李长春,李亚聪,王艺琳,马春艳,陈伟男,丁凡.基于小波能量系数和叶面积指数的冬小麦生物量估算[J].农业机械学报,2021,52(12):191-200.
LI Changchun,LI Yacong,WANG Yilin,MA Chunyan,CHEN Weinan,DING Fan.Winter Wheat Biomass Estimation Based on Wavelet Energy Coefficient and Leaf Area Index[J].Transactions of the Chinese Society for Agricultural Machinery,2021,52(12):191-200.
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基于小波能量系数和叶面积指数的冬小麦生物量估算   [下载全文]
Winter Wheat Biomass Estimation Based on Wavelet Energy Coefficient and Leaf Area Index   [Download Pdf][in English]
投稿时间:2021-07-31  
DOI:10.6041/j.issn.1000-1298.2021.12.020
中文关键词:  冬小麦  生物量  小波能量系数  叶面积指数  高斯过程回归
基金项目:国家自然科学基金项目(41871333)和河南省科技攻关项目(212102110238)
作者单位
李长春 河南理工大学 
李亚聪 河南理工大学 
王艺琳 河南理工大学 
马春艳 河南理工大学 
陈伟男 河南理工大学 
丁凡 河南理工大学 
中文摘要:生物量是评价作物长势及产量估算的重要指标,科学、快速、准确地获取生物量信息,对于监测冬小麦生长状况以及产量预测等具有重要意义。以冬小麦为研究对象,通过相关性分析,选取相关性较好的小波能量系数,同时耦合叶面积指数,基于支持向量回归算法、随机森林算法、高斯过程回归3种算法构建冬小麦生物量估算模型。结果显示,基于小波能量系数,分别利用支持向量回归算法、随机森林算法、高斯过程回归进行生物量估算,4个生育期的验证R2分别是0.55、0.40、0.39;0.75、0.70、0.83;0.84、0.92、0.93;0.84、0.89、0.85。表明高斯过程回归模型估算精度最优。叶面积指数耦合小波能量系数,利用支持向量回归算法、随机森林回归算法、高斯过程回归进行生物量估算,4个生育期的验证R2分别是0.76、0.73、0.77;0.76、0.72、0.84;0.87、0.94、0.94;0.85、0.90、0.91。表明高斯过程回归算法估算精度最优,并且在一定程度上能够克服冠层光谱饱和现象,提高模型估算精度。以小波能量系数和叶面积指数为输入变量结合高斯过程回归算法建立冬小麦生物量估算模型,可以提高生物量估算精度,为基于遥感技术的作物参数快速估算提供参考。
LI Changchun  LI Yacong  WANG Yilin  MA Chunyan  CHEN Weinan  DING Fan
Henan Polytechnic University
Key Words:winter wheat  biomass  wavelet energy coefficient  leaf area index  Gaussian process regression
Abstract:Biomass is an important indicator for evaluating crop growth and yield estimation. Obtaining biomass information scientifically, quickly and accurately is of great significance for monitoring the growth status of winter wheat and yield prediction. Taking winter wheat as the research object, through correlation analysis, the wavelet energy coefficient with good correlation was selected, and the leaf area index was coupled at the same time. Based on the support vector regression algorithm, random forest algorithm, and Gaussian process regression, three algorithms were used to construct a winter wheat biomass estimation model. The verification R2 of the four growth periods were 0.55, 0.40 and 0.39; 0.75, 0.70 and 0.83; 0.84, 0.92 and 0.93; 0.84, 0.89 and 0.85, respectively. It was showed that the estimation accuracy of Gaussian process regression model was the best. Leaf area index coupled with wavelet energy coefficients, using the three algorithms to estimate biomass, the verification R2 of the four growth periods were 0.76, 0.73 and 0.77; 0.76, 0.72 and 0.84; 0.87, 0.94 and 0.94; 0.85, 0.90 and 0.91, respectively, indicating that the Gaussian process regression algorithm had the best estimation accuracy, and to a certain extent, it can overcome the canopy spectrum saturation phenomenon and improve the estimation accuracy of the model. Using wavelet energy coefficient and leaf area index as input variables combined with Gaussian process regression algorithm to establish a winter wheat biomass estimation model, which can improve the accuracy of biomass estimation and provide a scientific reference for the rapid estimation of crop parameters 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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