The accurate prediction of soybean yield is of great significance for agricultural production, monitoring and early warning.Although previous studies have used machine learning algorithms to predict soybean yield base...The accurate prediction of soybean yield is of great significance for agricultural production, monitoring and early warning.Although previous studies have used machine learning algorithms to predict soybean yield based on meteorological data,it is not clear how different models can be used to effectively separate soybean meteorological yield from soybean yield in various regions. In addition, comprehensively integrating the advantages of various machine learning algorithms to improve the prediction accuracy through ensemble learning algorithms has not been studied in depth. This study used and analyzed various daily meteorological data and soybean yield data from 173 county-level administrative regions and meteorological stations in two principal soybean planting areas in China(Northeast China and the Huang–Huai region), covering 34 years.Three effective machine learning algorithms(K-nearest neighbor, random forest, and support vector regression) were adopted as the base-models to establish a high-precision and highly-reliable soybean meteorological yield prediction model based on the stacking ensemble learning framework. The model's generalizability was further improved through 5-fold crossvalidation, and the model was optimized by principal component analysis and hyperparametric optimization. The accuracy of the model was evaluated by using the five-year sliding prediction and four regression indicators of the 173 counties, which showed that the stacking model has higher accuracy and stronger robustness. The 5-year sliding estimations of soybean yield based on the stacking model in 173 counties showed that the prediction effect can reflect the spatiotemporal distribution of soybean yield in detail, and the mean absolute percentage error(MAPE) was less than 5%. The stacking prediction model of soybean meteorological yield provides a new approach for accurately predicting soybean yield.展开更多
本文报道了1280×1024元InAs/GaSb II类超晶格中/中波双色红外焦平面阵列探测器的研究结果。探测器采用PN-NP叠层双色外延结构,信号提取采用叠层双色结构和顺序读出方式。运用分子束外延技术在GaSb衬底上生长超晶格材料,双波段红外...本文报道了1280×1024元InAs/GaSb II类超晶格中/中波双色红外焦平面阵列探测器的研究结果。探测器采用PN-NP叠层双色外延结构,信号提取采用叠层双色结构和顺序读出方式。运用分子束外延技术在GaSb衬底上生长超晶格材料,双波段红外吸收区的超晶格周期结构分别为中波1:6 ML InAs/7 ML GaSb和中波2:9 ML InAs/7 ML GaSb。焦平面阵列像元中心距为12μm。在80 K时测试,器件双波段的工作谱段为中波1:3~4μm,中波2:3.8~5.2μm。中波1器件平均峰值探测率达到6.32×10^(11) cm·Hz^(1/2)W^(-1),中波2器件平均峰值探测率达到2.84×10^(11) cm·Hz^(1/2)W^(-1)。红外焦平面偏压调节成像测试得到清晰的双波段成像。本文是国内首次报道1280×1024规模InAs/GaSb II类超晶格中/中波双色红外焦平面探测器。展开更多
为了准确评价川产道地药材羌活栽培区耕作层土壤质量状况,分别采用聚类分析法(CA)和主成分分析法(PCA)构建栽培区耕作层土壤质量最小数据集(minimum data set,MDS),利用最小数据集土壤质量指数(soil quality index-CA,SQI-CA和SQI-PCA)...为了准确评价川产道地药材羌活栽培区耕作层土壤质量状况,分别采用聚类分析法(CA)和主成分分析法(PCA)构建栽培区耕作层土壤质量最小数据集(minimum data set,MDS),利用最小数据集土壤质量指数(soil quality index-CA,SQI-CA和SQI-PCA)和全量数据集土壤质量指数(SQI-T)评价川西北羌活栽培区耕作层土壤质量。结果表明:(1)羌活栽培区土壤有机质含量为(19.14±6.75)g·kg^(−1),处于中度贫瘠化水平;土壤速效氮、速效磷和速效钾含量较高,分别为(129.78±47.78)mg·kg^(−1)、(22.89±14.78)g·kg^(−1)和(159.87±97.87)mg·kg^(−1);土壤为中性土壤,pH均值为7.20±1.68。(2)基于不同数据集的土壤质量指数均值排序为SQI-T>SQI-PCA>SQI-CA,而SQI-PCA与SQI-T之间的Nash有效系数高于SQI-CA,相对偏差系数低于SQI-CA,说明基于主成分分析的最小数据集(MDS-PCA)评价效果更优,该数据集包括土壤容重、抗剪强度、有机质含量、饱和导水率、黏粒含量、pH、速效氮和砂粒含量共8个指标。(3)川西北羌活栽培区土壤质量指数SQI-PCA<0.33,表明该研究区耕作层土壤质量总体水平较差,主要体现在土壤紧实、有机质含量低,需要通过合理耕作、施肥和土壤改良等方式对耕作层土壤质量进行有效调控。研究结果可为川西北高原羌活栽培区土壤质量改良和生产适宜性调控提供参考,有利于川西北高原区中药材产区土壤可持续利用。展开更多
