Alauddin Mazumder, ID number of the Department of Computer Science and Engineering, Daffodil International University has been accepted as satisfactory in partial fulfillment of the requirements for the degree of B.Sc. Department of Computer Science and Engineering Faculty of Natural Sciences and Information Technology Daffodil International University. Syed Akhter Hossain, Head, Department of CSE, Daffodil International University, Dhaka for his kind help to complete our project and also to other faculty member and the staff of CSE department of Daffodil International University.
We would like to thank our entire coursemate in Daffodil International University who participated in this discussion while completing the coursework. One of the biggest changes in shopping experience in supermarkets in the last five years is the addition of self-checkout. Daffodil International University 2 billion can be saved by the proposed classification algorithm which cannot be fooled and it will actually help people not to become thieves.
The second stage is the segmentation of the foreground representing a new object, from the background. In the case of fresh produce, discriminating features are mainly related to color, but shape and texture can also be determining factors. Performance will be scored based on accuracy (best shot), average correct tag range and best three shots.
The rank refers to the position of the correct label in the sorted list of predictions based on their probability.
Contributions 4
Objectives 4
Expected Outcome 4
In the context of the fresh produce industry, computer vision techniques are mainly used for fruit detection for harvesting purposes or for quality evaluation. A combination of a laser rangefinder model and a two color and shape model is used, mainly targeting oranges, apples and other citrus fruits. Quality evaluation based on visual appearance is discussed, which used the Hue, Saturation and Intensity color model to classify the quality of apples. examined how carrots and celeriac change over a period of 14 days. The aim was to create an alternative quality control in the food industry to the usual analysis of chemical compounds extracted from the products. developed automatic detection of skin defects in citrus fruits based mainly on their textural characteristics.
What all these studies have in common is that they examine certain properties of one class of fruit or vegetable. Fruit and vegetable recognition by fusing color and texture features has been proposed. Their design relies on background subtraction and classification with a multi-class support vector machine. Applied fusion of multiple color channels for multi-class fruit detection from highly complex back images. reasons. Texture features are based on the histogram of the sum and difference of adjacent pixels and are represented. With one exception, all other articles used images of fruits and vegetables on a white background.
They report that the main discriminating features between different classes of fruits and vegetables are based on color and texture.[4][5].
Self-Checkout 6
Problem Statement 9
Motivation 9
WORKING 10-13
- Testset 10
- Re-train Model 10
- Read the Image 10
- Upload Image 12
- Technical Issue 14
- Pseudocode 14
- RGB 15
- HSV 15
- Flow Chart Works 16
- Accuracy 17
Daffodil International University 14 Now the name of the vegetable will appear at the top of the vegetable. This technique of using matlab code can be used for various purposes which are given below. The RGB color model is an additive color model in which red, green, and blue light are added together in different ways to reproduce a wide range of colors.
The name of the model comes from the initials of the three additive primary colours, red, green and blue. HSL (hue, saturation, lightness) and HSV (hue, saturation, value) are alternative representations of the RGB color model, designed in the 1970s by computer graphics researchers to accommodate the way human vision perceives color-making properties. Our experimental results showed that this application shows accuracy with 96.55% of identifying vegetables. Figure 4.5 shows samples of learned images and tested images. A histogram is a graphical representation that shows a visual impression of the distribution of data.
For any given image we can calculate H, S, V histogram and recognize the image for that particular vegetable.
METHODOLOGY 19-23
Calculate Area,Perimeter and Roundness Value 20
The value of fruit roundness (metric) can be calculated after extracting the area and perimeter of the fruit using the equation below. The results apply to the entire test fruit. Set the table with the fruit name and the calculated attribute value, such as average RGB color values, shape roundness, values, area and perimeter values.
Calculate Entropy Value 22
This function will classify the properties of input fruit sample with properties of all other training fruit examples and Find out the 'K' example and then classify the unknown fruit image to the class or group where the major of the 'K' nearest neighbors form.
DISCUSSION 24-25
Limitation 24
Conclusion 25
Although the position, number of items, and pixel presence varied across images, the conditions of creating the dataset were relatively controlled in terms of light and background. Changing these conditions in the real environment can degrade the performance of the classifier (or of a stage of the processing pipeline). However, all images in the second data set contain a color reference sticker if additional color correction is required.
1] Woo Chaw Sang and Seyed Hadi Mirisaee, "A New Method for Fruit Recognition System", MNCC Transaction on ICT, Vol1- No. 1, June 2009. 4] Subero and José, "Advance machine vision application for automatic Inspection and quality evolution of fruit and vegetables", In Springer, 2012. J On tree fruit recognition using Properties and color data", International conference on Intelligent Robots and System, pp.
Joshi, “Fruit Detection with improved.Multiple.Features based algorithm”, International Journal of Computer application vol.13-No 2, January 2011. 8] Nashir A.F.A., Rahman M.N.A and Mamat A.R, A study on image processing in agricultural applications under high computing Environment”, International Journal of Computer Science and Telecommunication, vol.3 No.8, pp. 9] Shiv Ram Dubey and Anand Singh Jalal, “Species and Variety Detection of Fruits and Vegetables from Images”, International Journal Applied Pattern Recognition , Vol.1 No.1, 2013.
