清晨好,您是今天最早来到科研通的研友!由于当前在线用户较少,发布求助请尽量完整地填写文献信息,科研通机器人24小时在线,伴您科研之路漫漫前行!

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
vbnn完成签到 ,获得积分10
2秒前
11秒前
过氧化氢发布了新的文献求助30
16秒前
woxinyouyou完成签到,获得积分0
25秒前
Demi_Ming发布了新的文献求助10
28秒前
Am完成签到 ,获得积分10
33秒前
Hello应助科研通管家采纳,获得10
1分钟前
田様应助科研通管家采纳,获得10
1分钟前
1分钟前
英俊的铭应助和谐乌龟采纳,获得10
1分钟前
负责以山完成签到 ,获得积分10
2分钟前
胖小羊完成签到 ,获得积分10
2分钟前
Akim应助Demi_Ming采纳,获得10
2分钟前
2分钟前
sunshine完成签到 ,获得积分10
2分钟前
美满的冬卉完成签到 ,获得积分10
2分钟前
Axs完成签到,获得积分10
3分钟前
完美世界应助科研通管家采纳,获得10
3分钟前
megumin完成签到,获得积分10
3分钟前
TEY完成签到 ,获得积分10
3分钟前
jiangqin123完成签到 ,获得积分10
3分钟前
菠萝包完成签到 ,获得积分10
3分钟前
3分钟前
3分钟前
Demi_Ming发布了新的文献求助10
3分钟前
winne完成签到,获得积分10
3分钟前
3分钟前
wanci应助李小猫采纳,获得10
4分钟前
山楂完成签到,获得积分10
4分钟前
4分钟前
4分钟前
和谐乌龟发布了新的文献求助10
4分钟前
和谐乌龟完成签到,获得积分10
4分钟前
ttyhtg完成签到,获得积分10
4分钟前
4分钟前
bkagyin应助萧萧采纳,获得10
4分钟前
阿巴完成签到 ,获得积分10
4分钟前
李小猫发布了新的文献求助10
4分钟前
Hello应助科研通管家采纳,获得10
5分钟前
drhwang完成签到,获得积分10
5分钟前
高分求助中
A new approach to the extrapolation of accelerated life test data 1000
Cognitive Neuroscience: The Biology of the Mind 1000
Technical Brochure TB 814: LPIT applications in HV gas insulated switchgear 1000
Immigrant Incorporation in East Asian Democracies 600
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
A Preliminary Study on Correlation Between Independent Components of Facial Thermal Images and Subjective Assessment of Chronic Stress 500
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 3968504
求助须知:如何正确求助?哪些是违规求助? 3513318
关于积分的说明 11167297
捐赠科研通 3248697
什么是DOI,文献DOI怎么找? 1794414
邀请新用户注册赠送积分活动 875030
科研通“疑难数据库(出版商)”最低求助积分说明 804652