| Summary: | 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. © 2023 IEEE.
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