基于无人机多光谱的猕猴桃园冠层叶绿素含量检测方法  

Detection Method of Chlorophyll Content in Canopy of Kiwifruit Orchard Based on UAV

作　　者：霍迎秋[1,2] 赵士超 赵国淇 孙江昊 胡少军 HUO Yingqiu;ZHAO Shichao;ZHAO Guoqi;SUN Jianghao;HU Shaojun(College of Information Engineering,Northwest A&F University,Yangling,Shaanxi 712100,China;Key Laboratory for Agricultural Internet of Things,Ministry of Agriculture and Rural Affairs,Yangling,Shaanxi 712100,China)

机构地区：[1]西北农林科技大学信息工程学院,陕西杨凌712100 [2]农业农村部农业物联网重点实验室,陕西杨凌712100

出　　处：《农业机械学报》2024年第9期297-307,共11页Transactions of the Chinese Society for Agricultural Machinery

基　　金：陕西省重点研发计划项目(2023-YBNY-080);陕西省自然科学基础研究计划项目(2023-JC-YB-489);国家级大学生创新训练计划项目(202310712098);西安市科技计划项目(24NYGG0031)。

摘　　要：为实现对猕猴桃园区果树整体生长健康状况的快速、大规模监测,以猕猴桃园冠层叶片为研究对象,基于无人机拍摄果园多光谱图像,然后利用Pix4Dmapper软件拼接多光谱图像,获取果园的正射影像图,并进行辐射校正。切分正射影像为420个区域图像作为样本,采用最大类间方差法(Otsu)分割样本图像的冠层叶片与土壤背景,并实测每个样本的冠层SPAD值,构建冠层叶片多光谱数据集。采用箱线图法对数据集进行异常值检测,剔除异常样本;然后利用多光谱图像多通道的数据特点,提取图像的相邻通道变化率和23种常用植被指数,以及二者组合作为样本特征值,接着利用CARS、LARS、IRIV等3种特征筛选算法优选特征,分别结合偏最小二乘回归(PLSR)、支持向量回归(SVR)、岭回归(RR)、多元线性回归(MLR)和极限梯度提升树(XGBoost)、最小绝对收缩和选择算子回归(Lasso)、随机森林回归(RFR)、高斯过程回归(GPR)等8种方法构建模型,识别猕猴桃园冠层SPAD值;最后对比分析以不同样本特征构建的24个模型的性能,实验结果表明:以相邻通道变化率为特征建立的模型中,GPR模型性能最好,R^(2)、RMSE分别为0.770、3.044;以植被指数和相邻通道变化率组合特征建立的模型中,GPR模型性能也最好,R^(2)、RMSE分别为0.783、2.957;以植被指数为数据特征建立的XGBoost模型性能最优,R^(2)、RMSE分别为0.787、2.933;因此基于无人机遥感的智能检测模型能够对果园冠层叶绿素含量进行准确评估。Digitalization and intelligence play a crucial role in facilitating the high-quality development of the kiwifruit industry.Unlike other fruit trees,kiwi trees are vine plants that require abundant mineral nutrients during their key growth period.Inadequate management can easily lead to nutrient deficiencies,which not only affect the health of the trees but also impact the yield and quality of kiwis.Therefore,real-time monitoring of tree growth health is essential.To achieve fast and large-scale monitoring of overall growth and health in kiwi orchards,the drone was used to capture multispectral images of orchards,and then Pix4Dmapper software was utilized to splice UAV multispectral images for orthophoto maps and radiation correction on canopy leaves.The segmented orthophoto images were used as samples from 420 regions.The maximum inter-class variance(Otsu)method was employed to segment canopy leaves from soil backgrounds in the sample images,enabling measurement of canopy SPAD values for constructing a multispectral dataset.Firstly,outliers within the dataset were detected by using box plot analysis and subsequently removed as abnormal samples.Next,based on data characteristics derived from multi-channel images,feature values such as change rates between adjacent channels and 23 kinds of common vegetation indices were extracted,as well as their combination,to serve as sample feature values.Then three feature screening algorithms,including CARS,LARS,and IRIV were applied to optimize these features accordingly.Eight modeling methods,partial least square regression(PLSR),support vector regression(SVR),ridge regression(RR),multiple linear regression(MLR),extreme gradient boosting(XGBoost),least absolute shrinkage and selection operator regression(Lasso),random forest regression(RFR),and Gaussian process regression(GPR),were employed to construct models for identifying canopy chlorophyll content in macaque peach orchards.Finally,the performance of the 24 models constructed with different sample features was compared a

关 键 词：猕猴桃园 叶绿素含量 多光谱 机器学习 无人机 

分 类 号：S663.4[农业科学—果树学] S127[农业科学—园艺学]