scholarly journals The classification of hunger behaviour of Lates Calcarifer through the integration of image processing technique and k-Nearest Neighbour learning algorithm

2018 ◽  
Vol 342 ◽  
pp. 012017 ◽  
Author(s):  
Z Taha ◽  
M A M Razman ◽  
A S Abdul Ghani ◽  
A P P Abdul Majeed ◽  
R M Musa ◽  
...  
Keyword(s):  
Image Processing ◽  
Learning Algorithm ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Nearest Neighbour ◽  
Lates Calcarifer

An Enhanced Technique for Classification of Fruits using Shape Color and Texture Features

2017 ◽  
Vol 7 (7) ◽  
pp. 408
Author(s):  
Yashpal Jitarwal ◽  
Tabrej Ahamad Khan ◽  
Pawan Mangal
Keyword(s):  
Image Processing ◽  
Texture Features ◽  
Automatic Classification ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Advance Technique ◽  
Time Required ◽  
Fruit Sorting ◽  
Human Inspection

In earlier times fruits were sorted manually and it was very time consuming and laborious task. Human sorted the fruits of the basis of shape, size and color. Time taken by human to sort the fruits is very large therefore to reduce the time and to increase the accuracy, an automatic classification of fruits comes into existence.To improve this human inspection and reduce time required for fruit sorting an advance technique is developed that accepts information about fruits from their images, and is called as Image Processing Technique.

Image Processing Technique for the Detection of Alberseem Leaves Diseases Based on Soft Computing

2021 ◽  
pp. 103-115
Author(s):  
Eimad Abdu Abusham
Keyword(s):  
Image Processing ◽  
Traditional Method ◽  
Processing Technique ◽  
Plant Diseases ◽  
Image Processing Technique ◽  
Economic Losses ◽  
Image Processing Techniques ◽  
Appropriate Treatment ◽  
Processing Techniques

Detecting plant diseases using the traditional method such as the naked eye can sometimes lead to incorrect identification and classification of the diseases. Consequently, this traditional method can strongly contribute to the losses of the crop. Image processing techniques have been used as an approach to detect and classify plant diseases. This study aims to focus on the diseases affecting the leaves of al-berseem and how to use image processing techniques to detect al-berseem diseases. Early detection of diseases important for finding appropriate treatment quickly and avoid economic losses. Detect the plant disease is based on the symptoms and signs that appear on the leaves. The detection steps include image preprocessing, segmentation, and identification. The image noise is removed in the preprocessing stage by using the MATLAB features energy, mean, homogeneity, and others. The k-mean-clustering is used to detect the affected area in leaves. Finally, KNN will be used to recognize unhealthy leaves and determines disease types (fungal diseases, pest diseases (shall), leaf minor (red spider), and deficiency of nutrient (yellow leaf)); these four types of diseases will detect in this thesis. Identification is the last step in which the disease will identify and classified.

scholarly journals Klasifikasi Sel Darah Putih Berdasarkan Ciri Warna dan Bentuk dengan Metode K-Nearest Neighbor (K-NN)

2016 ◽  
Vol 6 (2) ◽  
pp. 151 ◽  
Author(s):  
Mizan Nur Khasanah ◽  
Agus Harjoko ◽  
Ika Candradewi
Keyword(s):  
Image Processing ◽  
Blood Cells ◽  
Nearest Neighbor ◽  
White Blood Cells ◽  
Processing Technique ◽  
Image Processing Technique ◽  
K Nearest Neighbor ◽  
Traditional Procedure ◽  
Early Diagnose

The traditional procedure of classification of blood cells using a microscope in the laboratory of hematology to obtain information types of blood cells. It has become a cornerstone in the laboratory of hematology to diagnose and monitor hematologic disorders. However, the manual procedure through a series of labory test can take a while. Thresfore, this research can be helpful in the early stages of the classification of white blood cells automatically in the medical field.Efforts to overcome the length of time and for the purposes of early diagnose can use the image processing technique based on morphology of blood cells. This research aims to classify the white blood cells based on cell morphology with the k-nearest neighbor (knn). Image processing algorithms used hough circle, thresholding, feature extraction, then to the process of classification was used the method of k-nearest neighbor (knn).In the process of testing used 100 images to be aware of its kind. The test results showed segmentation accuracy of 78% and testing the classification of 64%.

scholarly journals Classification of Human Heart Abnormality Using Time-Frequency and Image Processing Technique

2008 ◽  
Vol 20 (1) ◽  
pp. 183-190
Author(s):  
Fadzlul Rahimi Ahmad Bustami ◽  
◽  
Mohd Hanif Md. Saad ◽  
Mohd Jailani Mohd Nor ◽  
Bilkis Banu Aziz
Keyword(s):  
Image Processing ◽  
Human Heart ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Time Frequency

scholarly journals Colour Detection of Agriculture Products for State Analysis

2021 ◽  
Vol 9 (VI) ◽  
pp. 4188-4197
Author(s):  
Anees Banu
Keyword(s):  
Image Processing ◽  
Learning Algorithm ◽  
Processing Technique ◽  
The State ◽  
Image Processing Technique ◽  
Agricultural Product ◽  
Maturity Stage ◽  
Image Libraries ◽  
The Right ◽  
State Analysis

The present market demands recognition of state analysis of an agricultural product automatically rather than conventionally checking the maturity stage and ripeness of an agricultural product which is mundane . In this project we are going to determine the state of an agricultural product using machine learning algorithm with the aid of colour detection. Image processing has been a great help in all kinds of fields which also extended its applicability in agriculture as well . Determining the maturity of an agricultural product at the right time will be very much helpful for the farmers . So by implementing this algorithm the colour as well as the state of the fruit will be determined automatically when we click on the image. Libraries we have used are open CV and Pandas which will help to work with images and the statistical data we are using to convert them into RGB colour models through different functions in the jupyter notebook platform. The two important parts in a project are the prepossessing and the state analysis stages. Firstly, the pre-processing stage determines the colour by calculating the distance to tell how close we are to the actual colour and we will choose the one which has the minimum distance. The second stage is mainly to classify the ripeness and state of an agricultural product. This technique also finds its application in detecting synthetic colours in the edible products . Colour detection is the initial set in any image processing technique. In the future it helps the cashier to determine the quality of the agricultural product effectively and quickly by reducing the effort they put in the traditional method .

scholarly journals Smart Leaf Recognition System

2020 ◽  
Vol 9 (5) ◽  
pp. 289-291
Keyword(s):  
Image Processing ◽  
Early Stage ◽  
Recognition System ◽  
Processing Technique ◽  
Image Processing Technique ◽  
Raspberry Pi ◽  
Plant Leaves ◽  
Plant Leaf ◽  
Automatic Technique

One of major issue nowadays is the agricultural productivity which is something our Nation’s economy highly depends. Technology based advancements may lead to detection of diseases in plants which are quite natural. Care should be taken in this area before it causes serious effects on plants which mainly affect the product quality, quantity or productivity. Early stage detection of diseases in plants through some automatic technique is beneficial as it reduces a huge work of monitoring in large acres of crops. When they appear on plant leaves, earlier detection helps us to increase the yield and productivity. This paper presents an algorithm for image processing technique which is used for automatic detection and classification of plant leaf diseases with the help of raspberry pi and sensors. This survey is about different diseases and its classification, techniques which are used for plant leaf disease detection and also its respective fertilizer sprayed on the leaves.

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