Real-Time Automated Classification of Sky Conditions Using Deep Learning and Edge Computing

The radiometric quality of remotely sensed imagery is crucial for precision agriculture applications because estimations of plant health rely on the underlying quality. Sky conditions, and specifically shadowing from clouds, are critical determinants in the quality of images that can be obtained from low-altitude sensing platforms. In this work, we first compare common deep learning approaches to classify sky conditions with regard to cloud shadows in agricultural fields using a visible spectrum camera. We then develop an artificial-intelligence-based edge computing system to fully automate the classification process. Training data consisting of 100 oblique angle images of the sky were provided to a convolutional neural network and two deep residual neural networks (ResNet18 and ResNet34) to facilitate learning two classes, namely (1) good image quality expected, and (2) degraded image quality expected. The expectation of quality stemmed from the sky condition (i.e., density, coverage, and thickness of clouds) present at the time of the image capture. These networks were tested using a set of 13,000 images. Our results demonstrated that ResNet18 and ResNet34 classifiers produced better classification accuracy when compared to a convolutional neural network classifier. The best overall accuracy was obtained by ResNet34, which was 92% accurate, with a Kappa statistic of 0.77. These results demonstrate a low-cost solution to quality control for future autonomous farming systems that will operate without human intervention and supervision.

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Image classification of different clove (Syzygium aromaticum) quality using deep learning method with convolutional neural network algorithm

IOP Conference Series Earth and Environmental Science ◽

10.1088/1755-1315/905/1/012018 ◽

2021 ◽

Vol 905 (1) ◽

pp. 012018

Author(s):

I Y Prayogi ◽

Sandra ◽

Y Hendrawan

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Test Data ◽

Training Data ◽

Syzygium Aromaticum ◽

Learning Method ◽

Validation Data ◽

Reading Accuracy

Abstract The objective of this study is to classify the quality of dried clove flowers using deep learning method with Convolutional Neural Network (CNN) algorithm, and also to perform the sensitivity analysis of CNN hyperparameters to obtain best model for clove quality classification process. The quality of clove as raw material in this study was determined according to SNI 3392-1994 by PT. Perkebunan Nusantara XII Pancusari Plantation, Malang, East Java, Indonesia. In total 1,600 images of dried clove flower were divided into 4 qualities. Each clove quality has 225 training data, 75 validation data, and 100 test data. The first step of this study is to build CNN model architecture as first model. The result of that model gives 65.25% reading accuracy. The second step is to analyze CNN sensitivity or CNN hyperparameter on the first model. The best value of CNN hyperparameter in each step then to be used in the next stage. Finally, after CNN hyperparameter carried out the reading accuracy of the test data is improved to 87.75%.

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Automatic vegetation identification in Google Earth images using a convolutional neural network: A case study for Japanese bamboo forests

10.1101/351643 ◽

2018 ◽

Author(s):

Shuntaro Watanabe ◽

Kazuaki Sumi ◽

Takeshi Ise

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Environmental Science ◽

Google Earth ◽

Image Resolution ◽

Training Data ◽

Identification Accuracy ◽

Automatic Identification

ABSTRACTClassifying and mapping vegetation are very important tasks in environmental science and natural resource management. However, these tasks are not easy because conventional methods such as field surveys are highly labor intensive. Automatic identification of target objects from visual data is one of the most promising ways to reduce the costs for vegetation mapping. Although deep learning has become a new solution for image recognition and classification recently, in general, detection of ambiguous objects such as vegetation still is considered difficult. In this paper, we investigated the potential for adapting the chopped picture method, a recently described protocol for deep learning, to detect plant communities in Google Earth images. We selected bamboo forests as the target. We obtained Google Earth images from three regions in Japan. By applying the deep convolutional neural network, the model successfully learned the features of bamboo forests in Google Earth images, and the best trained model correctly detected 97% of the targets. Our results show that identification accuracy strongly depends on the image resolution and the quality of training data. Our results also highlight that deep learning and the chopped picture method can potentially become a powerful tool for high accuracy automated detection and mapping of vegetation.

