[1]洪国军,张灵,徐恒,等.多高度无人机多光谱成像在枣树不同生育期LAI监测中的应用[J].江苏农业学报,2024,(11):2093-2101.[doi:doi:10.3969/j.issn.1000-4440.2024.11.013]
　HONG Guojun,ZHANG Ling,XU Heng,et al.Application of multi-altitude UAV multi-spectral imaging in LAI monitoring of jujube trees at different growth stages[J].,2024,(11):2093-2101.[doi:doi:10.3969/j.issn.1000-4440.2024.11.013]
点击复制

多高度无人机多光谱成像在枣树不同生育期LAI监测中的应用()
分享到：

江苏农业学报[ISSN:1006-6977/CN:61-1281/TN]

卷:
期数:
2024年11期
页码:
2093-2101
栏目:
农业信息工程
出版日期:
2024-11-30

文章信息/Info

Title:
Application of multi-altitude UAV multi-spectral imaging in LAI monitoring of jujube trees at different growth stages
作者:
洪国军1; 张灵1; 徐恒2; 喻彩丽3; 黄玉芬4; 5; 范振岐4; 5
(1.江西科技学院区域发展研究院,江西南昌330200；2.江西科技学院理学教育部,江西南昌330200；3.汕尾职业技术学院海洋学院,广东汕尾516600；4.塔里木大学信息工程学院,新疆阿拉尔843300；5.塔里木绿洲农业教育部重点实验室,新疆阿拉尔843300
Author(s):
HONG Guojun1; ZHANG Ling1; XU Heng2; YU Caili3; HUANG Yufen4; 5; FAN Zhenqi4; 5
(1.Institute of Regional Development, Jiangxi University of Technology, Nanchang 330200, China;2.Department of Science and Education, Jiangxi University of Technology, Nanchang 330200, China;3.College of Ocean, Shanwei Institute of Technology, Shanwei 516600, China;4.College of Information Engineering, Tarim University, Alaer 843300, China;5.Key Laboratory of Tarim Oasis Agriculture, Ministry of Education, Alaer 843300, China)
关键词:
枣树; 叶面积指数; TPE优化算法; CatBoost; 特征优选; 模型参数选优
Keywords:
jujube tree; leaf area index; TPE optimization algorithm; CatBoost; feature optimization; model parameter optimization
分类号:
S252+.9
DOI:
doi:10.3969/j.issn.1000-4440.2024.11.013
文献标志码:
A
摘要:
为了实现枣树叶面积指数(LAI)的快速估算,使用无人机多光谱相机获取新疆阿拉尔垦区枣树3个生育期的冠层无人机影像,并在地面同步测定样本点的LAI值,以180种植被指数为基础构建模型,采用贝叶斯算法中的树结构Parzen估计器(TPE),提取最优特征组合并优化模型参数,实现模型性能的全面提升,对比分析各模型(CatBoost、RF、DNN、SVR)对枣树LAI值的监测能力。结果表明,(1)在相同飞行高度下,在坐果期的表现中,4个模型中TPE-CatBoost模型在60 m飞行高度的性能最好,决定系数（R2）为0.867 5,均方误差（MSE）为0.005 2；(2)利用空间插值法、TPE-CatBoost模型对枣树LAI进行分析,揭示了整体趋势和精确的局部分布情况。研究提出的TPE-CatBoost模型实现了垦区枣园枣树LAI值的有效监测,为垦区枣园的生长监测提供了有效的技术参考。
Abstract:
In order to achieve rapid estimation of leaf area index (LAI) of jujube trees, unmanned aerial vehicle (UAV) multispectral cameras were used to obtain canopy UAV images of jujube trees at three growth stages in Alar Reclamation Area. The LAI values of sample points were measured synchronously on the ground. A model was constructed based on 180 vegetation indices, and the tree structure Parzen estimator (TPE) in Bayesian algorithm was used to extract the optimal feature combination and optimize the model parameters, so as to improve the performance of the model. The monitoring ability of models (CatBoost, RF, DNN, SVR) for jujube tree LAI values was compared and analyzed. The results showed that the TPE-CatBoost model was the best among the four models during the fruit setting period at a flight altitude of 60 meters, with a coefficient of determination (R2 ) of 0.867 5 and a mean square error (MSE) of 0.005 2, respectively. The spatial interpolation method and TPE-CatBoost model were used to analyze the LAI of jujube trees, revealing the overall trend and accurate local distribution. The TPE-CatBoost model proposed in this study can effectively monitor the LAI of jujube trees in reclaimed jujube orchards, providing an effective technical reference for the growth monitoring of jujube in reclaimed areas.

