Chili fruits maturity estimation using various convolutional neural network architecture

Zainudin, Muhammad Noorazlan Shah and Mohd Hussin, Najihah and Mohd Saad, Wira Hidayat and Kamarudin, Muhammad Raihaan and Muhammad, Sufri and Abd Razak, Muhd Shah Jehan (2023) 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

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Abstract

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%.

Item Type:	Article
Uncontrolled Keywords:	ADAM, Agricultural, Chili fruits, Convolutional neural network, SGDM
Divisions:	Faculty Of Electronics And Computer Technology And Engineering
Depositing User:	Sabariah Ismail
Date Deposited:	24 Jul 2024 16:00
Last Modified:	24 Jul 2024 16:00
URI:	http://eprints.utem.edu.my/id/eprint/27580
Statistic Details:	View Download Statistic

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