Pixel and color based fruit detection

Most of the people are being confused during identify the fruit having the feature like shape, color. In this project, we will develop an application and a complete framework will be proposed for the identification of fruit image and the study will show that this system has useful to identifying the

Project Title

Pixel and color based fruit detection

Project Area of Specialization

Artificial Intelligence

Project Summary

Most of the people are being confused during identify the fruit having the feature like shape, color. In this project, we will develop an application and a complete framework will be proposed for the identification of fruit image and the study will show that this system has useful to identifying the object especially fruits and vegetable.

The main advantage of PCBFD is to identify the object and give a result with great accuracy. This application has some key points which are as under:

• Feature extraction technique would be reviewed and select the best technique for detecting the images of fruits.
• To provide the more appropriate image processing technique for receiving accuracy during the recognition of fruit
• To achieve better accuracy by using the CNN (convolutional neural network technique).

Project Objectives

Pixel and Color based Fruit Detection (PCFD) using Advance Machine Learning (AML), it is a Desktop Application. Object detection is one of the major and complex problems of computer vision. We aim to use AML algorithms to detect fruits from images based on shape, size, pixels and extract the features from the input image and able to detect the fruits. In this regard, each object may contain a low level or high-level features. Each object from input dataset may consist of three channels (R, G, B) that are separated by using AML algorithms that may provide help to easily detect the fruits. We aim to use the supervised learning methods of AML. The project aims to incorporate state-of-the-art technique for fruit detection with the goal of achieving high accuracy

Project Implementation Method

For develop and implement PCBFD, we will be using AML algorithms in which we use CNN (Convolutional Neural Network), R-CNN (Re-Convolutional Neural Network), & HOG (Histogram of Oriented Gradient) these are the famous algorithms of Machine Learning. We will aim to utilize these algorithms on Python Programming Language 

Benefits of the Project

Exterior properties of fruit like color and shape, are very important attributes of fruit for identification. Due to the improvement in computer vision and hardware, and software available in low-cost manual work of fruit identification has now been replaced with an automated machine vision system. This application saves time and avoids from manual checking of fruit stock. This system has the ability to produce an accurate, rapid, and efficient result as compared to manual work. This project will be extended in future by including these features i.e. fruit counting, check the quality by its appearance.

Technical Details of Final Deliverable

This App, PCBFD aims to detect and recognize fruits from images. The aim is to build an accurate, fast and reliable fruit detection system, which is a vital element of an autonomous agricultural robotic platform, it is a key element for fruit yield estimation and automated harvesting. This is a desktop-based application, every individual can easily use this app. This app can accurately, efficiently and quickly detect fruits with respect to its image quality.

This application will enable a person to work in a smart way and saves time and avoid manual working such as manual checking and identification of fruits.

Final Deliverable of the Project

Software System

Type of Industry

IT

Technologies

Artificial Intelligence(AI)

Sustainable Development Goals

Industry, Innovation and Infrastructure

Required Resources

If you need this project, please contact me on contact@adikhanofficial.com