Abstract

The proposed study intends to provide an efficient algorithm for the instruction of an automatic robot arm to choose the ripe fruits on the tree. Steps involved in this study are recognizing and locating the ripe fruits from the leaf and branch portions by using an efficient machine vision algorithm. Initially, discrete wavelet transform is used for better preserving of edges and fine details in the given input image. Then RGB, HSV, L*a*b* and YIQ color spaces were studied to segment the ripe fruits from the surrounding objects. Finally, the results showed that 'I' component of the YIQ color space has the best criterion for recognizing the fruit from the foliage. The fruit segmentation based on machine vision has an occlusion problem. In this proposed method these problems are also examined.

Highlights

  • We focused on the development of an algorithm in order to recognize and localize the ripe fruits that have a smooth surface, such as apples, oranges and pomegranates using only color analysis

  • A vision algorithm was designed to recognize the ripe fruits from the other objects of image as well as to determine their location

  • Recognition algorithm developed in this study used color difference (‘I’ channel) as criteria for discriminating the ripe fruits from the leaves and boughs

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Summary

Introduction

Agribusiness has given careful consideration since old. Our precursor has created a lot of better approaches to make the marvelous rural human progress. With the advancement of horticulture engineering, farming modernization raises new prerequisites for agribusiness improvement. Again, previous customary recognition strategies have not fulfilled the prerequisite of up-to-date horticulture, which promotes modern detection technology applied. Around them machine vision technology can give proficient and dependable items recognition approach. Fruits picking by humans is a time-consuming, tiresome and expensive process. The automation of fruit harvesting has accomplished great popularity in the last decade. Image processing and use of automation techniques in agriculture have become a major issue in recent years

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