Fruit detection and recognition using faster R-CNN with FPN30 Pre-trained Network

Roslan, Muhammad Izzat, Ibrahim, Zaidah, Adnan, Nur Aina Khadijah, Diah, Norizan Mat, Narawi, Nur Azima Alya and Arif, Yunifa Miftachul ORCID: https://orcid.org/0000-0002-2183-0762 (2024) Fruit detection and recognition using faster R-CNN with FPN30 Pre-trained Network. Presented at IEEE 8th International Conference on Recent Advances and Innovations in Engineering (ICRAIE).

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Abstract

Accurate and reliable fruit detection and recognition in orchards is critical for enabling higher-level agriculture tasks such as fruit picking. However, detecting and recognizing fruits with occlusion by neighboring fruits is extremely difficult. Faster R-CNN (Faster Region-based Convolutional Neural Network) is a well-known deep learning technology for object detection and recognition. Thus, this study investigates the application of Faster R-CNN for apple detection and recognition. Two different datasets have been constructed under variable illumination conditions and occlusion; an inter-class dataset that consists of images of apples and oranges, and an intra-class dataset that consists of images of two types of apples, namely fuji and royal gala apples. Results indicate that Faster R-CNN can detect and recognize apples from oranges, and the fuji apple in the orchards, with high accuracy. This suggests that Faster R-CNN can be used practically in the real orchard context.

Item Type:	Conference (Paper)
Keywords:	artificial intelligence; fruit detection; Faster R-CNN; feature pyramid network; FPN; pre-trained network
Subjects:	08 INFORMATION AND COMPUTING SCIENCES > 0801 Artificial Intelligence and Image Processing
Divisions:	Faculty of Technology > Department of Informatics Engineering
Depositing User:	Yunifa Miftachul Arif
Date Deposited:	02 May 2024 10:39

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