conference e04 1

8/13/2019 Conference e04 1

1/22

Digital Image Processing pproach for Fruitand Flower Leaf Identification and

ecognition

Submitted by,Rahul H .N.

4JN11IS421

Guided by,

M r. Pavan Kumar M .PLecturer, Dept. of I nformation Science & Eng.

JNNCE, Shimoga


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CONTENTS

Introduction

System design

Techniques

Extraction features

Recognition & its method

Conclusion


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INTRODUCTION

Digital Image processing technique

Pattern recognition

Categorize of image processing

Applications


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INTRODUCTION.

It is difficult to analyze the plant

based on 2D & 3D images .

We can t define the the plant based

on the leaf color or based on the

fruit all the time.


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Sys tem Design


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IMAGE PROCESSING ALGORITHM

Yes


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LEAF FEATURE EXTRACTION

Basic quantity descriptors Area (A) Perimeter (P)

Maximum length (L) Maximum width (W) Convex hull (H)

Dimensionless shape factors Compactness (C)

Roundness (R) Elongation (E) Roughness (G)

Conventional Morphological Features


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LEAF FEATURE EXTRACTIONFourier descriptors

Steps to extract Fourier descriptors

Find the major axis of seedling leaf

with Hotelling transform

Rotate seedling leaf to horizontal position

and select 256 points on the leaf boundary

Convert x-y coordinates of boundary points

to complex number

Use FFT algorithm to obtain

Fourier transform coefficient

Normalization of Fourier transform

coefficients to obtain Fourier descriptors


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Original Image Binary Image

N=256 N=128 N=64 N=32

N=16 N=8 N=4 N=2

Cabbage


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Original Image Binary Image

N=256 N=128 N=64 N=32

N=16 N=8 N=4 N=2

Lettuce


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LEAF FEATURE EXTRACTIONBezier descriptors

Steps to obtain Bezier descriptors

Image acquisition Image segmentation Boundary detection

Finding leaf tip and

leaf base

Fitting boundary with

Bezier curves

Normalization and

obtain bezier descriptors

A B C

D E F


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LEAF FEATURE EXTRACTIONBezier descriptors

Bezier descriptors Leaf tip angle Leaf base angle Left control line ratio Right control line ratio Normalized controlpoint coordinates


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RESULTS

Leaf features at different growth stages

Basic morphologic features

Bezier descriptors

Applications

Geometric Modeling of Seedling Leaves

Leaf Shape Comparisons and Plant

Identification


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APPLICATIONSGeometric Modeling of Seedling Leaves

Wire Frame Model Perspective View Mapping with Texture

Elliptical Model


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Top View

Side View

Real Image Graphics Simulation

APPLICATIONS3D Reconstruction of Seedling Structure

Graphic Simulation of Cabbage Seedling


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APPLICATIONS

Leaf Shape Comparisons and Plant Identification

Leaf

Feature

Extraction

Leaf Image

Morphological

Features

Fourier

Descriptors

Bezier

Features

PatternRecognition

Statistical Analysis

Neural Network

Cluster Analysis

Genetic Algorithm

Plant

Identification Applications


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CONCLUSIONS

The recogni tion of local f rui t trees through leaf

structures using image processing techniques.

Chain code method is a method that was used to

obtain the shape of an object.

I n addition, a linear feature recogni tion technique

for comparison was successful implemented to

achieve the objectives of the research.


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Digital image processing- fruit tree

category, provide its statistical analysis and

general information using an image of aleaf as a parameter.

CONCLUSIONS


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THANK YOU

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