Citation

Naghipour Khanaposhtani, Mohammadmahdi and Lew, Sook Ling and Tee, Connie (2024) A Review of AI Techniques in Fruit Detection and Classification: Analyzing Data, Features and AI Models Used in Agricultural Industry. International Journal of Technology, 15 (3). p. 585. ISSN 2086-9614

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Abstract

Artificial Intelligence (AI) techniques are used in agricultural industry for detecting, classifying, and assessing the quality of fruits. The primary focus of the discussed fruits pertains to oil palm fresh fruit bunches (FFB), which have been a significant contributor to Malaysia's economy. The quantity of research concerning oil palm FFB is limited and has not received extensive attention in the literature. A clear guide regarding the most useful types of data and features in the field is absent. Different concerns also persist regarding the ability of AI models to perform agricultural tasks with sufficient accuracy. Therefore, this review aims to explore the significant data, features, and AI models used, ascertain the performance level in the domain, and contribute an informative analysis of agricultural and oil palm fields. In this context, various types of data, capturing devices, public datasets, features, and diverse AI models used in agricultural industry are subjected to analysis. Most of the analyzed research achieved above 90% performance in terms of accuracy, coefficient of determination (R2), as well as sensitivity and mean average precision (mAP). The results show that there is a high capability of AI to perform agricultural tasks with high accuracy. In this context, the literature is thoroughly examined to provide a comprehensive understanding of the different elements of AI in agricultural industry.

Item Type:	Article
Uncontrolled Keywords:	Artificial intelligence, agriculture
Subjects:	Q Science > Q Science (General) > Q300-390 Cybernetics S Agriculture > S Agriculture (General)
Divisions:	Faculty of Information Science and Technology (FIST)
Depositing User:	Ms Nurul Iqtiani Ahmad
Date Deposited:	02 Jul 2024 01:11
Last Modified:	02 Jul 2024 01:11
URII:	http://shdl.mmu.edu.my/id/eprint/12536

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