Tomato plant disease detection using transfer learning with C-GAN synthetic images

Abbas, Amreen; Jain, Sweta; Gour, Mahesh; Vankudothu, Swetha

doi:10.1016/j.compag.2021.106279

Cited by 402 publications

(183 citation statements)

References 11 publications

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“…The Convolution Neural Network is the driving concept of deep learning algorithms in computer vision, which led to outstanding performance in most of the pattern recognition tasks such as image classification [39] , [40] , [41] , [42] , object localization, segmentation, and detection [43] , [44] , [45] . It has also shown its superiority in the medical image analysis for image classification and segmentation problems [46] , [47] , [48] , [49] , especially in lung-related diseases such as lung nodule detection [50] , pneumonia detection [51] , and pulmonary tuberculosis [52] .…”

Section: Methodsmentioning

confidence: 99%

Automated COVID-19 detection from X-ray and CT images with stacked ensemble convolutional neural network

Gour

Jain

2022

Biocybernetics and Biomedical Engineering

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Automatic and rapid screening of COVID-19 from the radiological (X-ray or CT scan) images has become an urgent ion of SARS-CoV-2 worldwide. However, accurate and reliable screening of patients is challenging due to the discrepancy between the radiological images of COVID-19 and other viral pneumonia. So, in this paper, we design a new stacked convolutional neural network model for the automatic diagnosis of COVID-19 disease from the chest X-ray and CT images. In the proposed approach, different sub-models have been obtained from the VGG19 and the Xception models during the training. Thereafter, obtained sub-models are stacked together using softmax classifier. The proposed stacked CNN model combines the discriminating power of the different CNN’s sub-models and detects COVID-19 from the radiological images. In addition, we collect CT images to build a CT image dataset and also generate an X-ray images dataset by combining X-ray images from the three publicly available data repositories. The proposed stacked CNN model achieves a sensitivity of 97.62% for the multi-class classification of X-ray images into COVID-19, Normal and Pneumonia Classes and 98.31% sensitivity for binary classification of CT images into COVID-19 and no-Finding classes. Our proposed approach shows superiority over the existing methods for the detection of the COVID-19 cases from the X-ray radiological images.

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Section: Methodsmentioning

confidence: 99%

Automated COVID-19 detection from X-ray and CT images with stacked ensemble convolutional neural network

Gour

Jain

2022

Biocybernetics and Biomedical Engineering

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“…[31] reviews various modern feature extraction techniques for plant disease detection. In [32], the authors propose a C-GAN based deep learning approach to detect tomato diseases from leaf images. The residualbased CNN architecture is proposed in [33].…”

Section: Irri Rice Knowledgementioning

confidence: 99%

An Ontology-Based Expert System for Rice Disease Identification and Control Recommendation

et al. 2021

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A great deal of information related to rice cultivation has been published on the web. Conventionally, this information is studied by end-users to identify pests, and to prevent production losses from rice diseases. Despite its benefits, such information has not yet been encoded in a machine-processable form. This research closes the gap by modeling the knowledge-bases using ontologies and semantic technologies. Our modeled ontologies are externalized from existing reliable sources only, and offer axioms that describe abnormal appearances in rice diseases (and insects) and the corresponding controls. In addition, we developed an expert system called RiceMan, based on our ontologies, to support technical and non-technical users for diagnosing problems from observed abnormalities. We also introduce a composition procedure that aggregates users’ observation data with others for realizing spreadable diseases. This procedure, together with ontology reasoning, lies at the heart of our methodology. Finally, we evaluate our methodology practically with four groups of stakeholders in Thailand: senior agronomists, junior agronomists, agricultural students, and ontology specialists. Both ontologies and RiceMan are evaluated to verify their correctness, usefulness, and usability in various aspects. Our experimental results show that ontology reasoning is a promising approach for this domain problem.

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“…In contrast, automatic disease detection is significantly more accurate and takes less time and labor [18]. As a result, numerous studies [19][20][21][22] have been conducted and are discussed in detail below. This section provides a review of different techniques applied in the identification of crop diseases, presents the taxonomy of various crop diseases, and describes the concept of image processing and machine learning.…”

Section: Introductionmentioning

confidence: 99%

On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

2021

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The agricultural sector remains a key contributor to the Moroccan economy, representing about 15% of gross domestic product (GDP). Disease attacks are constant threats to agriculture and cause heavy losses in the country’s economy. Therefore, early detection can mitigate the severity of diseases and protect crops. However, manual disease identification is both time-consuming and error prone, and requires a thorough knowledge of plant pathogens. Instead, automated methods save both time and effort. This paper presents a contemporary overview of research undertaken over the past decade in the field of disease identification of different crops using machine learning, deep learning, image processing techniques, the Internet of Things, and hyperspectral image analysis. Additionally, a comparative study of several techniques applied to crop disease detection was carried out. Furthermore, this paper discusses the different challenges to be overcome and possible solutions. Then, several suggestions to address these challenges are provided. Finally, this research provides a future perspective that promises to be a highly useful and valuable resource for researchers working in the field of crop disease detection.

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Tomato plant disease detection using transfer learning with C-GAN synthetic images

Cited by 402 publications

References 11 publications

Automated COVID-19 detection from X-ray and CT images with stacked ensemble convolutional neural network

Automated COVID-19 detection from X-ray and CT images with stacked ensemble convolutional neural network

An Ontology-Based Expert System for Rice Disease Identification and Control Recommendation

On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

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