https://scholars.lib.ntu.edu.tw/handle/123456789/639775
標題: | Deep Learning for Tracking Honeybee and Pollen Movement in Facility Cultivation | 作者: | Chang, Han Bin Chang, Shan Cheng Chueh, Cheng Yu Lin, Hung Jen JOE-AIR JIANG Liu, An Chi EN-CHENG YANG Hsieh, Hsiang Wen CHENG-YING CHOU |
關鍵字: | honeybee | image segmentation | object detection | object tracking | 公開日期: | 1-一月-2023 | 來源出版物: | 2023 ASABE Annual International Meeting | 摘要: | The pollination of facility cultivation is heavily dependent on honeybees. In limited space, using too many beehives can result in wasted costs and overcrowding, which can negatively impact the health of the bees due to increased competition. Conversely, using too few beehives can lead to a low pollination rate. Therefore, it is essential to identify an effective method for evaluating honeybee pollination rates. To address this issue, this research aims to count the number of honeybees entering and leaving the nest, as well as identify the pollen grains collected by the bees, to evaluate their pollination rate. Raspberry Pi HQ cameras have been installed on the lower, left, and right side of an acrylic observation channel to capture videos of bees entering and leaving the nest. Additionally, we used the YOLOv5 object detection model with an s6 backbone for honeybee recognition, achieving an accuracy of 99.6%. To prevent counting errors, the model's identification results were input into the StrongSORT tracking algorithm(s). By tracking the start and end points and length of the bees' trajectories, we set counting rules for entering and leaving the nest. As a result, the number of bees entering and leaving the nest can be determined. Furthermore, we extracted images of bees entering the nest from both sides and input them into the YOLACT instance segmentation model and Swin vision transformer separately for identification and comparison. The prediction accuracy of the two models in the pollen grain mask exceeded 86%. By combining the counting of bees entering and leaving the nest with the identification of pollen grains, we established an evaluation index for bee pollination rate. This allows for more accurate and efficient monitoring of the pollination process. In addition to knowing the number of bees entering and leaving the nest, as well as the collected pollen grains, it is possible to better determine the pollination rate and make necessary adjustments to increase crop yields and reduce costs. |
URI: | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85183583397&doi=10.13031%2faim.202300602&partnerID=40&md5=38fe93159557ea71edda5204c7066d69 https://scholars.lib.ntu.edu.tw/handle/123456789/639775 |
ISBN: | 9781713885887 | DOI: | 10.13031/aim.202300602 |
顯示於: | 生物機電工程學系 |
在 IR 系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。