Fungus Detection using Convolutional Neural Networks

The fungus is enormously important for food, human health, and the surrounding. Fungus sign and symptoms in the food, medical science and any non-specific field which is an extremely large area which will result in us the challenging task for the fungus detection. Various traditional, as well as modern computer vision techniques, were applied to meet the challenge in the early days of fungus detection. Another main challenge that has been raised is that obtaining the enormous amount of dataset which is been related to the fungus detection and the processing of it. Despite this challenge, another phase that includes the classification of dataset separately and identifying the fungus presence, owing to all these difficulties, Transfer learning has been used in the approach to get multiplying our dataset. In pursuing this idea, we present a novel fungus dataset of its kind, with the goal of an advancing the State-of-the-art in fungus classification by placing the question of fungus detection.

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Very Deep Convolutional Neural Networks for Complex Land Cover Mapping Using Multispectral Remote Sensing Imagery

Remote Sensing ◽

10.3390/rs10071119 ◽

2018 ◽

Vol 10 (7) ◽

pp. 1119 ◽

Cited By ~ 102

Author(s):

Masoud Mahdianpari ◽

Bahram Salehi ◽

Mohammad Rezaee ◽

Fariba Mohammadimanesh ◽

Yun Zhang

Keyword(s):

Remote Sensing ◽

Neural Networks ◽

Computer Vision ◽

Support Vector Machine ◽

State Of The Art ◽

Support Vector ◽

Learning Tools ◽

Deep Convolutional Neural Networks ◽

Multispectral Remote Sensing

Despite recent advances of deep Convolutional Neural Networks (CNNs) in various computer vision tasks, their potential for classification of multispectral remote sensing images has not been thoroughly explored. In particular, the applications of deep CNNs using optical remote sensing data have focused on the classification of very high-resolution aerial and satellite data, owing to the similarity of these data to the large datasets in computer vision. Accordingly, this study presents a detailed investigation of state-of-the-art deep learning tools for classification of complex wetland classes using multispectral RapidEye optical imagery. Specifically, we examine the capacity of seven well-known deep convnets, namely DenseNet121, InceptionV3, VGG16, VGG19, Xception, ResNet50, and InceptionResNetV2, for wetland mapping in Canada. In addition, the classification results obtained from deep CNNs are compared with those based on conventional machine learning tools, including Random Forest and Support Vector Machine, to further evaluate the efficiency of the former to classify wetlands. The results illustrate that the full-training of convnets using five spectral bands outperforms the other strategies for all convnets. InceptionResNetV2, ResNet50, and Xception are distinguished as the top three convnets, providing state-of-the-art classification accuracies of 96.17%, 94.81%, and 93.57%, respectively. The classification accuracies obtained using Support Vector Machine (SVM) and Random Forest (RF) are 74.89% and 76.08%, respectively, considerably inferior relative to CNNs. Importantly, InceptionResNetV2 is consistently found to be superior compared to all other convnets, suggesting the integration of Inception and ResNet modules is an efficient architecture for classifying complex remote sensing scenes such as wetlands.

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Deep learning-enabled medical computer vision

npj Digital Medicine ◽

10.1038/s41746-020-00376-2 ◽

2021 ◽

Vol 4 (1) ◽

Author(s):

Andre Esteva ◽

Katherine Chou ◽

Serena Yeung ◽

Nikhil Naik ◽

Ali Madani ◽

...

Keyword(s):

Artificial Intelligence ◽

Neural Networks ◽

Computer Vision ◽

Deep Learning ◽

Medical Imaging ◽

Real World ◽

Recent Progress ◽

Medical Applications ◽

Modern Computer ◽

Medical Computer

AbstractA decade of unprecedented progress in artificial intelligence (AI) has demonstrated the potential for many fields—including medicine—to benefit from the insights that AI techniques can extract from data. Here we survey recent progress in the development of modern computer vision techniques—powered by deep learning—for medical applications, focusing on medical imaging, medical video, and clinical deployment. We start by briefly summarizing a decade of progress in convolutional neural networks, including the vision tasks they enable, in the context of healthcare. Next, we discuss several example medical imaging applications that stand to benefit—including cardiology, pathology, dermatology, ophthalmology–and propose new avenues for continued work. We then expand into general medical video, highlighting ways in which clinical workflows can integrate computer vision to enhance care. Finally, we discuss the challenges and hurdles required for real-world clinical deployment of these technologies.

