scholarly journals Friendly Farmer

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.

scholarly journals Comparative Study of Various Convolutional Neural Networks on Cifar-10

2020 ◽  
Vol 6 (12) ◽  
pp. 402-406
Author(s):  
Tushar Goyal
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Computer Vision ◽  
Comparative Study ◽  
Convolutional Neural Network ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
State Of The Art ◽  
Fair Comparison ◽  
Develop State

Image recognition plays a foundational role in the field of computer vision and there has been extensive research to develop state-of-the-art techniques especially using Convolutional Neural Network (CNN). This paper aims to study some CNNs, heavily inspired by highly popular state-of-the-art CNNs, designed from scratch specifically for the Cifar-10 dataset and present a fair comparison between them.

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

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.

scholarly journals Convolutional Neural Networks as a Model of the Visual System: Past, Present, and Future

2020 ◽  
pp. 1-15 ◽  
Author(s):  
Grace W. Lindsay
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Convolutional Neural Networks ◽  
Computational Neuroscience ◽  
Neural Activity ◽  
Vision Research ◽  
State Of The Art ◽  
Biological Vision ◽  
Visual Tasks ◽  
And Behavior

Convolutional neural networks (CNNs) were inspired by early findings in the study of biological vision. They have since become successful tools in computer vision and state-of-the-art models of both neural activity and behavior on visual tasks. This review highlights what, in the context of CNNs, it means to be a good model in computational neuroscience and the various ways models can provide insight. Specifically, it covers the origins of CNNs and the methods by which we validate them as models of biological vision. It then goes on to elaborate on what we can learn about biological vision by understanding and experimenting on CNNs and discusses emerging opportunities for the use of CNNs in vision research beyond basic object recognition.

scholarly journals Plant disease severity estimation by computer vision methods

2021 ◽  
Vol 51 (2) ◽  
pp. 107-112
Author(s):  
V. G. Altukhov
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Leaf Area ◽  
Disease Severity ◽  
Convolutional Neural Networks ◽  
Plant Disease ◽  
Plant Diseases ◽  
Visual Features ◽  
Lesion Area ◽  
Disease Progress

The first stage results within the framework of the thesis “Investigation of computer vision methods and algorithms in the field of plant diseases detection” are presented. The analysis of the work related to the automatic assessment of plant disease severity was carried out. It was established that for solving problems in this field, convolution neural networks are promising methods, which are currently superior to classical methods of computer vision in terms of accuracy. To assess the severity degree, classification and segmentation architectures of convolutional neural networks are used. Classification architectures are able to take into account disease visual features at different stages of the disease development, but information about the actual affected area is unavailable. On the other hand, solutions based on segmentation architectures provide actual data on the lesion area, but do not grade severity levels according to disease visual features. Based on the result of the research into the application of convolutional neural networks and options for their use, the goal of this study was determined, which is to develop an automatic system capable of determining the lesion area, as well as to take into account disease visual features and the type of immunological reaction of the plant at different stages of disease progress. It is planned to build a system based on the segmentation architecture of a convolutional neural network, which will produce multi-class image segmentation. Such a network is able to divide image pixels into several classes: background, healthy leaf area, affected leaf area. In turn, the class "affected leaf area" will include several subclasses corresponding to the disease visual features at different stages of disease progress.

scholarly journals Towards Interpretable Semantic Segmentation via Gradient-Weighted Class Activation Mapping (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13943-13944
Author(s):  
Kira Vinogradova ◽  
Alexandr Dibrov ◽  
Gene Myers
Keyword(s):  
Neural Networks ◽  
Image Segmentation ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Image Recognition ◽  
State Of The Art ◽  
Semantic Segmentation ◽  
Wide Range ◽  
Gradient Based ◽  
Activation Mapping

Convolutional neural networks have become state-of-the-art in a wide range of image recognition tasks. The interpretation of their predictions, however, is an active area of research. Whereas various interpretation methods have been suggested for image classification, the interpretation of image segmentation still remains largely unexplored. To that end, we propose seg-grad-cam, a gradient-based method for interpreting semantic segmentation. Our method is an extension of the widely-used Grad-CAM method, applied locally to produce heatmaps showing the relevance of individual pixels for semantic segmentation.

