| 基于无人机多光谱遥感的大豆生长参数和产量估算 |
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| 引用本文： | 向友珍,安嘉琪,赵笑,金琳,李志军,张富仓.基于无人机多光谱遥感的大豆生长参数和产量估算[J].农业机械学报,2023,54(8):230-239. |
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| 作者姓名： | 向友珍 安嘉琪 赵笑 金琳 李志军 张富仓 |
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| 作者单位： | 西北农林科技大学 |
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| 基金项目： | 国家自然科学基金项目（52179045） |
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| 摘 要： | 为适应现代农业发展对作物生长动态、连续、快速监测的要求，本文基于无人机多光谱遥感技术，以西北地区大豆作为研究对象，分别筛选出与大豆叶面积指数(Leaf area index, LAI)、地上部生物量和产量相关性较好的5个植被指数，采用支持向量机(Support vector machine, SVM)、随机森林(Random forest, RF)和反向神经网络(Back propagation neural network, BPNN)分别构建了大豆LAI、地上部生物量和产量的估计模型，并对模型进行了验证。结果表明，基于RF模型构建的大豆LAI和地上部生物量预测模型的精度显著高于SVM与BP模型，LAI估计模型验证集的R2为0.801,RMSE为0.675 m2/m2,MRE为18.684%;地上部生物量估算模型验证集的R2为0.745,RMSE为1 548.140 kg/hm2,MRE为18.770。而在产量的估算模型构建中，在大豆开花期(R4)基于RF模型构建的大豆产量预...
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| 关 键 词： | 大豆 多光谱 植被指数 叶面积指数 地上部生物量 产量 |
| 收稿时间： | 2022/12/9 0:00:00 |
Soybean Growth Parameters and Yield Estimation Based on UAV Multispectral Remote Sensing |
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| XIANG Youzhen,AN Jiaqi,ZHAO Xiao,JIN Lin,LI Zhijun,ZHANG Fucang.Soybean Growth Parameters and Yield Estimation Based on UAV Multispectral Remote Sensing[J].Transactions of the Chinese Society of Agricultural Machinery,2023,54(8):230-239. |
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| Authors: | XIANG Youzhen AN Jiaqi ZHAO Xiao JIN Lin LI Zhijun ZHANG Fucang |
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| Affiliation: | Northwest A&F University |
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| Abstract: | In order to meet the requirements of modern agriculture for dynamic, continuous, and rapid monitoring of crop growth, soybean was used as the research object based on UAV multispectral remote sensing technology in northwest China, and five vegetation indices were selected with the best correlation to soybean leaf area index (LAI), above-ground biomass and yield, and support vector machine (SVM), random forest (RF) and back propagation neural network (BPNN) were used to construct models for estimating soybean LAI, above-ground biomass and yield, respectively. RF and BPNN were used to construct and validate the models for estimating soybean LAI, aboveground biomass and yield, respectively. The results showed that the accuracy of soybean LAI and above ground biomass prediction models constructed based on the RF model was significantly higher than that of SVM and BP models, with R2 of 0.801, RMSE of 0.675m2/m2, and MRE of 18.684% for the validation set of LAI estimation model; R2 of 0.745, RMSE of 1548.140kg/hm2, and MRE of 18.770. In the estimation model construction of yield, the soybean yield prediction model constructed based on RF model in soybean flowering period (R4) had the highest accuracy with R2 of 0.818, RMSE of 287.539kg/hm2 and MRE of 7.128 in the validation set. The research results can provide a theoretical basis for the application of UAV multispectral remote sensing in crop monitoring and provide a rapid estimation of crop yield application reference. |
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