jSS Polytchnic for the differently ablrd20.pptx

jSS Polytchnic for the differently ablrd20.pptx

1. jSS Polytchnic for the differently ablrd TOPIC:- Prediction of Banana ripening using Machine learning Department of Electronics and Communication Under the guidance of Smt. Smitha M  Lecturer of E&C name of students 1.DADI SASIKUMAR 3.THEJU G U 2.DARSHAN MURTHY 4.TIBIN

2. CONTENTS: ABSTRACT INTRODUCTION PREPOSESSING K-MEANS SEGMENTATION COLOUR HISTOGRAMS OBJECTIVE CLASSIFICATION SYSTEM ARCHITECTURE FLOW DIAGRAM SOFTWARE SPECIFICATION INTRODUCTION TO IMAGE PROCESSING IMAGE PROCESSING FUNDAMENTALS Results CONCLUSION

3. ABSTRACT: A Computer Vision System Was Implemented To Identify The Ripening Stages Of Bananas Based On Color, Development Of Brown Spots, And Image Texture Information. Computer Vision Shows Promise For Online Prediction Of Ripening Stages Of Bananas . Keywords: Computer Vision, Ripening Of Bananas, Color, Appearance, Classification

4. INTRODUCTION: Banana is a type of fruit often found in Indonesia Image processing is a technique that can be used to process images by converting them into the desired digital image data to obtain specific information P roven image processing technology can improve accuracy in the sorting process of fruit ripeness

5. PREPROCESSING: In preprocessing unnecessary noises in the image were eliminated. An unnecessary noise refers to the unwanted pixels in the frames. Pre-processing methods use a small neighbourhood of a pixel in an input image to get a new brightness value in output image Such pre-processing operations are also called filtration. Filters are used in the preprocessing techniques. infilter

6. K-MEANS SEGMENTATION K-means clustering is a method of vector quantization originally from signal processing that is popular for cluster analysis in image processing

7. COLOR HISTOGRAMS : A color histogram is a representation of the distribution of colors in an image. For digital images A color histogram represents the number of pixels that have colors The color histogram can be built for any kind of color space, although the term is more often used for three-dimensional spaces like RGB or HSV. For monochromatic images,

8. OBJECTIVES : T o identify and segment the banana grading in the given image. To enhance the object ,image enhancement is done using imadjust with thresholding. Features like statistical and textural features were extracted using GLCM ,colour movements, skewness, ketosis using

9. CLASSIFICATION: Classification is done using KNN classifier. In machine learning, support vector machines are supervised learning models with associated learning algorithms that analyze data and recognize patterns, used for classification and regression analysis. Given a set of training examples, each marked as belonging to one of two categories, an KNN training algorithm builds a model that assigns new examples into one category or the other.

10. SYSTEM ARCHITECTURE:

11. FLOW DIAGRAM:

12. SOFTWARE SPECIFICATION: HARDWARE REQUIREMENTS: Core mi3– 2.7 GHz 2GB DDR RAM 250Gb Hard Disk   SOFTWARE REQUIREMENT: Operating System : Windows 7 Tool : Matlab Version : 2018

13. SOFTWARE DESCRIPTION: MATLAB ® is a high-level technical computing language and interactive environment for algorithm development, data visualization, data analysis, and numerical computation. Using MATLAB, you can solve technical computing problems faster than with traditional programming languages, such as C, C++, and Fortran. Matlab is a data analysis and visualization tool which has been designed with powerful support for matrices and matrix operations.

14. INTRODUCTION TO IMAGE PROCESSING: What is an image? An image is an array, or a matrix, of square pixels (picture elements) arranged in columns and rows. An image — an array or a matrix of pixels arranged in columns and rows.

15. IMAGE PROCESSING FUNDAMENTALS : Pixel: In order for any digital computer processing to be carried out on an image, it must first be stored within the computer in a suitable form that can be manipulated by a computer program. The most practical way of doing this is to divide the image up into a collection of discrete (and usually small) cells, which are known as pixels . Most commonly, the image is divided up into a rectangular grid of pixels, so that each pixel is itself a small rectangle.

16. Results: Classification of banana grading images with accuracy

17. CONCLUSION: Sample images of apples and bananas were alone taken for experimentation, in future few more fruits or vegetables can be taken as samples for experimentation. Images having white background alone can only be considered for testing this algorithm using KNN classifier. Sample images should be acquired at 360 degrees in order to obtain 100% accuracy in real time classification of any fruit or vegetable in the agriculture industry. Thus a machine vision system for segregating/ classifying apple fruit and banana fruit was developed and tested for 96% accuracy and the same was obtained.

18. Thank you