Automated estimation of grape ripeness

  • Kaveri Kangune
  • Dr. Vrushali Kulkarni
  • Prof. Pranali Kosamkar


India is worldwide well known for exporting
fruits, having immense importance in the world. Global
food security is necessary for not only durable
production of fruits but also for remarkable reduction in
pre and post- harvest waste. Harvesting fruits and
detecting ripeness of fruits by human is an expensive,
laborious and time consuming task. For this reason,
there is need for an automated ripeness estimation
system in the last decade. Fruit ripeness estimation is
major task that influence its quality and later its
marketing. Researchers have started targeting towards
for the study of ripeness estimation using methods in
image processing and machine learning to automatic
classification of ripeness of fruit accurately, quickly and
non-destructively. Traditional methods for fruit ripeness
estimation considered fruits such as orange, apple,
tomato, banana, papaya and etc. which is single fruit. By
taking into account increasing productivity of grapes
and bunch of berries in grapes need to focus on
estimation of ripeness of grapes fruit. We have reviewed
various studies in this domain and believe this is a
primary effort in summarizing the highlights of
researches done. This will give direction for fellow

Keywords: Ripeness estimation, image processing, Deep Learning, Convolution Neural Networks


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Bansal, ScienceDirect - Journal of King Saud
University “Computer and Information Sciences, 3
June 2018
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Image Processing and Computational Intelligence
Methods", Ashfaqur Rahman and Andrew Hellicar,
2014 IEEE
[3] "Detecting maturity of persimmon fruit based on
image processing technique", Vahid Mohammadi,
Kamran Kheiralipour and Mahdi Ghasemi-
Varnamkhasti, ELSEVIER - Scientia Horticulturae
[4] "Apple Ripeness Estimation using Artificial Neural
Network", Raja Hamza, Mohamed Chtourou,
International Conference on High Performance
Computing & Simulation, July 2018 IEEE
[5] "Using machine learning techniques for evaluating
tomato ripeness", ScienceDirect- Expert Systems with
Applications, Nashwa El-Bendary, Esraa El Hariri,
Aboul Ella Hassanien and Amr Badr
[6] "Prediction of banana quality indices from color
features using support vector regression",Alireza
Sanaeifar , Adel Bakhshipour and Miguel de la
Guardia, Elsevier - Talanta, 2016
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Sehgal,ScienceDirect - Applied Soft Computing
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Sekhar Nandi, Bipan Tudu, and Chiranjib Koley, IEEEtransactions on instrumentation and measurement, July
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Based on BP Feed Forward Neural Network", Shreya
Lal, Santi Kumari Behera, Prabira Kumar Sethy and
Amiya Kumar Rath, 2017 IEEE 3rd International
Conference on Sensing, Signal Processing and
Security (ICSSS)
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Mahmod Othman , Ab. Razak Mansor and Mohd
Nazari Abu Bakar, Springer - International Conference
on Computing, Mathematics and Statistics, November
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artificial neural network based on histogram
approach”, Hasnida Saadl, Ahmad Puad Ismaie,
Noriza Othmanl, Mohamad Huzaimy Jusohl, Nani
fadzlina Naiml , Nur Azam Ahmadi , 2009 IEEE
International Conference on Signal and Image
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Classification of Banana Ripeness”
M Senthilarasi, and S Mohamed Mansoor Roomi,
IEEE 2017
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for Tomato Ripeness”, Esraa Elhariri, Nashwa El-
Bendary, Mohamed Mostafa M. Fouad, Jan Plato,
Aboul Ella Hassanien and Ahmed M.M. Hussein,
[14] “Using machine learning techniques and different
color spaces for the classification of Cape gooseberry
fruits according to ripeness level” Carlos Cotrina,
Karen Bazan, Jimy Oblitas, Himer Avila-George and
Wilson Castro, ScienceDirect 14 Mar 2018
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analysis for banana ripeness estimation” Yuttana
Intaravanne, Sarun Sumriddetchkajorn and Jiti
Nukeaw, ScienceDirect 2012
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GLCM and HSV color space", Oktaviana Rena
Indriani, Edi Jaya Kusuma, Christy Atika Sari, Eko
Hari Rachmawanto and De Rosal Ignatius Moses
Setiadi, November 2017
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evaluation of strawberry with support vector
machine”, Chu zhang, Chentong Guo, Fei Liu,
Wenwen Kong, Yong He, Binggan Lou, January 2016
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Viticulture Technology”, K.P. Seng, L.M. Ang, Leigh
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and neural networks" Alex Zuñiga, MarcoMora,
MiguelOyarce and Claudio Fredes, ScienceDirect
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Peng Wan, Arash Toudeshki, Hequn Tan and Reza
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Agriculture, 12 january 2018
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development of colourgrade chart for Indian mangoes
(Mangifera indica L.) Using multivariate cluster
analysis” V.Eyarkai Nambi, K. Thangavel and
D.Manohar Jesudas, ScienceDirect- 2015
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Grapes during Harvest Time based on Visible
and Near-infrared (Vis-NIR) Spectroscopy” Gang
Lv,Haiqing Yang, Ning Xu and Abdul M. Mouazen,
IEEE 2012
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How to Cite
Kangune, K., Kulkarni, D. V., & Kosamkar, P. P. (2019). Automated estimation of grape ripeness. Asian Journal For Convergence In Technology (AJCT). Retrieved from