Design and Development of California Papaya Murability Detection Based on Learning Vector Quantization Method Using LDR Sensor and Camera

Masjudin; Alimuddin Alimuddin; Oktavia Widia Ningrum; Romi Wiryadinata

doi:10.2991/978-94-6463-090-9_8

<Previous Article In Volume

Next Article In Volume>

Design and Development of California Papaya Murability Detection Based on Learning Vector Quantization Method Using LDR Sensor and Camera

Authors

Masjudin¹, Alimuddin Alimuddin¹, Oktavia Widia Ningrum¹, Romi Wiryadinata¹^{, *}

¹Electrical Engineering Department, Universitas Sultan Ageng Tirtayasa, Banten, Indonesia

^*Corresponding author. Email: wiryadinata@untirta.ac.id

Corresponding Author

Romi Wiryadinata

Available Online 24 December 2022.

DOI: 10.2991/978-94-6463-090-9_8 How to use a DOI?
Keywords: Papaya; TCS3200 series LDR; Camera; Learning Vector Quantization (LVQ)
Abstract: Sorting the ripeness of papaya fruit is generally done manually. Technological developments can simplify and speed up the work of farmers in sorting papaya fruit, such as using the TCS3200 series LDR sensor, which produces red, green, and blue color frequency values. This sensor can distinguish ripe papaya fruit from different skin colors. Papaya with perfect green skin color is included in raw papaya, papaya with balanced green and yellow skin color means that papaya is mature, and papaya with even yellow skin color is included in ripe papaya. This category is also included in the class in the classification of papaya fruit maturity using the LVQ method. The data is taken directly using the camera by classifying it using the parameters of mean, skewness, and kurtosis. The results of the highest papaya ripeness classification accuracy are in the 2nd experiment with a learning rate value of 0.2 with hidden layer ten (10) and epoch 100, which is 93.3%, and the test results of the whole tool have an average success percentage value of 69.41%.
Copyright: © 2023 The Author(s)
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Download article (PDF)

<Previous Article In Volume

Next Article In Volume>

Volume Title: Proceedings of the 2nd International Conference for Smart Agriculture, Food, and Environment (ICSAFE 2021)
Series: Advances in Biological Sciences Research
Publication Date: 24 December 2022
ISBN: 978-94-6463-090-9
ISSN: 2468-5747
DOI: 10.2991/978-94-6463-090-9_8 How to use a DOI?
Open Access: Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

ris enw bib

TY  - CONF
AU  - Masjudin
AU  - Alimuddin Alimuddin
AU  - Oktavia Widia Ningrum
AU  - Romi Wiryadinata
PY  - 2022
DA  - 2022/12/24
TI  - Design and Development of California Papaya Murability Detection Based on Learning Vector Quantization Method Using LDR Sensor and Camera
BT  - Proceedings of the 2nd International Conference for Smart Agriculture, Food, and Environment (ICSAFE 2021)
PB  - Atlantis Press
SP  - 63
EP  - 73
SN  - 2468-5747
UR  - https://doi.org/10.2991/978-94-6463-090-9_8
DO  - 10.2991/978-94-6463-090-9_8
ID  - 2022
ER  -

download .riscopy to clipboard