Chili fruits maturity estimation using various convolutional neural network architecture
Agricultural robots recently become popular by helping the farmer to conduct their daily chores. A slow process of picking and grading will leads to an inaccurate result thus increasing the production cost. This study represents an innovative and economical alternative for farmers who require to und...
| Main Authors: | , , , , , |
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| Format: | Article |
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Institute of Advanced Engineering and Science
2024
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| _version_ | 1811137740259983360 |
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| author | Mohd Hussin, Najihah Zainudin, Muhammad Noorazlan Shah Mohd Saad, Wira Hidayat Kamarudin, Muhammad Raihaan Muhammad, Sufri Razak, Muhd Shah Jehan Abd |
| author_facet | Mohd Hussin, Najihah Zainudin, Muhammad Noorazlan Shah Mohd Saad, Wira Hidayat Kamarudin, Muhammad Raihaan Muhammad, Sufri Razak, Muhd Shah Jehan Abd |
| author_sort | Mohd Hussin, Najihah |
| collection | UPM |
| description | Agricultural robots recently become popular by helping the farmer to conduct their daily chores. A slow process of picking and grading will leads to an inaccurate result thus increasing the production cost. This study represents an innovative and economical alternative for farmers who require to undergone the process of estimating their maturity categories. A total of 1,200 chili images with 256×256 pixel are used, where 840 is used for training and the remaining 360 being served for testing. The maturity is determined by measuring the length of chili structure between the calyx and apex. Various convolutional neural network (CNN) architectures are applied to learn and recognize the chili fruits into three maturity categories; immature, moderately mature, and mature. ADAM and stochastic gradient descent with momentum (SGDM) optimizers with multiple CNN architectures is capable in recognising and classifying chilli fruits with an accuracy of above 85. |
| first_indexed | 2024-09-25T03:39:06Z |
| format | Article |
| id | upm.eprints-106227 |
| institution | Universiti Putra Malaysia |
| last_indexed | 2024-09-25T03:39:06Z |
| publishDate | 2024 |
| publisher | Institute of Advanced Engineering and Science |
| record_format | dspace |
| spelling | upm.eprints-1062272024-05-08T13:33:41Z http://psasir.upm.edu.my/id/eprint/106227/ Chili fruits maturity estimation using various convolutional neural network architecture Mohd Hussin, Najihah Zainudin, Muhammad Noorazlan Shah Mohd Saad, Wira Hidayat Kamarudin, Muhammad Raihaan Muhammad, Sufri Razak, Muhd Shah Jehan Abd Agricultural robots recently become popular by helping the farmer to conduct their daily chores. A slow process of picking and grading will leads to an inaccurate result thus increasing the production cost. This study represents an innovative and economical alternative for farmers who require to undergone the process of estimating their maturity categories. A total of 1,200 chili images with 256×256 pixel are used, where 840 is used for training and the remaining 360 being served for testing. The maturity is determined by measuring the length of chili structure between the calyx and apex. Various convolutional neural network (CNN) architectures are applied to learn and recognize the chili fruits into three maturity categories; immature, moderately mature, and mature. ADAM and stochastic gradient descent with momentum (SGDM) optimizers with multiple CNN architectures is capable in recognising and classifying chilli fruits with an accuracy of above 85. Institute of Advanced Engineering and Science 2024 Article PeerReviewed Mohd Hussin, Najihah and Zainudin, Muhammad Noorazlan Shah and Mohd Saad, Wira Hidayat and Kamarudin, Muhammad Raihaan and Muhammad, Sufri and Razak, Muhd Shah Jehan Abd (2024) Chili fruits maturity estimation using various convolutional neural network architecture. Indonesian Journal of Electrical Engineering and Computer Science, 33 (1). pp. 557-567. ISSN 2502-4752; ESSN: 2502-4760 https://ijeecs.iaescore.com/index.php/IJEECS/article/view/27659 10.11591/ijeecs.v33.i1.pp557-567 |
| spellingShingle | Mohd Hussin, Najihah Zainudin, Muhammad Noorazlan Shah Mohd Saad, Wira Hidayat Kamarudin, Muhammad Raihaan Muhammad, Sufri Razak, Muhd Shah Jehan Abd Chili fruits maturity estimation using various convolutional neural network architecture |
| title | Chili fruits maturity estimation using various convolutional neural network architecture |
| title_full | Chili fruits maturity estimation using various convolutional neural network architecture |
| title_fullStr | Chili fruits maturity estimation using various convolutional neural network architecture |
| title_full_unstemmed | Chili fruits maturity estimation using various convolutional neural network architecture |
| title_short | Chili fruits maturity estimation using various convolutional neural network architecture |
| title_sort | chili fruits maturity estimation using various convolutional neural network architecture |
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