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Friendly Farmer

International Journal of Advanced Research in Science, Communication and Technology ◽

10.48175/ijarsct-1414 ◽

2021 ◽

pp. 488-491

Author(s):

Ritwik Chavhan ◽

Kadir Sheikh ◽

Rishikesh Bondade ◽

Swaraj Dhanulkar ◽

Aniket Ninave ◽

...

Keyword(s):

Neural Networks ◽

Computer Vision ◽

Food Security ◽

Convolutional Neural Networks ◽

Image Recognition ◽

Associate Degree ◽

Plant Disease ◽

State Of The Art ◽

Smallholder Farmers ◽

Definite Diagnosis

Plant disease is an ongoing challenge for smallholder farmers, which threatens income and food security. The recent revolution in smartphone penetration and computer vision models has created an opportunity for image classification in agriculture. The project focuses on providing the data relating to the pesticide/insecticide and therefore the quantity of pesticide/insecticide to be used for associate degree unhealthy crop. The user, is that the farmer clicks an image of the crop and uploads it to the server via the humanoid application. When uploading the image the farmer gets associate degree distinctive ID displayed on his application screen. The farmer must create note of that ID since that ID must be utilized by the farmer later to retrieve the message when a minute. The uploaded image is then processed by Convolutional Neural Networks. Convolutional Neural Networks (CNNs) are considered state-of-the-art in image recognition and offer the ability to provide a prompt and definite diagnosis. Then the result consisting of the malady name and therefore the affected space is retrieved. This result's then uploaded into the message table within the server. Currently the Farmer are going to be ready to retrieve the whole info during a respectable format by coming into the distinctive ID he had received within the Application.

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Modified Deep Neural Networks for Dog Breeds Identification

10.20944/preprints201812.0232.v1 ◽

2018 ◽

Cited By ~ 1

Author(s):

Aydin Ayanzadeh ◽

Sahand Vahidnia

Keyword(s):

Neural Networks ◽

Deep Neural Networks ◽

State Of The Art ◽

The State ◽

Fine Tuning ◽

Test Accuracy ◽

Data Sets ◽

Data Set

In this paper, we leverage state of the art models on Imagenet data-sets. We use the pre-trained model and learned weighs to extract the feature from the Dog breeds identification data-set. Afterwards, we applied fine-tuning and dataaugmentation to increase the performance of our test accuracy in classification of dog breeds datasets. The performance of the proposed approaches are compared with the state of the art models of Image-Net datasets such as ResNet-50, DenseNet-121, DenseNet-169 and GoogleNet. we achieved 89.66% , 85.37% 84.01% and 82.08% test accuracy respectively which shows thesuperior performance of proposed method to the previous works on Stanford dog breeds datasets.

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Convolutional Neural Networks Inference Memory Optimization with Receptive Field-Based InputTiling

10.21203/rs.3.rs-743636/v1 ◽

2021 ◽

Author(s):

Weihao Zhuang ◽

Tristan Hascoet ◽

Xunquan Chen ◽

Ryoichi Takashima ◽

Tetsuya Takiguchi ◽

...

Keyword(s):

Neural Networks ◽

Computer Vision ◽

Convolutional Neural Networks ◽

Language Processing ◽

State Of The Art ◽

Input Image ◽

Memory Consumption ◽

Excellent Performance ◽

Conceptual Approach ◽

Recent Developments

Abstract Currently, deep learning plays an indispensable role in many fields, including computer vision, natural language processing, and speech recognition. Convolutional Neural Networks (CNNs) have demonstrated excellent performance in computer vision tasks thanks to their powerful feature extraction capability. However, as the larger models have shown higher accuracy, recent developments have led to state-of-the-art CNN models with increasing resource consumption. This paper investigates a conceptual approach to reduce the memory consumption of CNN inference. Our method consists of processing the input image in a sequence of carefully designed tiles within the lower subnetwork of the CNN, so as to minimize its peak memory consumption, while keeping the end-to-end computation unchanged. This method introduces a trade-off between memory consumption and computations, which is particularly suitable for high-resolution inputs. Our experimental results show that MobileNetV2 memory consumption can be reduced by up to 5.3 times with our proposed method. For ResNet50, one of the most commonly used CNN models in computer vision tasks, memory can be optimized by up to 2.3 times.

