scholarly journals Insect Detection in Rice Crop using Google Code Lab

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
K. Sumathi, Et. al.
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Agricultural Sector ◽  
Quality Analysis ◽  
Leaf Quality ◽  
Insect Identification ◽  
The Neural Network ◽  
Physical Classification ◽  
Score Map ◽  
The Moment

Herb plants are essential in the medical field today and can help humans. Phyllanthus Elegans Wall is used in this study to analyse and categorize whether it is a safe or unhealthy leaf. At the moment, most insect identification methods rely on physical classification, making it difficult to automatically, quickly, and reliably identify in stored grains. The concept of this research is to ascertain the quality of leaves by combining technology with pesticide classification in the agricultural sector. Picture collection, image processing, and classification are the first steps in enhancing leaf quality analysis. The segmentation using HSV to input RGB image for the colour alteration structure is the most significant image processing method for this section. The colour and shape of a leaf disease image are used to analyse it. Insect detection in complex backgrounds is more versatile with the score map that is decision alternate highly interconnected layer, and our detection speed has upgraded. Finally, the taxonomy approach employs an algorithm that feeds directly that employs formation backwards techniques. The result shows a Many-layer Preceptor and Nonlinear Activation Feature comparison, as well as a percentage of accuracy contrast between MLP and RBF. MLP and RBF are neural network algorithms. Clearly, the Neural Network classifier has a better presentation and precision.  

scholarly journals Computer image processing and neural network technology for boiler thermal energy diagnosis

2020 ◽  
Vol 24 (5 Part B) ◽  
pp. 3059-3068
Author(s):  
Qinghong Wu
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Thermal Energy ◽  
Processing System ◽  
Heat Energy ◽  
Image Feature ◽  
The Neural Network ◽  
Computer Image Processing ◽  
On Line ◽  
Parameter Values

The paper uses the flame image processing technology to diagnose the furnace flame combustion achieve the measurement of boiler heat energy. The paper obtains the combustion image of the flame image processing system, and extracts the flame image characteristics of the boiler thermal energy diagnosis, constructs the neural network model of the boiler thermal energy diagnosis, and trains and tests the extracted flame image feature parameter values as the input of the neural network. A rough diagnosis of the boiler?s thermal energy is obtained while predicting the state of combustion. According to the research results, a boiler thermal energy diagnosis system was designed and tested on the boiler of 200 MW unit. The experimental results confirmed the applicability of the system, which can realize on-line monitoring of boiler heat energy and evaluate the combustion situation.

Artificial neural networks in the problem of determining the position and size of the solar disk in wide-angle photographs of the sky

2021 ◽  
Author(s):  
Mikhail Borisov ◽  
Mikhail Krinitskiy
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Artificial Neural Networks ◽  
Solar Disk ◽  
Weather Conditions ◽  
Wide Angle ◽  
Know How ◽  
The Neural Network ◽  
Artificial Neural ◽  
The Moment

<p>Total cloud score is a characteristic of weather conditions. At the moment, there are algorithms that automatically calculate cloudiness based on a photograph of the sky These algorithms do not know how to find the solar disk, so their work is not absolutely accurate.</p><p>To create an algorithm that solves this data, the data used, obtained as a result of sea research voyages, is used, which is marked up for training the neural network.</p><p>As a result of the work, an algorithm was obtained based on neural networks, based on a photograph of the sky, in order to determine the size and position of the solar disk, other algorithms can be used to work with images of the visible hemisphere of the sky.</p>

scholarly journals Using high-performance deep learning platform to accelerate object detection

2019 ◽  
pp. 354-360 ◽  
Author(s):  
S O Stepanenko ◽  
P Y Yakimov
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Image Processing ◽  
Processing Speed ◽  
High Performance ◽  
Object Classification ◽  
High Accuracy ◽  
Learning Platform ◽  
The Neural Network

Object classification with use of neural networks is extremely current today. YOLO is one of the most often used frameworks for object classification. It produces high accuracy but the processing speed is not high enough especially in conditions of limited performance of a computer. This article researches use of a framework called NVIDIA TensorRT to optimize YOLO with the aim of increasing the image processing speed. Saving efficiency and quality of the neural network work TensorRT allows us to increase the processing speed using an optimization of the architecture and an optimization of calculations on a GPU.

scholarly journals Colour recognition using colour histogram feature extraction and K-nearest neighbour classifier

2020 ◽  
pp. 08-14
Author(s):  
Rabia Bayraktar ◽  
Batur Alp Akgul ◽  
Kadir Sercan Bayram
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Feature Extraction ◽  
Video Camera ◽  
Training Data ◽  
Machine Learning Classification ◽  
The Neural Network ◽  
Knn Classifier ◽  
Nearest Neighbour Classifier ◽  
Histogram Feature

