Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach

Resources for image-based high-throughput phenotyping in crops and data sharing challenges

A Novel Malware Detection and Family Classification Scheme for IoT Based on DEAM and DenseNet

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