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Quantification of the suitable rooftop area for solar panel installation from overhead imagery using Convolutional Neural Networks

Journal of Physics Conference Series ◽

10.1088/1742-6596/2042/1/012002 ◽

2021 ◽

Vol 2042 (1) ◽

pp. 012002

Author(s):

Roberto Castello ◽

Alina Walch ◽

Raphaël Attias ◽

Riccardo Cadei ◽

Shasha Jiang ◽

...

Keyword(s):

Neural Network ◽

Machine Learning ◽

Neural Networks ◽

Computer Vision ◽

State Of The Art ◽

Solar Panel ◽

Post Processing ◽

Processing Step ◽

Recent Method

Abstract The integration of solar technology in the built environment is realized mainly through rooftop-installed panels. In this paper, we leverage state-of-the-art Machine Learning and computer vision techniques applied on overhead images to provide a geo-localization of the available rooftop surfaces for solar panel installation. We further exploit a 3D building database to associate them to the corresponding roof geometries by means of a geospatial post-processing approach. The stand-alone Convolutional Neural Network used to segment suitable rooftop areas reaches an intersection over union of 64% and an accuracy of 93%, while a post-processing step using building database improves the rejection of false positives. The model is applied to a case study area in the canton of Geneva and the results are compared with another recent method used in the literature to derive the realistic available area.

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Analysis of Explainers of Black Box Deep Neural Networks for Computer Vision: A Survey

Machine Learning and Knowledge Extraction ◽

10.3390/make3040048 ◽

2021 ◽

Vol 3 (4) ◽

pp. 966-989

Author(s):

Vanessa Buhrmester ◽

David Münch ◽

Michael Arens

Keyword(s):

Neural Networks ◽

Computer Vision ◽

Deep Neural Networks ◽

State Of The Art ◽

Black Box ◽

Complex Data ◽

Comprehensive Overview ◽

Nonlinear Structure ◽

Black Boxes ◽

Insight Into

Deep Learning is a state-of-the-art technique to make inference on extensive or complex data. As a black box model due to their multilayer nonlinear structure, Deep Neural Networks are often criticized as being non-transparent and their predictions not traceable by humans. Furthermore, the models learn from artificially generated datasets, which often do not reflect reality. By basing decision-making algorithms on Deep Neural Networks, prejudice and unfairness may be promoted unknowingly due to a lack of transparency. Hence, several so-called explanators, or explainers, have been developed. Explainers try to give insight into the inner structure of machine learning black boxes by analyzing the connection between the input and output. In this survey, we present the mechanisms and properties of explaining systems for Deep Neural Networks for Computer Vision tasks. We give a comprehensive overview about the taxonomy of related studies and compare several survey papers that deal with explainability in general. We work out the drawbacks and gaps and summarize further research ideas.

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Investigating Deep Feedforward Neural Networks for Classification of Transposon-Derived piRNAs

10.1101/2020.04.08.032755 ◽

2020 ◽

Author(s):

Alisson Hayasi da Costa ◽

Renato Augusto C. dos Santos ◽

Ricardo Cerri

Keyword(s):

