| 基于多时相多参数融合的麦玉轮作小麦产量估算方法 |
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| 引用本文： | 李阳,苑严伟,赵博,王吉中,伟利国,董鑫.基于多时相多参数融合的麦玉轮作小麦产量估算方法[J].农业机械学报,2023,54(12):186-196. |
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| 作者姓名： | 李阳 苑严伟 赵博 王吉中 伟利国 董鑫 |
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| 作者单位： | 中国农业机械化科学研究院集团有限公司 |
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| 基金项目： | 国家重点研发计划项目（2022YFD2001502） |
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| 摘 要： | 为了进一步提高冬小麦产量预测的准确性，针对麦玉轮作体系缺乏直接把前茬作物信息纳入到当季作物的产量估算及管理中的研究状况，利用前茬玉米季中长势遥感信息及产量信息，融合小麦拔节期、灌浆期及成熟期长势遥感信息、播前施肥信息及土壤特性信息等多时相多模态数据，基于GPR算法，建立多时相多模态参数融合的麦玉轮作体系小麦产量估算模型，结果显示：基于多生育期的产量估算模型较单生育期最优产量估算模型性能有所提升，R2提高0.01～0.03。其中基于拔节期产量估算模型精度略低于多生育期产量估算模型，但精度相近。基于多模态参数融合的产量估算模型中，除玉米作物信息与土壤特性信息融合构建的产量估算模型，多模态参数融合的产量估算模型精度较相应低模态参数融合的产量估算模型精度高。四模态参数融合的GPR模型决定系数R2为0.92,RMSE为213.75 kg/hm2,较其他模型，R2提高0.02～0.41。对于小麦产量估算模型，各模态参数影响由大到小依次为施肥信息、小麦遥感信息、土壤特性信息、玉米作物信息。玉米作物信息对于多模态参...
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| 关 键 词： | 小麦 玉米 产量估算模型 作物信息 多模态参数融合 机器学习 |
| 收稿时间： | 2023/5/26 0:00:00 |
Estimation of Wheat Yield in Wheat-Maize Rotation Based on Multi-temporal and Multi-parameter Fusion |
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| LI Yang,YUAN Yanwei,ZHAO Bo,WANG Jizhong,WEI Liguo,DONG Xin.Estimation of Wheat Yield in Wheat-Maize Rotation Based on Multi-temporal and Multi-parameter Fusion[J].Transactions of the Chinese Society of Agricultural Machinery,2023,54(12):186-196. |
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| Authors: | LI Yang YUAN Yanwei ZHAO Bo WANG Jizhong WEI Liguo DONG Xin |
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| Institution: | Chinese Academy of Agricultural Mechanization Sciences Group Co. |
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| Abstract: | Yield prediction models can be improved by better integration of data and algorithms, and the accuracy of yield prediction can be further improved by incorporating other factors such as those affecting yield into the model. The research situation was addressed that the wheat-maize rotation system lacked the direct incorporation of the previous crop information into the yield prediction and management of the seasonal crop, a multi-temporal and multimodal crop yield prediction model based on GPR was established by using remote sensing information of the growing season and yield information of the previous maize crop, fusing multi-temporal and multimodal data such as remote sensing information of wheat growing season at the jointing stage, filling stage and maturity stage, fertilization information before sowing and soil properties. The results showed that the performance of the yield prediction model based on the multiple growth periods was improved compared with that based on the single growth period, in which the decision coefficient R2 of the yield prediction model was improved by 0.01~0.03. The accuracy of the yield prediction model based on the spectral indexes of wheat growing season at the jointing stage was higher than the accuracy of the yield prediction model based on the spectral indexes of wheat growing season at the filling stage, and the accuracy of the yield prediction model based on the spectral indexes of wheat growing season at the maturity stage was the lowest, and the accuracy of the yield prediction model based on the jointing stage was slightly lower than that of the yield prediction model based on the multiple growth periods, but the accuracy was similar. In the yield prediction models based on the multimodal parameters fusion, the yield prediction models based on two-modal parameters fusion had higher accuracy than the unimodal yield prediction models, except for the yield prediction model constructed by fusing maize information with soil properties. The accuracy of the yield prediction models with four-modal parameters fusion and three-modal parameters fusion was higher than that of the corresponding yield prediction models with low-modal parameters fusion. The GPR model with four-modal parameters fusion had a decision coefficient R2 of 0.92 and RMSE of 213.75kg/hm2, which improved R2 by 0.02 to 0.41 compared with the wheat yield prediction models based on other modalities. For wheat yield prediction models based on multimodal parameters fusion, from large to small, the influence of each modal parameters was as follows: fertilization information, wheat remote sensing information, soil properties information, maize crop information. Maize crop information had the least improvement in the accuracy of the yield prediction models based on the multimodal parameters fusion, which improved R2 by 0.02~0.07. Maize crop information characterized the soil fertility condition of post-harvest to a certain extent, and it was a high spatial resolution supplement to soil properties information, which could further improve the ability to quantify soil fertility, then combined with other parameters, they can improve the accuracy of wheat yield prediction. In conclusion, the research result provided a scientific basis and method for the comprehensive utilization of soil-crop data and the comprehensive management of wheat-maize rotation system. |
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| Keywords: | wheat maize yield prediction model crop information multimodal parameters fusion machine learning |
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