Monitoring of key Camellia Oleifera phenology features using field cameras and deep learning

钥匙(锁) 物候学 油茶 领域(数学) 人工智能 遥感 深度学习 计算机科学 地理 植物 数学 生态学 生物 纯数学
作者
Haoran Li.,Enping Yan,Jiawei Jiang,Dengkui Mo
出处
期刊:Computers and Electronics in Agriculture [Elsevier BV]
卷期号:219: 108748-108748 被引量:3
标识
DOI:10.1016/j.compag.2024.108748
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 × 3000 pixels to 512 × 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.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
刚刚
lijiaxin完成签到,获得积分10
1秒前
1秒前
2秒前
2秒前
Endeavor发布了新的文献求助10
2秒前
澡雪发布了新的文献求助10
3秒前
yang完成签到,获得积分20
3秒前
小马甲应助典雅的俊驰采纳,获得10
3秒前
4秒前
搜集达人应助啊啊啊啊采纳,获得10
5秒前
6秒前
6秒前
yuan完成签到,获得积分10
6秒前
小鱼发布了新的文献求助10
6秒前
7秒前
啊啊发布了新的文献求助10
7秒前
积极香菇发布了新的文献求助10
7秒前
追寻的雁完成签到,获得积分10
8秒前
科目三应助认真的焦采纳,获得10
9秒前
赵李锋完成签到,获得积分10
10秒前
fyjlfy发布了新的文献求助10
10秒前
11秒前
12秒前
12秒前
天边发布了新的文献求助10
13秒前
量子星尘发布了新的文献求助10
14秒前
14秒前
111完成签到,获得积分10
16秒前
KDC发布了新的文献求助10
16秒前
清脆大树发布了新的文献求助10
17秒前
天天快乐应助冷傲小猫咪采纳,获得10
17秒前
17秒前
啊啊啊啊发布了新的文献求助10
17秒前
SYLH应助Ion采纳,获得10
18秒前
万能图书馆应助天边采纳,获得10
19秒前
芈冖发布了新的文献求助10
19秒前
KDC完成签到,获得积分10
20秒前
20秒前
李健应助Endeavor采纳,获得10
20秒前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3975755
求助须知:如何正确求助?哪些是违规求助? 3520108
关于积分的说明 11200829
捐赠科研通 3256492
什么是DOI,文献DOI怎么找? 1798298
邀请新用户注册赠送积分活动 877509
科研通“疑难数据库(出版商)”最低求助积分说明 806403