@inproceedings{71af782c20174360a79589e68caf8f54,
title = "A Deep Object Detection Method for Pineapple Fruit and Flower Recognition in Cluttered Background",
abstract = "Natural initiation of pineapple flowers is not synchronized, which yields difficulties in yield prediction and the decision of harvest. Computer vision based pineapple detection system is an automated solution to address this issue. However, it is faced with significant challenges, e.g. pineapple flowers and fruits vary in size at different growing stages, the images are influenced by camera viewpoint, illumination conditions, occlusion and so on. This paper presents an approach for pineapple fruit and flower recognition using a state-of-the-art deep object detection model. We collected images from pineapple orchard using three different cameras and selected suitable ones to create a dataset. The experimental results show promising detection performance, with an mAP of 0.64 and F1 score of 0.69.",
keywords = "Deep learning, Pineapple detection, YOLOv3",
author = "Chen Wang and Jun Zhou and Xu, \{Cheng yuan\} and Xiao Bai",
note = "Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 2nd International Conference on Pattern Recognition and Artificial Intelligence, ICPRAI 2020 ; Conference date: 19-10-2020 Through 23-10-2020",
year = "2020",
doi = "10.1007/978-3-030-59830-3\_19",
language = "英语",
isbn = "9783030598297",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "218--227",
editor = "Yue Lu and Nicole Vincent and Yuen, \{Pong Chi\} and Wei-Shi Zheng and Farida Cheriet and Suen, \{Ching Y.\}",
booktitle = "Pattern Recognition and Artificial Intelligence - International Conference, ICPRAI 2020, Proceedings",
address = "德国",
}