外文摘要：A rapid and accurate yield estimation is of great significance to the management and sustainable development of Camellia Oleifera forests. Consequently, the simultaneous and accurate detection of key phenology features of Camellia Oleifera (buds, flowers, fruits) is crucial for precise yield estimation. This not only enables robotic harvesting but also allows for the prediction of peak flowering and fruit ripening periods to determine the optimal harvesting time. However, in recent studies, only Camellia Oleifera fruits have been marked and detected. Therefore, to enable rapid yield estimation, it is necessary to simultaneously detect the key phenology stages (buds, flowers, fruits) of Camellia Oleifera. In this study, we annotated, trained, and predicted Camellia Oleifera buds, flowers, and fruits collected via field cameras to observe their daily quantitative changes. Quantity change curves were generated to estimate crucial phenology stages. Phenology feature detection and transfer learning were performed using the YOLO v5 model, widely used YOLO v3 model, and CenterNet model with center point prediction, all trained on the same dataset. The best model for phenology feature detection was selected based on a comparison of average precision, with the YOLO v5 model achieving a higher mean Average Precision (mAP) value of 91.31 % compared to the CenterNet (85.43 %) and YOLO v3 (81.00 %) models. In YOLO v5, the AP values for bud, flower, and fruit detection were 82.80 %, 98.13 %, and 92.99 %, respectively, surpassing the CenterNet model by 3.97 %, 2.44 %, and 11.23 %, and the YOLO v3 model by 6.39 %, 17.13 %, and 11.67 %. The image size was adapted from 4000 x 3000 pixels to 512 x 512 pixels for model optimization. Additionally, data from the Seedling Center of Liuyang City collected at different years and times were utilized to showcase the generalizability and scalability of the selected models, resulting in mAP values of 86.14 %, 80.17 %, and 69.20 % for the three above-mentioned models respectively. The plotted phenology change curves unveiled that Camellia Oleifera undergoes four stages: fruit enlargement period, bud enlargement period, flowering period, and flower wilting period. The conclusion can be drawn that using field cameras and YOLOv5 can simultaneously achieve real-time detection of key phenology features (buds, flowers, and fruits) of Camellia Oleifera, in order to further record crucial phenology patterns (such as flowering peaks and fruit ripening periods). This study offers theoretical references and scientific evidence for monitoring changes in key phenology features of Camellia Oleifera.
外文关键词：Camellia Oleifera;YOLO v5;Close -range photography;Field Camera;Phenology features
作者：Yan, Enping;Mo, Dengkui;Li, Haoran;Jiang, Jiawei
作者单位：Cent South Univ Forestry & Technol;Hunan Prov Key Lab Forestry Remote Sensing Based B;Key Lab Natl Forestry & Grassland Adm Forest Resou
期刊名称：COMPUTERS AND ELECTRONICS IN AGRICULTURE
期刊影响因子：0.0
出版年份：2024
出版刊次：219
原文传递申请：江苏省科技资源（工程技术文献）统筹服务平台