Automated fruit grading system

In India, agriculture plays a major role in the economic development. Agriculture must be able to produce fruit of better quality and grow at a faster rate. With the use of different image processing techniques, improvement in agriculture field for quality identification, sorting the fruits with different quality, irrigation becomes feasible. Major parameters considered are reduction in the time required and cost efficient, using Image processing. In this proposal, we have provided a technique to address the challenge of fruit grading using image processing techniques for smart farming.

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Automated Grading of Tomatoes Using Artificial Intelligence

AI and Big Data’s Potential for Disruptive Innovation - Advances in Computational Intelligence and Robotics ◽

10.4018/978-1-5225-9687-5.ch008 ◽

2020 ◽

pp. 216-239

Author(s):

Tawanda Mushiri ◽

Liberty Tende

Keyword(s):

Neural Network ◽

Artificial Intelligence ◽

Image Processing ◽

Tomato Fruit ◽

Grading System ◽

Past Century ◽

Manual Sorting ◽

Fruit Processing ◽

Automated Grading ◽

Fruit Grading

The rate of production of horticultural produce had been seen increasing from the past century owing to the increase of population. Manual sorting and grading of tomatoes had become a challenge in market places and fruit processing firms since the demand of the fruit had increased. Considering grading of tomatoes, color is of major importance when it comes to the maturity of the tomatoes. Hence, there is a need to accurately classify them according to color. This process is very complicated, tiresome, and laborious when it is done manually by a human being. Apart from being labor-demanding, human sorting, and grading results in inaccuracy in classifying of tomatoes which is a loss to both the farmer and customer. This chapter had been prepared focusing on the automatic and effective tomato fruit grading system using artificial intelligence particularly using artificial neural network in Matlab. The system makes use of the image processing toolbox and the ANN toolbox to process and classify the tomatoes images according to color and size.

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Machine vision based automatic fruit grading system using fuzzy algorithm

Proceedings of The 2014 International Conference on Control, Instrumentation, Energy and Communication (CIEC) ◽

10.1109/ciec.2014.6959043 ◽

2014 ◽

Cited By ~ 8

Author(s):

Chandra Sekhar Nandi ◽

Bipan Tudu ◽

Chiranjib Koley

Keyword(s):

Machine Vision ◽

Grading System ◽

Fuzzy Algorithm ◽

Fruit Grading

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Quality Detection and Specie Identification of Apples Based on Multi-Spectral Imaging

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.301-303.158 ◽

2011 ◽

Vol 301-303 ◽

pp. 158-164 ◽

Cited By ~ 1

Author(s):

Chun Xiao Tang ◽

En Bang Li ◽

Chuan Zhen Zhao ◽

Chao Li

Keyword(s):

Spectral Imaging ◽

Identification System ◽

Grading System ◽

Infrared Images ◽

Accuracy Rate ◽

Quality Detection ◽

Fruit Grading ◽

Fuji Apple ◽

Grading Standards ◽

Apple Quality

This paper introduced an apple quality detection and specie identification system based on multi-spectral imaging. Under an international mixed light illumining, system can capture red, green and infrared images of apples at the same time. A software programmed based on Matlab 6.5.1 is used for image processing to complete the detection of quality and specie. According to processing results, the subtotals and classification are made into grading standards. These can be quickly and easily applied to the automation of agriculture fruit grading system. In the experiment, some most common apples including Fuji apple, Red delicious apples, Green apples, Gina Apple's were detected for quality and variety . Accuracy rate can be more than 90%.

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