参考文献/References:

[1]ZOU M X, LIU Y, FU M D, et al. Combining spectral and texture feature of UAV image with plant height to improve LAI estimation of winter wheat at jointing stage[J]. Frontiers in Plant Science,2024,14:1272049.
[2]DABROWSKI P, KEUTGEN A J, KEUTGEN N, et al. Photosynthetic efficiency of perennial ryegrass (Lolium perenne L.) seedlings in response to Ni and Cd stress[J]. Scientific Reports,2023,13:5357.
[3]刘丽莹,付祥钊,罗志强,等. 小叶榕气生根气体交换特征及影响因子研究[J]. 热带亚热带植物学报,2011,19(1):45-50.
[4]孟畅,赵杨,杨菊,等. 光质对皂荚幼苗生长发育、光合特性及相关生理指标的影响[J]. 植物生理学报,2022,58(10):1961-1972.
[5]苏钰格,包云轩,唐倩,等. 基于地面高光谱遥感的稻纵卷叶螟为害下的水稻LAI估算模型研究[J]. 中国植保导刊,2023,43(1):44-51.
[6]ZHUO W,FANG S B,GAO X R,et al. Crop yield prediction using MODIS LAI,TIGGE weather forecasts and WOFOST model:a case study for winter wheat in Hebei,China during 2009-2013[J]. International Journal of Applied Earth Observation and Geoinformation,2022,106:102668.
[7]杨楠,周萌,陈欢,等. 基于无人机RGB影像的小麦叶面积指数与产量估算研究[J]. 麦类作物学报,2023,43(7):920-932.
[8]CHEN Y, ZHANG Z, TAO F L. Improving regional winter wheat yield estimation through assimilation of phenology and leaf area index from remote sensing data[J]. European Journal of Agronomy,2018,101:163-173.
[9]BAI T C, ZHANG N N, MERCATORIS B, et al. Improving jujube fruit tree yield estimation at the field scale by assimilating a single landsat remotely-sensed LAI into the WOFOST model[J]. Remote Sensing,2019,11(9):1119.
[10]谷晓博,程智楷,周智辉,等. 基于特征降维和机器学习的覆膜冬小麦LAI遥感反演[J]. 农业机械学报,2023,54(6):148-157,167.
[11]JIN X L, ZARCO-TEJADA P J, SCHMIDHALTER U, et al. High-throughput estimation of crop traits:a review of ground and aerial phenotyping platforms[J]. IEEE Geoscience and Remote Sensing Magazine,2021,9(1):200-231.
[12]孙永祺,陈梦媛,黄倩,等. 低空无人机遥感在油料作物表型分析中的应用[J]. 浙江大学学报(农业与生命科学版),2023,49(4):472-483.
[13]SHAO G M, HAN W T, ZHANG H H, et al. Mapping maize crop coefficient Kc using random forest algorithm based on leaf area index and UAV-based multispectral vegetation indices[J]. Agricultural Water Management,2021,252:106906.
[14]刘帅兵,金秀良,冯海宽,等. 基于无人机多源遥感的玉米LAI垂直分布估算[J]. 农业机械学报,2023,54(5):181-193,287.
[15]ZOU M X, LIU Y, FU M D, et al. Combining spectral and texture feature of UAV image with plant height to improve LAI estimation of winter wheat at jointing stage[J]. Frontiers in Plant Science,2024,14:1272049.
[16]CARUSO G, PALAI G, TOZZINI L, et al. The role of LAI and leaf chlorophyll on NDVI estimated by UAV in grapevine canopies[J]. Scientia Horticulturae,2023,322:112398.