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Biodiversity Image Quality Metadata Augments Convolutional Neural Network Classification of Fish Species

10.1101/2021.01.28.428644 ◽

2021 ◽

Author(s):

Jeremy Leipzig ◽

Yasin Bakis ◽

Xiaojun Wang ◽

Mohannad Elhamod ◽

Kelly Diamond ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Image Quality ◽

Convolutional Neural Network ◽

Training Data ◽

Biological Research ◽

Small Subset ◽

Learning Approaches ◽

Species Classification ◽

Neural Network Approach

AbstractBiodiversity image repositories are crucial sources of training data for machine learning approaches to biological research. Metadata, specifically metadata about object quality, is putatively an important prerequisite to selecting sample subsets for these experiments. This study demonstrates the importance of image quality metadata to a species classification experiment involving a corpus of 1935 fish specimen images which were annotated with 22 metadata quality properties. A small subset of high quality images produced an F1 accuracy of 0.41 compared to 0.35 for a taxonomically matched subset of low quality images when used by a convolutional neural network approach to species identification. Using the full corpus of images revealed that image quality differed between correctly classified and misclassified images. We found the visibility of all anatomical features was the most important quality feature for classification accuracy. We suggest biodiversity image repositories consider adopting a minimal set of image quality metadata to support future machine learning projects.

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Oversampling Based on Data Augmentation in Convolutional Neural Network for Silicon Wafer Defect Classification

Knowledge Innovation Through Intelligent Software Methodologies, Tools and Techniques - Frontiers in Artificial Intelligence and Applications ◽

10.3233/faia200547 ◽

2020 ◽

Author(s):

Uzma Batool ◽

Mohd Ibrahim Shapiai ◽

Nordinah Ismail ◽

Hilman Fauzi ◽

Syahrizal Salleh

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Silicon Wafer ◽

Data Augmentation ◽

Imbalanced Data ◽

Training Data ◽

Defect Classification ◽

Learning Method ◽

Test Set

Silicon wafer defect data collected from fabrication facilities is intrinsically imbalanced because of the variable frequencies of defect types. Frequently occurring types will have more influence on the classification predictions if a model gets trained on such skewed data. A fair classifier for such imbalanced data requires a mechanism to deal with type imbalance in order to avoid biased results. This study has proposed a convolutional neural network for wafer map defect classification, employing oversampling as an imbalance addressing technique. To have an equal participation of all classes in the classifier’s training, data augmentation has been employed, generating more samples in minor classes. The proposed deep learning method has been evaluated on a real wafer map defect dataset and its classification results on the test set returned a 97.91% accuracy. The results were compared with another deep learning based auto-encoder model demonstrating the proposed method, a potential approach for silicon wafer defect classification that needs to be investigated further for its robustness.

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Convolutional Neural Network

10.4018/978-1-6684-2408-7.ch077 ◽

2022 ◽

pp. 1559-1575

Author(s):

Mário Pereira Véstias

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Artificial Neural Networks ◽

Deep Learning ◽

Convolutional Neural Network ◽

Machine Learning Algorithms ◽

Training Data ◽

Machine Learning Model ◽

Artificial Neural

Machine learning is the study of algorithms and models for computing systems to do tasks based on pattern identification and inference. When it is difficult or infeasible to develop an algorithm to do a particular task, machine learning algorithms can provide an output based on previous training data. A well-known machine learning model is deep learning. The most recent deep learning models are based on artificial neural networks (ANN). There exist several types of artificial neural networks including the feedforward neural network, the Kohonen self-organizing neural network, the recurrent neural network, the convolutional neural network, the modular neural network, among others. This article focuses on convolutional neural networks with a description of the model, the training and inference processes and its applicability. It will also give an overview of the most used CNN models and what to expect from the next generation of CNN models.

