摘要
作物产量快速准确预估对规模化农业生产具有重要意义。该研究在河北省涿州市开展田间试验,设置5个灌水处理及雨养对照区,使用无人机搭载多光谱相机在苜蓿的分枝期、现蕾期和初花期进行遥感监测,将光谱参数与苜蓿产量做相关性分析,分别挑选各生育时期相关性较好的5种植被指数,以去除土壤噪声后的5种植被指数和作物表面模型提取的苜蓿株高为输入变量,通过支持向量回归算法建立各生育时期的苜蓿产量预测模型,并结合实测产量进行模型评价。结果表明,苜蓿产量预测模型精度由高到低的生育时期依次为初花期、现蕾期、分枝期,使用植被指数和株高组合作为输入变量可以提高产量模型的预测精度。在初花期使用植被指数和株高组合输入变量所构建的估产模型最优,其决定系数、均方根误差和标准均方根误差分别为0.90、500 kg/hm2和14.3%,可用于苜蓿产量的快速准确评估。研究结果可为苜蓿的规模化生产和精细化管理提供技术支持。
Alfalfa has been one of the most widely grown forage crops around the world.The“king of forage”,alfalfa can be known as the high production of grass rich in nutrients.Timely and accurate monitoring of alfalfa growth and yield can be a high demand for large-scale agricultural production in recent years.In this study,a series of yield prediction models were established for the alfalfa using Unmanned Aerial Vehicle(UAV)remote sensing and machine learning.A field experiment was also performed in Zhuozhou City,Hebei Province,China.Five irrigation modes and a rainfed treatment were set for different alfalfa growth statuses and yield prediction.The UAV multispectral platform was used to monitor the alfalfa during the growing stage of branching,budding,and early blooming.A correlation analysis was then made to determine the alfalfa yields and 11 spectral parameters(vegetation indices).The top five correlation indexes were picked out for each growth stage.Then,a Structure from Motion(SfM)imaging technology was used to reconstruct the plant height of alfalfa.The high accuracy of prediction was achieved with the determination coefficient(R2)of 0.86,the Root Mean Square Error(RMSE)of 6.8 cm,and the Normalized Root Mean Square Error(NRMSE)of 14.4%,compared with the measurement.Eventually,three yield prediction models were established by Support Vector Regression(SVR).Specifically,the measured yield was always used as the output,but the input was used the vegetation indices,the plant height,as well as the combination of vegetation indices and plant height.Soil noise was also removed from the vegetation indices for the best performance.The results showed that there was the most significant correlation of vegetation indices with the measured alfalfa yield during the branching,budding,and early blooming stage.In the branching stage,the R2 values of the three models were between 0.4 and 0.6.The NRMSEs of the models were greater than 30%with the vegetation indices and plant height as the input variables.In the budding stage,the R2 values were above 0.8,and the NRMSEs were less than 20%for all three models.The highest accuracy was achieved in the model with the combination of vegetation indices and plant height as the input variables,with the R2 of 0.87,the RMSE of 564 kg/hm2,and the NRMSE of 16.1%.The R2 value was 0.72 in the yield prediction model with the vegetation indices only as the input variable in the early blooming stage,indicating a lower accuracy than that in the budding stage.However,the R2 values were 0.89 and 0.90 in the models with the plant height only,and the combination of vegetation indices and plant height as the input variables,respectively,while the NRMSEs were lower than 15%,indicating a higher accuracy than that in the budding stage.A higher accuracy of the yield prediction model was found,as the alfalfa grew.The combination of plant height and vegetation indices as the input variables can also be expected to improve the accuracy of the yield prediction model.The best yield prediction was achieved in the model with the combination of five vegetation indices and plant height as the inputs at an early blooming stage,with the R2 of 0.90,the RMSE of 500 kg/hm2,and the NRMSE of 14.3%.The optimal model can be strongly recommended for the rapid and accurate prediction of alfalfa yield.The finding can provide technical support to the large-scale production and precision management of alfalfa.
作者
严海军
卓越
李茂娜
王云玲
郭辉
王晶晶
李长硕
丁峰
Yan Haijun;Zhuo Yue;Li Maona;Wang Yunling;Guo Hui;Wang Jingjing;Li Changshuo;Ding Feng(College of Water Resources and Civil Engineering,China Agricultural University,Beijing 100083,China;Engineering Research Center of Agricultural Water-Saving and Water Resources,Ministry of Education,Beijing 100083,China;College of Grassland Science and Technology,China Agricultural University,Beijing 100193,China;Research Institute of Soil,Fertilizer and Agricultural Water Conservation,Xinjiang Academy of Agricultural Sciences,Urumqi 830091,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2022年第11期64-71,共8页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家自然科学基金项目(51939005)
河北省现代农业产业体系草业创新团队专项资金资助项目(HBCT2018160202)
自治区区域协同创新专项(科技援疆计划)(2021E02056)
国家牧草产业技术体系专项(CARS-34)。
关键词
无人机
产量
植被指数
苜蓿
支持向量回归
生育时期
unmanned aerial vehicle
yield
vegetation index
alfalfa
support vector regression
growth stage