scholarly journals Exploiting Features with Split-and-Share Module

Electronics ◽  
2022 ◽  
Vol 11 (2) ◽  
pp. 235
Author(s):  
Jae-Min Lee ◽  
Min-Seok Seo ◽  
Dae-Han Kim ◽  
Sang-Woo Lee ◽  
Jong-Chan Park ◽  
...  
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Convolutional Neural Networks ◽  
State Of The Art ◽  
Structural Characteristics ◽  
Classification Task ◽  
Deep Convolutional Neural Networks

Deep convolutional neural networks (CNNs) have shown state-of-the-art performances in various computer vision tasks. Advances on CNN architectures have focused mainly on designing convolutional blocks of the feature extractors, but less on the classifiers that exploit extracted features. In this work, we propose Split-and-Share Module (SSM), a classifier that splits a given feature into parts, which are partially shared by multiple sub-classifiers. Our intuition is that the more the features are shared, the more common they will become, and SSM can encourage such structural characteristics in the split features. SSM can be easily integrated into any architecture without bells and whistles. We have extensively validated the efficacy of SSM on ImageNet-1K classification task, and SSM has shown consistent and significant improvements over baseline architectures. In addition, we analyze the effect of SSM using the Grad-CAM visualization.

scholarly journals SOFTWARE FOR FOREST SPECIES RECOGNITION BASED ON DIGITAL IMAGES OF WOOD

FLORESTA ◽  
2019 ◽  
Vol 49 (3) ◽  
pp. 543
Author(s):  
Wellington De Oliveira ◽  
Pedro Luiz de Paula Filho ◽  
Jefferson Gustavo Martins
Keyword(s):  
Neural Networks ◽  
Computer Vision ◽  
Convolutional Neural Networks ◽  
Intelligent Systems ◽  
Digital Images ◽  
Environmental Control ◽  
State Of The Art ◽  
Species Recognition ◽  
Forest Species ◽  
Anatomical Analysis

Classifying forest species is an essential process for the correct management of wood and forest control. After cutting off the trunk of the tree, many of the characteristics of the species are lost and identifying them becomes a much more difficult task. In this context, an anatomical analysis of the wood becomes necessary, needing to be done by specialists who know very well the cellular structures of each species. However, such methodology approaches few automated techniques, making it a delayed and error-prone activity. These factors undermine environmental control and decision-making. The use of computer vision is an alternative to automatic recognition, since it allows the development of intelligent systems, in which, from images, are able to detect features and perform a final classification. One of the techniques of Computer Vision is the use of Convolutional Neural Networks, technique that represents the state of the art in this area, it is the construction of models capable of interpreting patterns in images. This research addresses experiments using convolutional neural networks for recognizing forest species from digital images. Two original datasets were used, one including macroscopic images and the other including microscopic images, for which three models were created: scale recognition, species recognition from macroscopic images and species recognition from microscopic. The best models provide 100% recognition rates for the scale dataset, 98.73% for the macroscopic and 99.11% for the microscopic which made possible to develop a software as a final product, using these three models.

scholarly journals Using spatial-temporal ensembles of convolutional neural networks for lumen segmentation in ureteroscopy

2021 ◽  
Author(s):  
Jorge F. Lazo ◽  
Aldo Marzullo ◽  
Sara Moccia ◽  
Michele Catellani ◽  
Benoit Rosa ◽  
...  
Keyword(s):  
Neural Networks ◽  
Convolutional Neural Networks ◽  
State Of The Art ◽  
Automatic Segmentation ◽  
Temporal Information ◽  
Invasive Technique ◽  
Dice Similarity Coefficient ◽  
Specular Reflections ◽  
Lumen Segmentation ◽  
Previous State