K-nearest neighbours (KNN) is a widely used neural network and machine learning classification algorithm. Recently, it has been used in the neural network and digital image processing fields. In this study, the KNN classifier is used to distinguish 12 different colours. These colours are black, blue, brown, forest green, green, navy, orange, pink, red, violet, white and yellow. Using colour histogram feature extraction, which is one of the image processing techniques, the features that distinguish these colours are determined. These features increase the effectiveness of the KNN classifier. The training data consist of saved frames and the test data are obtained from the video camera in real-time. The video consists of consecutive frames. The frames are 100 × 70 in size. Each frame is tested with K = 3,5,7,9 and the obtained results are recorded. In general, the best results are obtained when used K = 5.   Keywords: KNN algorithm, classifier, application, neural network, image processing, developed, colour, dataset, colour recognition.

Cotton Leaf Disease Detection by Feature Extraction

2016 ◽  
pp. 89-104
Author(s):  
Savita N. Ghaiwat ◽  
Parul Arora
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Image Processing ◽  
Feature Extraction ◽  
Early Stage ◽  
Cotton Leaf ◽  
Control Methods ◽  
The Neural Network ◽  
The World ◽  
Leaf Image

Cotton leaf diseases have occurred all over the world, including India. They adversely affect cotton quality and yield. Technology can help in identifying disease in early stage so that effective treatment can be given immediately. Now, the control methods rely mainly on artificial means. This paper propose application of image processing and machine learning in identifying three cotton leaf diseases through feature extraction. Using image processing, 12 types of features are extracted from cotton leaf image then the pattern was learned using BP Neural Network method in machine learning process. Three diseases have been diagnosed, namely Powdery mildew, Downy mildew and leafminer. The Neural Network classification performs well and could successfully detect and classify the tested disease.

LEAF DISEASE CLASSIFICATION USING ARTIFICIAL NEURAL NETWORK

2015 ◽  
Vol 77 (17) ◽  
Author(s):  
Syafiqah Ishak ◽  
Mohd Hafiz Fazalul Rahiman ◽  
Siti Nurul Aqmariah Mohd Kanafiah ◽  
Hashim Saad
Keyword(s):  
Breast Cancer ◽  
Neural Network ◽  
Image Processing ◽  
Back Propagation ◽  
Disease Classification ◽  
Back Propagation Algorithm ◽  
Feed Forward Neural Network ◽  
Leaf Quality ◽  
Leaf Disease ◽  
Color Transformation

Nowadays, herb plants are importance to medical field and can give benefit to human. In this research, Phyllanthus Elegans Wall (Asin-Asin Gajah) is used to analyse and to classify whether it is healthy or unhealthy leaf. This plant was chosen because its function can cure breast cancer. Therefore, there is a need for alternative cure for patient of breast cancer rather than use the technology such as Chemotherapy, surgery or use of medicine from hospital. The purpose of this research to identify the quality of leaf and using technology in agriculture field. The process to analysis the leaf quality start from image acquisition, image processing, and classification. For image processing method, the most important for this part is the segmentation using HSV to input RGB image for the color transformation structure. The analysis of leaf disease image is applied based on colour and shape. Finally, the classification method use feed-forward Neural Network, which uses Back-propagation algorithm. The result shows comparison between Multi-layer Perceptron (MLP) and Radial Basis Function (RBF) and comparison between MLP and RBF shown in percentage of accuracy. MLP and RBF is algorithm for Neural Network. Conclusively, classifier of Neural Network shows better performance and more accuracy.

scholarly journals Stability of the Stochastic Reaction-Diffusion Neural Network with Time-Varying Delays andp-Laplacian

2012 ◽  
Vol 2012 ◽  
pp. 1-10 ◽  
Author(s):  
Pan Qingfei ◽  
Zhang Zifang ◽  
Huang Jingchang
Keyword(s):  
Neural Network ◽  
Exponential Stability ◽  
Reaction Diffusion ◽  
Time Varying ◽  
The Neural Network ◽  
The Moment ◽  
Delay Differential ◽  
Stochastic Reaction ◽  
Moment Exponential Stability ◽  
Delay Differential Inequality

The main aim of this paper is to discuss moment exponential stability for a stochastic reaction-diffusion neural network with time-varying delays andp-Laplacian. Using the Itô formula, a delay differential inequality and the characteristics of the neural network, the algebraic conditions for the moment exponential stability of the nonconstant equilibrium solution are derived. An example is also given for illustration.