Neural Networks ◽

Deep Learning ◽

State Of The Art ◽

Feedforward Neural Networks ◽

The State ◽

Machine Learning Algorithms ◽

Support Vector ◽

Advantages And Disadvantages ◽

Large Application

AbstractPIWI-Interacting RNAs (piRNAs) form an important class of non-coding RNAs that play a key role in the genome integrity through the silencing of transposable elements. However, despite their importance and the large application of deep learning in computational biology for classification tasks, there are few studies of deep learning and neural networks for piRNAs prediction. Therefore, this paper presents an investigation on deep feedforward networks models for classification of transposon-derived piRNAs. We analyze and compare the results of the neural networks in different hyperparameters choices, such as number of layers, activation functions and optimizers, clarifying the advantages and disadvantages of each configuration. From this analysis, we propose a model for human piRNAs classification and compare our method with the state-of-the-art deep neural network for piRNA prediction in the literature and also traditional machine learning algorithms, such as Support Vector Machines and Random Forests, showing that our model has achieved a great performance with an F-measure value of 0.872, outperforming the state-of-the-art method in the literature.

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FruitVegCNN: Power- and Memory-Efficient Classification of Fruits & Vegetables Using CNN in Mobile MPSoC

10.36227/techrxiv.12686051.v1 ◽

2020 ◽

Author(s):

Somdip Dey ◽

Suman Saha ◽

Amit Singh ◽

Klaus D. Mcdonald-Maier

Keyword(s):

Neural Networks ◽

Convolutional Neural Networks ◽

Power Efficiency ◽

Fruits And Vegetables ◽

State Of The Art ◽

Agricultural Industry ◽

Power Efficient ◽

System On A Chip ◽

Memory Efficient

<div><div><div><p>Fruit and vegetable classification using Convolutional Neural Networks (CNNs) has become a popular application in the agricultural industry, however, to the best of our knowledge no previously recorded study has designed and evaluated such an application on a mobile platform. In this paper, we propose a power-efficient CNN model, FruitVegCNN, to perform classification of fruits and vegetables in a mobile multi-processor system-on-a-chip (MPSoC). We also evaluated the efficacy of FruitVegCNN compared to popular state-of-the-art CNN models in real mobile plat- forms (Huawei P20 Lite and Samsung Galaxy Note 9) and experimental results show the efficacy and power efficiency of our proposed CNN architecture.</p></div></div></div>

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Biologically Inspired Visual System Architecture for Object Recognition in Autonomous Systems

Algorithms ◽

10.3390/a13070167 ◽

2020 ◽

Vol 13 (7) ◽

pp. 167 ◽

Cited By ~ 2

Author(s):

Dan Malowany ◽

Hugo Guterman

Keyword(s):

Neural Networks ◽

Computer Vision ◽

Object Recognition ◽

Visual System ◽

Human Visual System ◽

State Of The Art ◽

Top Down ◽

Feed Forward ◽

Recognition Process ◽

The World

Computer vision is currently one of the most exciting and rapidly evolving fields of science, which affects numerous industries. Research and development breakthroughs, mainly in the field of convolutional neural networks (CNNs), opened the way to unprecedented sensitivity and precision in object detection and recognition tasks. Nevertheless, the findings in recent years on the sensitivity of neural networks to additive noise, light conditions, and to the wholeness of the training dataset, indicate that this technology still lacks the robustness needed for the autonomous robotic industry. In an attempt to bring computer vision algorithms closer to the capabilities of a human operator, the mechanisms of the human visual system was analyzed in this work. Recent studies show that the mechanisms behind the recognition process in the human brain include continuous generation of predictions based on prior knowledge of the world. These predictions enable rapid generation of contextual hypotheses that bias the outcome of the recognition process. This mechanism is especially advantageous in situations of uncertainty, when visual input is ambiguous. In addition, the human visual system continuously updates its knowledge about the world based on the gaps between its prediction and the visual feedback. CNNs are feed forward in nature and lack such top-down contextual attenuation mechanisms. As a result, although they process massive amounts of visual information during their operation, the information is not transformed into knowledge that can be used to generate contextual predictions and improve their performance. In this work, an architecture was designed that aims to integrate the concepts behind the top-down prediction and learning processes of the human visual system with the state-of-the-art bottom-up object recognition models, e.g., deep CNNs. The work focuses on two mechanisms of the human visual system: anticipation-driven perception and reinforcement-driven learning. Imitating these top-down mechanisms, together with the state-of-the-art bottom-up feed-forward algorithms, resulted in an accurate, robust, and continuously improving target recognition model.

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