Intelligent System to Evaluate the Quality of Orange, Lemon, Sweet Lime and Tomato Using Back-Propagation Neural-Network (BPNN) and Probabilistic Neural Network (PNN)

V. G. Narendra, K. Govardhan Hegde

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The quality assessment and sorting millions of fruits as well as vegetables by manual is usually slower. But also costly and cannot give an accurate result. In this research, to increase the quality of food above products were developed by using a vision-based quality inspection and sorting system. The quality assessment and sorting process analyzes taken image for its quality (good). It discards the defected one (bad). The image can be of vegetables or fruits. Four different systems for different food products (Orange, Lemon, Sweet Lime, and Tomato) have been developed. We have used a dataset of one thousand two hundred images which can be used to train as well as test the image systems. All images of 300 in the count. The obtained overall accuracy ranges between 85.0% to 95.00% for Orange, Lemon, Sweet Lime, and Tomato by using soft-computing techniques such as Backpropagation neural network and Probabilistic neural network.

Original languageEnglish
Title of host publicationAdvanced Informatics for Computing Research - 3rd International Conference, ICAICR 2019, Revised Selected Papers
EditorsAshish Kumar Luhach, Dharm Singh Jat, Kamarul Bin Ghazali Hawari, Xiao-Zhi Gao, Pawan Lingras
PublisherSpringer Paris
Pages369-382
Number of pages14
ISBN (Print)9789811501074
DOIs
Publication statusPublished - 01-01-2019
Event3rd International Conference on Advanced Informatics for Computing Research, ICAICR 2019 - Shimla, India
Duration: 15-06-201916-06-2019

Publication series

NameCommunications in Computer and Information Science
Volume1075
ISSN (Print)1865-0929
ISSN (Electronic)1865-0937

Conference

Conference3rd International Conference on Advanced Informatics for Computing Research, ICAICR 2019
CountryIndia
CityShimla
Period15-06-1916-06-19

Fingerprint

Probabilistic Neural Network
Tomato
Back-propagation Neural Network
Intelligent systems
Intelligent Systems
Backpropagation
Sorting
Lime
Vegetables
Fruits
Neural networks
Evaluate
Quality Assessment
Fruit
Soft computing
Inspection
Soft Computing
Count
Range of data

All Science Journal Classification (ASJC) codes

  • Computer Science(all)
  • Mathematics(all)

Cite this

Narendra, V. G., & Govardhan Hegde, K. (2019). Intelligent System to Evaluate the Quality of Orange, Lemon, Sweet Lime and Tomato Using Back-Propagation Neural-Network (BPNN) and Probabilistic Neural Network (PNN). In A. K. Luhach, D. S. Jat, K. B. G. Hawari, X-Z. Gao, & P. Lingras (Eds.), Advanced Informatics for Computing Research - 3rd International Conference, ICAICR 2019, Revised Selected Papers (pp. 369-382). (Communications in Computer and Information Science; Vol. 1075). Springer Paris. https://doi.org/10.1007/978-981-15-0108-1_34
Narendra, V. G. ; Govardhan Hegde, K. / Intelligent System to Evaluate the Quality of Orange, Lemon, Sweet Lime and Tomato Using Back-Propagation Neural-Network (BPNN) and Probabilistic Neural Network (PNN). Advanced Informatics for Computing Research - 3rd International Conference, ICAICR 2019, Revised Selected Papers. editor / Ashish Kumar Luhach ; Dharm Singh Jat ; Kamarul Bin Ghazali Hawari ; Xiao-Zhi Gao ; Pawan Lingras. Springer Paris, 2019. pp. 369-382 (Communications in Computer and Information Science).
@inproceedings{b02627012d3148ceaf513f461cc9021e,
title = "Intelligent System to Evaluate the Quality of Orange, Lemon, Sweet Lime and Tomato Using Back-Propagation Neural-Network (BPNN) and Probabilistic Neural Network (PNN)",
abstract = "The quality assessment and sorting millions of fruits as well as vegetables by manual is usually slower. But also costly and cannot give an accurate result. In this research, to increase the quality of food above products were developed by using a vision-based quality inspection and sorting system. The quality assessment and sorting process analyzes taken image for its quality (good). It discards the defected one (bad). The image can be of vegetables or fruits. Four different systems for different food products (Orange, Lemon, Sweet Lime, and Tomato) have been developed. We have used a dataset of one thousand two hundred images which can be used to train as well as test the image systems. All images of 300 in the count. The obtained overall accuracy ranges between 85.0{\%} to 95.00{\%} for Orange, Lemon, Sweet Lime, and Tomato by using soft-computing techniques such as Backpropagation neural network and Probabilistic neural network.",
author = "Narendra, {V. G.} and {Govardhan Hegde}, K.",
year = "2019",
month = "1",
day = "1",
doi = "10.1007/978-981-15-0108-1_34",
language = "English",
isbn = "9789811501074",
series = "Communications in Computer and Information Science",
publisher = "Springer Paris",
pages = "369--382",
editor = "Luhach, {Ashish Kumar} and Jat, {Dharm Singh} and Hawari, {Kamarul Bin Ghazali} and Xiao-Zhi Gao and Pawan Lingras",
booktitle = "Advanced Informatics for Computing Research - 3rd International Conference, ICAICR 2019, Revised Selected Papers",
address = "France",