[17]LIU S B, JIN X L, BAI Y, et al. UAV multispectral images for accurate estimation of the maize LAI considering the effect of soil background[J]. International Journal of Applied Earth Observation and Geoinformation,2023,121:103383.
[18]陈家华,张立福,黄长平,等. 基于Sentinel-2A影像光谱和纹理特征的冬小麦叶面积指数估算模型研究[J]. 遥感技术与应用,2024,39(2):290-305.
[19]LI X N, XU X Y, XIANG S, et al. Soybean leaf estimation based on RGB images and machine learning methods[J]. Plant Methods,2023,19(1):59.
[20]石浩磊,曹红霞,张伟杰,等. 基于无人机多光谱的棉花多生育期叶面积指数反演[J]. 中国农业科学,2024,57(1):80-95.
[21]张传波,李卫国,王晶,等. 波段反射率和植被指数结合的作物生长季农田土壤水分估测[J]. 江苏农业学报,2022,38(1):111-118.
[22]BLASCH G, ANBERBIR T, NEGASH T, et al. The potential of UAV and very high-resolution satellite imagery for yellow and stem rust detection and phenotyping in Ethiopia[J]. Scientific Reports,2023,13:16768.
[23]韩振强,李卫国,张晓东,等. 多遥感光谱指标结合进行大田冬小麦叶片叶绿素含量估测研究[J]. 麦类作物学报,2023,43(11):1467-1474.
[24]洪国军,谢俊博,张 灵,等. 基于多光谱影像的阿拉尔垦区棉田土壤盐分反演[J]. 干旱区研究,2024,41(5):894-904.
[25]GOVI D, PAPPALARDO S E, DE MARCHI M, et al. From space to field:combining satellite,UAV and agronomic data in an open-source methodology for the validation of NDVI maps in precision viticulture[J]. Remote Sensing,2024,16(5):735.
[26]MOSCOVINI L, ORTENZI L, PALLOTTINO F, et al. An open-source machine-learning application for predicting pixel-to-pixel NDVI regression from RGB calibrated images[J]. Computers and Electronics in Agriculture,2024,216:108536.
[27]吴运力,张钰,田佳榕. 气候变化和人类活动对内蒙古高原不同植被类型NDVI的影响[J]. 中国农业气象,2023,44(12):1155-1168.
[28]XIAO W, WANG C B, LIU J M, et al. Optimizing faulting prediction for rigid pavements using a hybrid SHAP-TPE-CatBoost model[J]. Applied Sciences,2023,13(23):12862.
[29]张锡煜,李思佳,王翔,等. 基于Sentinel-2卫星影像的黑龙江绥化市土壤全氮定量遥感反演[J]. 农业工程学报,2023,39(15):144-151.
[30]SZOSTAK B, SPECHT M, BURDZIAKOWSKI P, et al. Methodology for performing bathymetric measurements of shallow waterbodies using an UAV,and their processing based on the SVR algorithm[J]. Measurement,2023,223:113720.

相似文献/References:

[1]安霞,董月,吴建燕,等.氮肥形态对甘薯产量和养分吸收的影响[J].江苏农业学报,2016,(05):1049.[doi:10.3969/j.issn.1000-4440.2016.05.015]
　AN Xia,DONG Yue,WU Jian-yan,et al.Effects of forms of nitrogen fertilizer on yield and nutrient uptake of sweet potato[J].,2016,(11):1049.[doi:10.3969/j.issn.1000-4440.2016.05.015]

备注/Memo

备注/Memo:
收稿日期:2024-07-17基金项目:国家自然科学基金地区基金项目(42061046、61662064)作者简介:洪国军(1995-),男,江西乐平人,硕士研究生,研究方向为遥感与数字农业。(E-mail)hgj950603@163.com通讯作者:范振岐,(E-mail)alaeclp@126.com
更新日期/Last Update: 2025-01-20