Gear Noise Diagnosis System for Automobile Transmission Using Artificial Intelligence (Effect of Number of Intermediate Layers on Judgement Performance)

2013 ◽  
Author(s):  
Morimasa Nakamura ◽  
Masahiko Nishiyama ◽  
Ichiro Moriwaki
Keyword(s):  
Neural Network ◽  
Moment Method ◽  
Network System ◽  
Noise Levels ◽  
Diagnosis System ◽  
Gear Noise ◽  
Intermediate Layers ◽  
The Neural Network ◽  
Neural Network System ◽  
The Moment

The present paper describes a digitizing method for the measured gear noise and a construction of a neural network system for gear noise diagnosis. Gear noise emitted from automobile transmissions should be evaluated by gear noise experts. Although quietness performance estimates from measured noise levels of the transmissions on some production lines, the estimation must be very difficult. There is not a certain relationship between the measured noise levels and the evaluations by the gear noise experts. Therefore, the estimation should be severe. As a result, such an automatic gear noise diagnosis system must yield transmissions with over-quality. The present study deals with a new gear noise diagnosis system to which an artificial intelligence, that is, a neural network system is applied. The previous evaluations by the new gear noise diagnosis system were good when the statistical property of the teacher signals from which the neural network system learned was similar to that of population. This fact means that many teacher signals are necessary on the practical use. Proposed digitizing method of gear noise levels provided good evaluations of neural network system even when the statistical properties of the teacher signals were not similar to that of the population. In addition, a new method, “Moment method” for determining the construction of the neural network system was introduced instead of “Back Propagation Method”. The Moment Method contributed to the improvement of the system judgments. The neural network system constructed using the Moment Method brought good performance. And the number of intermediate layers in the neural network system could be small enough to obtain good performance. It was found that the Moment method provided good learning because of connecting weights update function. When the Moment method was used for determining the connection weights between neurons in the neural network system, the developed gear noise diagnosis system achieved high and stable correct answer ratio. And the number of intermediate layers in the neural network system was only one enough for obtaining good performance of the system. Four intermediate layers, which was the maximum in this paper, did not provide much good performance.

scholarly journals An Android Application for Plant Leaf Disease Detection using Convolution Neural Network

2019 ◽  
Vol 8 (12) ◽  
pp. 4133-4137
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Agricultural Sector ◽  
Agricultural Productivity ◽  
Agricultural Product ◽  
Android Application ◽  
Plant Leaf ◽  
Important Concern ◽  
Plant Area ◽  
Detection And Recognition

Plant leaf diseases and ruinous insects are an important concern in the agricultural sector. The agriculture is dependent on the agricultural productivity by the country, the better is the agricultural productivity, the better is the economy, and hence better is the GDP. The most common and useful way of boosting this economy for any country is the identification of these diseases in the plant and agricultural product that has been obtained. Developments in Deep Learning have facilitated researchers to improve the performance and in exacting systems for object detection and recognition. In this paper, we propose an image processing and Convolutional Neural Network based approach to detect the diseases affecting plants. Our goal is to develop an Android application with a suitable algorithm that will help automate the process of monitoring and detecting plant health. The proposed android application can effectively detect and identify various types of diseases with the ability to handle complex plant-area scenarios

scholarly journals Neural Network Controller Application on a Visual based Object Tracking and Following Robot

2019 ◽  
Vol 8 (1) ◽  
pp. 31-40
Author(s):  
Pola Risma ◽  
Tresna Dewi ◽  
Yurni Oktarina ◽  
Yudi Wijanarko
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Object Tracking ◽  
Effective Control ◽  
Computational Time ◽  
Control Input ◽  
Distance Information ◽  
Neural Network Controller ◽  
The Neural Network ◽  
Network Controller

Navigation is the main issue for autonomous mobile robot due to its mobility in an unstructured environment. The autonomous object tracking and following robot has been applied in many places such as transport robot in industry and hospital, and as an entertainment robot. This kind of image processing based navigation requires more resources for computational time, however microcontroller currently applied to a robot has limited memory. Therefore, effective image processing from a vision sensor and obstacle avoidances from distance sensors need to be processed efficiently. The application of neural network can be an alternative to get a faster trajectory generation. This paper proposes a simple image processing and combines image processing result with distance information to the obstacles from distance sensors. The combination is conducted by the neural network to get the effective control input for robot motion in navigating through its assigned environment. The robot is deployed in three different environmental setting to show the effectiveness of the proposed method. The experimental results show that the robot can navigate itself effectively within reasonable time periods.

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