}

Narendra, VG & Govardhan Hegde, K 2019, Intelligent System to Evaluate the Quality of Orange, Lemon, Sweet Lime and Tomato Using Back-Propagation Neural-Network (BPNN) and Probabilistic Neural Network (PNN). in AK Luhach, DS Jat, KBG Hawari, X-Z Gao & P Lingras (eds), Advanced Informatics for Computing Research - 3rd International Conference, ICAICR 2019, Revised Selected Papers. Communications in Computer and Information Science, vol. 1075, Springer Paris, pp. 369-382, 3rd International Conference on Advanced Informatics for Computing Research, ICAICR 2019, Shimla, India, 15-06-19. https://doi.org/10.1007/978-981-15-0108-1_34

Intelligent System to Evaluate the Quality of Orange, Lemon, Sweet Lime and Tomato Using Back-Propagation Neural-Network (BPNN) and Probabilistic Neural Network (PNN). / Narendra, V. G.; Govardhan Hegde, K.

Advanced Informatics for Computing Research - 3rd International Conference, ICAICR 2019, Revised Selected Papers. ed. / Ashish Kumar Luhach; Dharm Singh Jat; Kamarul Bin Ghazali Hawari; Xiao-Zhi Gao; Pawan Lingras. Springer Paris, 2019. p. 369-382 (Communications in Computer and Information Science; Vol. 1075).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

TY - GEN

T1 - Intelligent System to Evaluate the Quality of Orange, Lemon, Sweet Lime and Tomato Using Back-Propagation Neural-Network (BPNN) and Probabilistic Neural Network (PNN)

AU - Narendra, V. G.

AU - Govardhan Hegde, K.

PY - 2019/1/1

Y1 - 2019/1/1

N2 - The quality assessment and sorting millions of fruits as well as vegetables by manual is usually slower. But also costly and cannot give an accurate result. In this research, to increase the quality of food above products were developed by using a vision-based quality inspection and sorting system. The quality assessment and sorting process analyzes taken image for its quality (good). It discards the defected one (bad). The image can be of vegetables or fruits. Four different systems for different food products (Orange, Lemon, Sweet Lime, and Tomato) have been developed. We have used a dataset of one thousand two hundred images which can be used to train as well as test the image systems. All images of 300 in the count. The obtained overall accuracy ranges between 85.0% to 95.00% for Orange, Lemon, Sweet Lime, and Tomato by using soft-computing techniques such as Backpropagation neural network and Probabilistic neural network.

AB - The quality assessment and sorting millions of fruits as well as vegetables by manual is usually slower. But also costly and cannot give an accurate result. In this research, to increase the quality of food above products were developed by using a vision-based quality inspection and sorting system. The quality assessment and sorting process analyzes taken image for its quality (good). It discards the defected one (bad). The image can be of vegetables or fruits. Four different systems for different food products (Orange, Lemon, Sweet Lime, and Tomato) have been developed. We have used a dataset of one thousand two hundred images which can be used to train as well as test the image systems. All images of 300 in the count. The obtained overall accuracy ranges between 85.0% to 95.00% for Orange, Lemon, Sweet Lime, and Tomato by using soft-computing techniques such as Backpropagation neural network and Probabilistic neural network.

UR - http://www.scopus.com/inward/record.url?scp=85075249966&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=85075249966&partnerID=8YFLogxK

U2 - 10.1007/978-981-15-0108-1_34

DO - 10.1007/978-981-15-0108-1_34

M3 - Conference contribution

AN - SCOPUS:85075249966

SN - 9789811501074

T3 - Communications in Computer and Information Science

SP - 369

EP - 382

BT - Advanced Informatics for Computing Research - 3rd International Conference, ICAICR 2019, Revised Selected Papers

A2 - Luhach, Ashish Kumar

A2 - Jat, Dharm Singh

A2 - Hawari, Kamarul Bin Ghazali

A2 - Gao, Xiao-Zhi

A2 - Lingras, Pawan

PB - Springer Paris

ER -

Narendra VG, Govardhan Hegde K. Intelligent System to Evaluate the Quality of Orange, Lemon, Sweet Lime and Tomato Using Back-Propagation Neural-Network (BPNN) and Probabilistic Neural Network (PNN). In Luhach AK, Jat DS, Hawari KBG, Gao X-Z, Lingras P, editors, Advanced Informatics for Computing Research - 3rd International Conference, ICAICR 2019, Revised Selected Papers. Springer Paris. 2019. p. 369-382. (Communications in Computer and Information Science). https://doi.org/10.1007/978-981-15-0108-1_34