Abstract Purpose Ureteroscopy is an efficient endoscopic minimally invasive technique for the diagnosis and treatment of upper tract urothelial carcinoma. During ureteroscopy, the automatic segmentation of the hollow lumen is of primary importance, since it indicates the path that the endoscope should follow. In order to obtain an accurate segmentation of the hollow lumen, this paper presents an automatic method based on convolutional neural networks (CNNs). Methods The proposed method is based on an ensemble of 4 parallel CNNs to simultaneously process single and multi-frame information. Of these, two architectures are taken as core-models, namely U-Net based in residual blocks ($$m_1$$ m 1 ) and Mask-RCNN ($$m_2$$ m 2 ), which are fed with single still-frames I(t). The other two models ($$M_1$$ M 1 , $$M_2$$ M 2 ) are modifications of the former ones consisting on the addition of a stage which makes use of 3D convolutions to process temporal information. $$M_1$$ M 1 , $$M_2$$ M 2 are fed with triplets of frames ($$I(t-1)$$ I ( t - 1 ) , I(t), $$I(t+1)$$ I ( t + 1 ) ) to produce the segmentation for I(t). Results The proposed method was evaluated using a custom dataset of 11 videos (2673 frames) which were collected and manually annotated from 6 patients. We obtain a Dice similarity coefficient of 0.80, outperforming previous state-of-the-art methods. Conclusion The obtained results show that spatial-temporal information can be effectively exploited by the ensemble model to improve hollow lumen segmentation in ureteroscopic images. The method is effective also in the presence of poor visibility, occasional bleeding, or specular reflections.

scholarly journals Image Classification for the Automatic Feature Extraction in Human Worn Fashion Data

Mathematics ◽  
2021 ◽  
Vol 9 (6) ◽  
pp. 624
Author(s):  
Stefan Rohrmanstorfer ◽  
Mikhail Komarov ◽  
Felix Mödritscher
Keyword(s):  
Neural Networks ◽  
Feature Extraction ◽  
Image Classification ◽  
Convolutional Neural Networks ◽  
Data Augmentation ◽  
State Of The Art ◽  
Image Data ◽  
Classification Model ◽  
Upper Body ◽  
Automatic Feature Extraction

With the always increasing amount of image data, it has become a necessity to automatically look for and process information in these images. As fashion is captured in images, the fashion sector provides the perfect foundation to be supported by the integration of a service or application that is built on an image classification model. In this article, the state of the art for image classification is analyzed and discussed. Based on the elaborated knowledge, four different approaches will be implemented to successfully extract features out of fashion data. For this purpose, a human-worn fashion dataset with 2567 images was created, but it was significantly enlarged by the performed image operations. The results show that convolutional neural networks are the undisputed standard for classifying images, and that TensorFlow is the best library to build them. Moreover, through the introduction of dropout layers, data augmentation and transfer learning, model overfitting was successfully prevented, and it was possible to incrementally improve the validation accuracy of the created dataset from an initial 69% to a final validation accuracy of 84%. More distinct apparel like trousers, shoes and hats were better classified than other upper body clothes.

scholarly journals Canopy Height Estimation at Landsat Resolution Using Convolutional Neural Networks

2020 ◽  
Vol 2 (1) ◽  
pp. 23-36
Author(s):  
Syed Aamir Ali Shah ◽  
Muhammad Asif Manzoor ◽  
Abdul Bais
Keyword(s):  
Neural Networks ◽  
Random Forest ◽  
Convolutional Neural Networks ◽  
Forest Structure ◽  
State Of The Art ◽  
Spatial Association ◽  
Canopy Height ◽  
Landsat Images ◽  
Structure Estimation ◽  
Vegetation Height

Forest structure estimation is very important in geological, ecological and environmental studies. It provides the basis for the carbon stock estimation and effective means of sequestration of carbon sources and sinks. Multiple parameters are used to estimate the forest structure like above ground biomass, leaf area index and diameter at breast height. Among all these parameters, vegetation height has unique standing. In addition to forest structure estimation it provides the insight into long term historical changes and the estimates of stand age of the forests as well. There are multiple techniques available to estimate the canopy height. Light detection and ranging (LiDAR) based methods, being the accurate and useful ones, are very expensive to obtain and have no global coverage. There is a need to establish a mechanism to estimate the canopy height using freely available satellite imagery like Landsat images. Multiple studies are available which contribute in this area. The majority use Landsat images with random forest models. Although random forest based models are widely used in remote sensing applications, they lack the ability to utilize the spatial association of neighboring pixels in modeling process. In this research work, we define Convolutional Neural Network based model and analyze that model for three test configurations. We replicate the random forest based setup of Grant et al., which is a similar state-of-the-art study, and compare our results and show that the convolutional neural networks (CNN) based models not only capture the spatial association of neighboring pixels but also outperform the state-of-the-art.

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