基金supported by the Science and Technology Innovation Project of Chinese Academy of Agricultural Sciences (CAAS-ASTIP-2016-AII)。
文摘The accurate prediction of soybean yield is of great significance for agricultural production, monitoring and early warning.Although previous studies have used machine learning algorithms to predict soybean yield based on meteorological data,it is not clear how different models can be used to effectively separate soybean meteorological yield from soybean yield in various regions. In addition, comprehensively integrating the advantages of various machine learning algorithms to improve the prediction accuracy through ensemble learning algorithms has not been studied in depth. This study used and analyzed various daily meteorological data and soybean yield data from 173 county-level administrative regions and meteorological stations in two principal soybean planting areas in China(Northeast China and the Huang–Huai region), covering 34 years.Three effective machine learning algorithms(K-nearest neighbor, random forest, and support vector regression) were adopted as the base-models to establish a high-precision and highly-reliable soybean meteorological yield prediction model based on the stacking ensemble learning framework. The model's generalizability was further improved through 5-fold crossvalidation, and the model was optimized by principal component analysis and hyperparametric optimization. The accuracy of the model was evaluated by using the five-year sliding prediction and four regression indicators of the 173 counties, which showed that the stacking model has higher accuracy and stronger robustness. The 5-year sliding estimations of soybean yield based on the stacking model in 173 counties showed that the prediction effect can reflect the spatiotemporal distribution of soybean yield in detail, and the mean absolute percentage error(MAPE) was less than 5%. The stacking prediction model of soybean meteorological yield provides a new approach for accurately predicting soybean yield.
文摘本文报道了1280×1024元InAs/GaSb II类超晶格中/中波双色红外焦平面阵列探测器的研究结果。探测器采用PN-NP叠层双色外延结构,信号提取采用叠层双色结构和顺序读出方式。运用分子束外延技术在GaSb衬底上生长超晶格材料,双波段红外吸收区的超晶格周期结构分别为中波1:6 ML InAs/7 ML GaSb和中波2:9 ML InAs/7 ML GaSb。焦平面阵列像元中心距为12μm。在80 K时测试,器件双波段的工作谱段为中波1:3~4μm,中波2:3.8~5.2μm。中波1器件平均峰值探测率达到6.32×10^(11) cm·Hz^(1/2)W^(-1),中波2器件平均峰值探测率达到2.84×10^(11) cm·Hz^(1/2)W^(-1)。红外焦平面偏压调节成像测试得到清晰的双波段成像。本文是国内首次报道1280×1024规模InAs/GaSb II类超晶格中/中波双色红外焦平面探测器。
文摘为了准确评价川产道地药材羌活栽培区耕作层土壤质量状况,分别采用聚类分析法(CA)和主成分分析法(PCA)构建栽培区耕作层土壤质量最小数据集(minimum data set,MDS),利用最小数据集土壤质量指数(soil quality index-CA,SQI-CA和SQI-PCA)和全量数据集土壤质量指数(SQI-T)评价川西北羌活栽培区耕作层土壤质量。结果表明:(1)羌活栽培区土壤有机质含量为(19.14±6.75)g·kg^(−1),处于中度贫瘠化水平;土壤速效氮、速效磷和速效钾含量较高,分别为(129.78±47.78)mg·kg^(−1)、(22.89±14.78)g·kg^(−1)和(159.87±97.87)mg·kg^(−1);土壤为中性土壤,pH均值为7.20±1.68。(2)基于不同数据集的土壤质量指数均值排序为SQI-T>SQI-PCA>SQI-CA,而SQI-PCA与SQI-T之间的Nash有效系数高于SQI-CA,相对偏差系数低于SQI-CA,说明基于主成分分析的最小数据集(MDS-PCA)评价效果更优,该数据集包括土壤容重、抗剪强度、有机质含量、饱和导水率、黏粒含量、pH、速效氮和砂粒含量共8个指标。(3)川西北羌活栽培区土壤质量指数SQI-PCA<0.33,表明该研究区耕作层土壤质量总体水平较差,主要体现在土壤紧实、有机质含量低,需要通过合理耕作、施肥和土壤改良等方式对耕作层土壤质量进行有效调控。研究结果可为川西北高原羌活栽培区土壤质量改良和生产适宜性调控提供参考,有利于川西北高原区中药材产区土壤可持续利用。
