马春艳,王艺琳,翟丽婷,郭辅臣,李长春,牛海鹏.冬小麦不同叶位叶片的叶绿素含量高光谱估算模型[J].农业机械学报,2022,53(6):217-225,358.
MA Chunyan,WANG Yilin,ZHAI Liting,GUO Fuchen,LI Changchun,NIU Haipeng.Hyperspectral Estimation Model of Chlorophyll Content in Different Leaf Positions of Winter Wheat[J].Transactions of the Chinese Society for Agricultural Machinery,2022,53(6):217-225,358.
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冬小麦不同叶位叶片的叶绿素含量高光谱估算模型   [下载全文]
Hyperspectral Estimation Model of Chlorophyll Content in Different Leaf Positions of Winter Wheat   [Download Pdf][in English]
投稿时间:2021-11-28  
DOI:10.6041/j.issn.1000-1298.2022.06.023
中文关键词:  冬小麦  不同叶位  叶绿素含量  高光谱  机器学习算法
基金项目:国家自然科学基金项目(41871333)和河南省高校科技创新团队支持计划项目(22IRTSTHN008)
作者单位
马春艳 河南理工大学 
王艺琳 河南理工大学 
翟丽婷 河南理工大学 
郭辅臣 河南理工大学 
李长春 河南理工大学 
牛海鹏 河南理工大学 
中文摘要:科学、高效地获取作物不同叶位叶绿素含量的垂直分布信息,可监测农作物长势状况并进行田间管理。基于冬小麦抽穗期获取的不同叶位叶片的高光谱反射率和叶绿素含量实测数据,将原始光谱、一阶微分光谱、二阶微分光谱、植被指数和连续小波系数与叶绿素含量进行相关性分析,筛选相关性较强的光谱特征参数,然后分别采用偏最小二乘回归、支持向量机、随机森林和反向传播神经网络4种机器学习算法构建冬小麦上1叶、上2叶、上3叶和上4叶的叶绿素含量估算模型,并根据精度评估结果筛选不同叶位叶绿素含量估算的最佳模型。结果表明,上1叶、上2叶和上3叶采用小波系数结合偏最小二乘回归构建的叶绿素含量估算模型精度最高,建模和验证R2分别为0.82和0.75、0.80和0.77、0.71和0.62;上4叶采用植被指数结合支持向量机构建的叶绿素含量估算模型效果最佳,建模和验证R2为0.74和0.79。研究结果可为基于遥感技术精准监测作物营养成分的垂直变化特征提供理论和技术支撑。
MA Chunyan  WANG Yilin  ZHAI Liting  GUO Fuchen  LI Changchun  NIU Haipeng
Henan Polytechnic University
Key Words:winter wheat  different leaf positions  chlorophyll content  hyperspectral  machine learning algorithm
Abstract:The information of vertical distribution of chlorophyll content in different leaf positions of crops was obtained scientifically and efficiently to facilitate monitoring of crop growth conditions and field management. Based on the hyperspectral reflectance and chlorophyll content of different leaf positions of winter wheat obtained during the heading period, the correlation analysis of raw spectra, first-order differential spectra, second-order differential spectra, vegetation indices, continuous wavelet coefficients and chlorophyll content were performed to screen the spectral feature parameters with strong correlation. Then partial least squares regression, support vector machine, random forest and back propagation neural network algorithms were employed to construct chlorophyll content estimation models for the upper 1, upper 2, upper 3 and upper 4 leaves of winter wheat, and the best models for chlorophyll content estimation at different leaf positions were screened based on the accuracy assessment results. The results showed that the chlorophyll content estimation models constructed using wavelet coefficients combined with partial least squares were the most accurate for the upper 1, upper 2 and upper 3 leaves, with modeling and validation R2 of 0.82 and 0.75, 0.80 and 0.77, 0.71 and 0.62, respectively; the chlorophyll content estimation models constructed using vegetation indices combined with support vector machine were the best for the upper 4 leaves, with modeling and validation R2 of 0.74 and 0.79, respectively. The research result could provide theoretical and technical support for accurate monitoring of the vertical variation characteristics of crop nutrient content based on remote sensing technology.

Transactions of the Chinese Society for Agriculture Machinery (CSAM), in charged of China Association for Science and Technology (CAST), sponsored by CSAM and Chinese Academy of Agricultural Mechanization Science(CAAMS), started publication in 1957. It is the earliest interdisciplinary journal in Chinese which combines agricultural and engineering. It always closely grasps the development direction of agriculture engineering disciplines and the published papers represent the highest academic level of agriculture engineering in China. Currently, nearly 8,000 papers have been already published. There are around 3,000 papers contributed to the journal each year, but only around 600 of them will be accepted. Transactions of CSAM focuses on a wide range of agricultural machinery, irrigation, electronics, robotics, agro-products engineering, biological energy, agricultural structures and environment and more. Subjects in Transactions of the CSAM have been embodied by many internationally well-known index systems, such as: EI Compendex, CA, CSA, etc.

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