Details

Autor(en) / Beteiligte

• Roslan, Muhammad Izzat
• Ibrahim, Zaidah
• Adnan, Nur Aina Khadijah
• Diah, Norizan Mat
• Narawi, Nur Azima Alya
• Arif, Yunifa Miftachul

Titel

Fruit Detection and Recognition Using Faster R-CNN with FPN30 Pre-trained Network

Ist Teil von

• 2023 IEEE 8th International Conference on Recent Advances and Innovations in Engineering (ICRAIE), 2023, p.1-6

Ort / Verlag

IEEE

Erscheinungsjahr

2023

Quelle

IEEE Electronic Library (IEL)

Beschreibungen/Notizen

• 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.