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Empirical Evaluation of the Effect of Optimization and Regularization Techniques on the Generalization Performance of Deep Convolutional Neural Network

Applied Sciences ◽

10.3390/app10217817 ◽

2020 ◽

Vol 10 (21) ◽

pp. 7817

Author(s):

Ivana Marin ◽

Ana Kuzmanic Skelin ◽

Tamara Grujic

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Empirical Evaluation ◽

Training Data ◽

Generalization Performance ◽

Deep Model ◽

Unseen Data ◽

Regularization Techniques ◽

Learning Architectures

The main goal of any classification or regression task is to obtain a model that will generalize well on new, previously unseen data. Due to the recent rise of deep learning and many state-of-the-art results obtained with deep models, deep learning architectures have become one of the most used model architectures nowadays. To generalize well, a deep model needs to learn the training data well without overfitting. The latter implies a correlation of deep model optimization and regularization with generalization performance. In this work, we explore the effect of the used optimization algorithm and regularization techniques on the final generalization performance of the model with convolutional neural network (CNN) architecture widely used in the field of computer vision. We give a detailed overview of optimization and regularization techniques with a comparative analysis of their performance with three CNNs on the CIFAR-10 and Fashion-MNIST image datasets.

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Analysis of COVID-19 Infections on a CT Image Using DeepSense Model

Frontiers in Public Health ◽

10.3389/fpubh.2020.599550 ◽

2020 ◽

Vol 8 ◽

Author(s):

Adil Khadidos ◽

Alaa O. Khadidos ◽

Srihari Kannan ◽

Yuvaraj Natarajan ◽

Sachi Nandan Mohanty ◽

...

Keyword(s):

Neural Network ◽

Deep Learning ◽

Input Data ◽

Training Data ◽

Learning Framework ◽

Learning Classifiers ◽

Tomography Image ◽

Mining Model ◽

Deep Learning Model

In this paper, a data mining model on a hybrid deep learning framework is designed to diagnose the medical conditions of patients infected with the coronavirus disease 2019 (COVID-19) virus. The hybrid deep learning model is designed as a combination of convolutional neural network (CNN) and recurrent neural network (RNN) and named as DeepSense method. It is designed as a series of layers to extract and classify the related features of COVID-19 infections from the lungs. The computerized tomography image is used as an input data, and hence, the classifier is designed to ease the process of classification on learning the multidimensional input data using the Expert Hidden layers. The validation of the model is conducted against the medical image datasets to predict the infections using deep learning classifiers. The results show that the DeepSense classifier offers accuracy in an improved manner than the conventional deep and machine learning classifiers. The proposed method is validated against three different datasets, where the training data are compared with 70%, 80%, and 90% training data. It specifically provides the quality of the diagnostic method adopted for the prediction of COVID-19 infections in a patient.

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Quality Index of Supervised Data for Convolutional Neural Network-Based Localization

Applied Sciences ◽

10.3390/app9101983 ◽

2019 ◽

Vol 9 (10) ◽

pp. 1983 ◽

Cited By ~ 2

Author(s):

Seigo Ito ◽

Mineki Soga ◽

Shigeyoshi Hiratsuka ◽

Hiroyuki Matsubara ◽

Masaru Ogawa

Keyword(s):

Neural Network ◽

Convolutional Neural Network ◽

Quality Index ◽

Single Photon ◽

Automated Guided Vehicles ◽

Training Data ◽

Supervised Training ◽

Localization Performance ◽

Imaging Sensor

Automated guided vehicles (AGVs) are important in modern factories. The main functions of an AGV are its own localization and object detection, for which both sensor and localization methods are crucial. For localization, we used a small imaging sensor named a single-photon avalanche diode (SPAD) light detection and ranging (LiDAR), which uses the time-of-flight principle and arrays of SPADs. The SPAD LiDAR works both indoors and outdoors and is suitable for AGV applications. We utilized a deep convolutional neural network (CNN) as a localization method. For accurate CNN-based localization, the quality of the supervised data is important. The localization results can be poor or good if the supervised training data are noisy or clean, respectively. To address this issue, we propose a quality index for supervised data based on correlations between consecutive frames visualizing the important pixels for CNN-based localization. First, the important pixels for CNN-based localization are determined, and the quality index of supervised data is defined based on differences in these pixels. We evaluated the quality index in indoor-environment localization using the SPAD LiDAR and compared the localization performance. Our results demonstrate that the index correlates well to the quality of supervised training data for CNN-based localization.

