The document discusses a project from Paavai Engineering College that aims to identify fruit quality through image processing using MATLAB. It outlines a hierarchical grading method to differentiate good and bad fruits by extracting features from images, employing techniques like thresholding and clustering. The project addresses health risks from bad quality fruits and highlights various applications of image processing in agricultural operations.

1. PAAVAI ENGINEERING COLLEGE-(AUTONOMOUS)
DEPARTMENT OF AGRICULTURE ENGINEERING
IDENTIFICATION OF FRUIT QUALITY IN IMAGE PROCESSING
BY MATLAB
TEAM MEMBERS
GUIDED BY
Mrs.M.BHUVANESWARI, M.E.,
ASSISTANT PROFFESOR,
DEPARTMENT OF AGRICUTURE ENGINEERING,
PAAVAI ENGINEERING COLLEGE.

2. ABSTRACT
The ability to identify the fruits based on quality in the food industry which is the most important
technology in the realization of automatic fruit sorting machine in order to reduce the work of human
and also time consuming. This work presents a hierarchical grading method applied to the Fruits. In this
work the identification of good and bad Fruits is focused on the methods using MATLAB. First we
extract certain features from t he input fruit image, later using different method like thresholding,
segmentation, k-means clustering and thus we get related databases. Comparing several trained
databases, we get a specific range for the good and bad fruits. From the proposed range we can identify
the good and bad fruits. Thus this paper analysis the good and bad fruits with a very high accuracy
successfully using image processing.
Keywords: Image Acquisition, Pre-processing, Segmentation, feature extraction,
feature training, feature matching.

3. OBJECTIVES
 To Identify the best quality of the fruit.
 To Detect the bad quality fruit
 To analyses the fruit properties like color, shape, texture.

4. PROBLEM IDENTIFICATION
 Bad quality fruits which are available in the market causs various
diseases to human health.
 Bad quality fruit which are rotten cause environmental disorder.

5. LITERATURE REVIEW

6. .
Fruit Quality Inspection using Image Processing
(Dr. A. N. Jayanthi , C. NARESHKUMAR , S. RAJESH , S. SATHISH KUMAR , K. VAZHA
GURUNATHAN 2019)
The image processing circumvents the problem of processing or quantifying the
photographic data mathematically. Several applications of image processing technology
have been developed for the agricultural operations. These applications involve
implementation of camera-based hardware systems or color scanners for inputting the
images. Fruit classification and fruit disease identification can be seen as an instance of
image categorization.

7. Image processing and machine learning for automated fruit grading system
(Rashmi Pandey,Sapan Naik 2016)
ANN was used to classify empire and golden delicious apples based on surface
characteristics of the apple images Textural and histogram features are extracted from the
images at selected wavelengths. Then, images of apples with surface characteristics were
used in classification applications with two cases two class classification and five class
classification. Effectiveness of method depend on the correlation between measured
feature parameter and quality factor.

8. Image Segmentation using K mean algorithm and Graph based algorithm
(Pengfei Shan EURASIP Journal on Image and Video Processing volume 2018)
The algorithm firstly uses k-means algorithm to split the original image into
regions based on Euclidean color distance in l*a*b* to produce an over segmentation
result. The objective of this work is to develop a general algorithm to effectively
segment objects in images to facilitate fruit defect detection. The dimension of feature
vectors depends on the numbers of color channel used graylevel based k-means for
segmenting images. L*a*b* or CIELab color space is used for k-means clustering.

9. Fruit Classification System using Computer Vision
(Raja Sekar L, Ambika N, Divya V and Kowsalya T 2018)
Color, textural and morphological feature are the most commonly used to identify
the disease, maturity and class of the fruits. The computer vision technique include
clustering and color based segmentation, artificial neural network and different
classifiers based classification of disease. Using digital method, the disease detection
can be accurately, time efficient and result in saves time. Different image processing
techniques have been developed with help of MATLAB for accurate fruit disease
identification.

10. Machine Vision Based Autonomous Fruit Inspection and Sorting
(Kedar Patil1, Shriniwas Kadam, Suraj Kale, Yogesh Rachetti, Kiran Jagtap, Dr. K.H.
Inamdar 2016 )
Machine vision technology or image processing is used for inspection and grade
wise sorting of fruits. MATLAB algorithms like conversion to binary image, area
calculation and average pixel value is used. Arduino-Uno microcontroller is used for
sorting. HSV color space is used to carry out fruit segmentation. It captures the image of
the fruit and calculate percentage value of the color in order to classify the grade of the
fruit.

11. Potential area of Project
• It mainly analysis the quality of the fruit we have taken for
analysis.
• In market various quality of fruits available so we have to find
out the good quality fruit.

12. Benefit of Project
• To detect the best quality of the fruit among the various types
sale in market.
• The proposed model is capable of detecting a quality of one
fruit at a time and this can be scaled up to detecting of multiple
fruits of different kinds at a same time.

13. Technology used in project
• For detection of quality image processing used
• Matlab Software has been used.

14. Problem identification
• Detection of low quality of fruit we have taken.

15. Methodology
Fruit Input image Pre-processing SEGMENTATION
FEATURE
MATCHING
FEATURE
EXTRACTION
Collection
of Material

16. GOOD QUALITY

17. BAD QUALITY

18. THANK YOU