Fruit Quality Analysis Using Clustering Method

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Description

                  Taking healthy fruits and vegetables is vital as they are the source of energy for all living beings. There is an increasing demand for quality in all the consumed food items. Nowadays, starting from consumers, retailers to food manufacturing companies are inspecting food visually for its quality. This manual process incurs more time and it is a laborious and tiring task. So, there is a demand for an automated process that quickly examines, detects the defects, and sorts them according to quality. There are many factors such as temperature, humidity, etc., that affect the quality of fruits. In this work, we have put forward a reliable mechanism for detecting the defects in fruits. The principal goal of this work is to detect and segregate low and best-quality fruits. It is achieved using the combination of hardware and image processing techniques and machine learning algorithms. The segmentation, feature extraction, and classification are done using MATLAB. Our proposed system exhibits better performance than the existing system.


OVERVIEW AND SCOPE

The main objective is to identify the fruit and it will check whether the fruit is ripening or not using a deep learning technique.

SCOPE OF PROJECT

The main contributions of this project therefore are

  • Data Analysis
  • Dataset Pre-processing
  • Training the Model
  • Testing of Dataset

DOMAIN OVERVIEW

PREPROCESSING

Digital Image Processing. Does digital image processing deal with? the manipulation of digital images through a digital computer. It is a subfield of signals and systems but focuses particularly on images. DIP focuses on developing a computer system that is able to perform?processing?on an? image. The input of that system is a digital?image?and the system process that?image?using an efficient algorithm

?It allows a much wider range of algorithms to be applied to the input data and can avoid problems such as the build-up of noise and distortion during processing.

  1. Importing the image via image acquisition tools;
  2. Analyzing and manipulating the image;
  3. Output in which result can be altered image

Image Pre-processing? is a common name for operations with?images?at the lowest level of abstraction.Its input and output are intensity? images.? The aim of?pre-processing?is an improvement of the? image? or data that suppresses unwanted distortions or enhances some images? features important for further processing.


DIGITAL IMAGE PROCESSING

CLASSIFICATION OF IMAGES

There are 3 kinds of pix applied in Digital Image Processing. They are

  1. Binary Image
  2. Gray Scale Image
  3. Colour Image

Deep? neural networks? are now the state-of-the-art machine learning models across a variety of areas, from image analysis to? natural language processing, and widely deployed in academia and industry.?

These developments have a huge potential for medical imaging technology, medical data analysis, medical diagnostics, and healthcare in general, slowly being realized. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. Long before deep learning was used, traditional machine learning methods were mainly used. Such as Decision Trees, SVM, Naive Bayes Classifier, and Logistic Regression.


BLOCK DIAGRAM:

Fruit Quality Analysis Using Clustering Method sd2
Fruit Quality Analysis Using Clustering Method sd2

 


CONCLUSION AND FUTURE SCOPE

In this, we have proposed? fruit of quality and check whether the fruit is ripening or not then it will be divided into two morphological segmentation one is ripening and another one is not ripening using deep learning technique. And GLCM feature extraction is used. fruit image is captured From the captured image, features have been extracted based on the energy, contrast, correlation, and homogeneity parameters Based on these extracted features they are classified using Multiclass svm. The accuracy obtained is 85.64%. The analysis of results determines the fruits for whether they are edible or inedible. The time taken by the segmentation technique is 0.68 seconds. Further, the work can be extended to real-time inspection of 1) multiple fruits 2) multiple fruits with more variety of diseases 3) multiple vegetables 4) a combination of fruits and vegetables can also be considered.


FUTURE ENHANCEMENT

                   In the future, we increased the performance of this process and able to get more accuracy.

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