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An Efficient Deep Learning Approach to Pneumonia Classification in Healthcare

Journal of Healthcare Engineering ◽

10.1155/2019/4180949 ◽

2019 ◽

Vol 2019 ◽

pp. 1-7 ◽

Cited By ~ 44

Author(s):

Okeke Stephen ◽

Mangal Sain ◽

Uchenna Joseph Maduh ◽

Do-Un Jeong

Keyword(s):

Neural Network ◽

Deep Learning ◽

Convolutional Neural Network ◽

Network Model ◽

Neural Network Model ◽

Data Augmentation ◽

Classification Performance ◽

Learning Approaches ◽

X Ray ◽

Chest X Ray

This study proposes a convolutional neural network model trained from scratch to classify and detect the presence of pneumonia from a collection of chest X-ray image samples. Unlike other methods that rely solely on transfer learning approaches or traditional handcrafted techniques to achieve a remarkable classification performance, we constructed a convolutional neural network model from scratch to extract features from a given chest X-ray image and classify it to determine if a person is infected with pneumonia. This model could help mitigate the reliability and interpretability challenges often faced when dealing with medical imagery. Unlike other deep learning classification tasks with sufficient image repository, it is difficult to obtain a large amount of pneumonia dataset for this classification task; therefore, we deployed several data augmentation algorithms to improve the validation and classification accuracy of the CNN model and achieved remarkable validation accuracy.

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Detection of Brief Episodes of Atrial Fibrillation Based on Electrocardiomatrix and Convolutional Neural Network

Frontiers in Physiology ◽

10.3389/fphys.2021.673819 ◽

2021 ◽

Vol 12 ◽

Author(s):

Ricardo Salinas-Martínez ◽

Johannes de Bie ◽

Nicoletta Marzocchi ◽

Frida Sandberg

Keyword(s):

Neural Network ◽

Atrial Fibrillation ◽

Deep Learning ◽

Convolutional Neural Network ◽

Thrombus Formation ◽

Normal Sinus Rhythm ◽

Morphological Characteristics ◽

P Wave ◽

Learning Approaches ◽

Normal Sinus

Background: Brief episodes of atrial fibrillation (AF) may evolve into longer AF episodes increasing the chances of thrombus formation, stroke, and death. Classical methods for AF detection investigate rhythm irregularity or P-wave absence in the ECG, while deep learning approaches profit from the availability of annotated ECG databases to learn discriminatory features linked to different diagnosis. However, some deep learning approaches do not provide analysis of the features used for classification. This paper introduces a convolutional neural network (CNN) approach for automatic detection of brief AF episodes based on electrocardiomatrix-images (ECM-images) aiming to link deep learning to features with clinical meaning.Materials and Methods: The CNN is trained using two databases: the Long-Term Atrial Fibrillation and the MIT-BIH Normal Sinus Rhythm, and tested on three databases: the MIT-BIH Atrial Fibrillation, the MIT-BIH Arrhythmia, and the Monzino-AF. Detection of AF is done using a sliding window of 10 beats plus 3 s. Performance is quantified using both standard classification metrics and the EC57 standard for arrhythmia detection. Layer-wise relevance propagation analysis was applied to link the decisions made by the CNN to clinical characteristics in the ECG.Results: For all three testing databases, episode sensitivity was greater than 80.22, 89.66, and 97.45% for AF episodes shorter than 15, 30 s, and for all episodes, respectively.Conclusions: Rhythm and morphological characteristics of the electrocardiogram can be learned by a CNN from ECM-images for the detection of brief episodes of AF.

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