IFIP International Federation for Information Processing 2019
Leaf nitrogen concentration (LNC) of winter wheat can reflect its nitrogen (N) status. Rapid, non-destructive and accurate monitoring of LNC of winter wheat has important practical applications in monitoring N nutrition and fertilizing management. The experimental site of winter wheat was located at Xiaotangshan National Demonstration Base of Precision Agricultural Research located in Changping District, Beijing, China. High spatial resolution digital images of the winter wheat were acquired using a low-cost unmanned aerial vehicle (UAV) with digital camera system at three key growth stages of booting, flowering and filling during April to June in 2015. Firstly, the acquired UAV digital images were mosaicked to generate a Digital Orthophoto Map (DOM) of the entire experimental site and 15 digital image variables were constructed. Then, based on the ground measured data onto LNC and digital image variables derived from the DOM for 48 sampling plots of winter wheat, linear and stepwise regression models were constructed for estimating LNC. Finally, the optimum model for estimating LNC was screened out by comprehensively considering the coefficient of determination (R2 ), the root mean square error (RMSE), the normalized root mean square error (nRMSE) and the simplicity of model calibrating and validating. The experimental results showed that the linear regression model of r/b that was one of the digital image variables for estimating LNC had the best accuracy with the model’s calibration and validation of R2 , RMSE and nRMSE were 0.76, 0.40, 11.97% and 0.69, 0.43, 13.02%, respectively. The results suggest that it is feasible to estimate LNC of winter wheat based on the DOM acquired by UAV remote sensing platform carrying a low-cost, high-resolution digital camera, which can rapidly and non-destructively obtains the LNC of winter wheat experiment site and provide a quick and low-cost method for monitoring N nutrition and fertilizing management.
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[学术文献] Examining the social and biophysical determinants of U.S. Midwestern corn farmers’ adoption of precision agriculture 进入全文
Precision agricultural technologies (PA) such as global positioning system tools have been commercially available since the early 1990s and they are widely thought to have environmental and economic beneft; however, adoption studies show uneven adoption among farmers in the U.S. and Europe. This study aims to tackle a lingering puzzle regarding why some farmers adopt precision agriculture as an approach to food production and why others do not. The specifc objective of this study is to examine the social and biophysical determinants of farmers’ adoption of PA. This paper flls a research gap by including measurements of farmer identity—specifcally their own conceptions of their role in the food system—as well as their perceptions of biophysical risks as these relate to the adoption of PA among a large sample of Midwestern U.S. farmers. The study has identifed that farmer identity and perceptions of environmental risk do indeed infuence PA adoption and that these considerations ought to be incorporated into further studies of PA adoption in other jurisdictions. The fndings also appear to highlight the social force of policy and industry eforts to frame PA as not only good for productivity and efciency but also as an ecologically benefcial technology.
为了充分挖掘物联网的潜力,设计了基于LoRa技术的农田物联网系统,该系统可实现田间环境数据的采集,并进行了田间通信试验。选取空地及两块甘蔗地进行试验,分别获取了不同发射功率、不同通信距离、不同天气状况下的共3 000个接收信号强度指示(Received signal strength indication,RSSI)数据。试验结果表明,在蔗田通信中,发射功率15 dB,晴天的通信品质比发射功率10、20 d B及黎明、出现露水等潮湿条件下更稳定,甘蔗密度明显影响通信品质。建立了发射功率15 dB、晴天条件下RSSI值与通信距离的关系模型,其决定系数R~2达到0. 929 1。
冬小麦全蚀病是导致小麦大幅减产甚至绝收的土传检疫性病害。快速、无损地监测冬小麦全蚀病空间分布对其防治具有重要意义。以无人机搭载成像高光谱仪为遥感平台,利用成像高光谱影像结合地面病害调查数据,在田块尺度对冬小麦全蚀病病情指数分布进行空间填图。利用地物光谱仪（ASD）同步获取的高光谱数据评价UHD185光谱数据质量,综合运用统计分析以及遥感反演填图技术,计算光谱指数（Difference spectral index,DSI）、比值光谱指数（Ratio spectral index,RSI）及归一化差值光谱指数（Normalized difference spectral index,NDSI）与病情指数（DI）构建决定系数等势图,筛选最优光谱指数与DI构建线性回归模型,并利用3个光谱指数构建偏最小二乘回归预测模型,以对比模型预测精度与稳健性。最后用独立数据对模型进行检验。结果表明,冬小麦冠层的ASD光谱数据与UHD185光谱数据相关性显著,决定系数R2达0.97以上,3类光谱指数与DI构建偏最小二乘回归模型,得到模型验证结果(R2=0.629 2,RMSE=10.2%,MAE=16.6%),其中DSI(R818,R534)对模型贡献度最高,利用DSI(R818,R534)与DI构建线性回归模型为y=-6.490 1x+1.461 3(R2=0.860 5,RMSE=7.3%,MAE=19.1%),且通过独立样本的模型验证精度(R2=0.76,RMSE=14.9%,MAE=11.7%,n=20)。最后使用该模型对冬小麦进行病情指数反演,制作了冬小麦全蚀病病害空间分布图,本研究结果为无人机高光谱遥感在冬小麦全蚀病的精准监测方面提供了技术支撑,并对未来卫星遥感探索冬小麦全蚀病大面积监测提供了理论基础。
准确、快速地获取关键生育期冬小麦氮素含量,对农业管理者进行田间氮素施肥有重要的决策作用。利用无人机(unmannedaerialvehicle,UAV)搭载数码相机,可以短时间内获取冬小麦长势信息,实现对冬小麦氮素含量动态监测。该研究利用2015年北京市小汤山冬小麦无人机数码影像,采用3种阈值分割方法,将田间植株作物与土壤背景分离。对比影像分割方法的时效性与准确性,最终确定可见光波段差异植被指数VDVI(visible-band difference vegetation index)提取植被信息。按照试验方案要求,在不同的氮肥与水分胁迫管理下,将冬小麦3次重复试验分成48个试验小区,依据小区边界提取小区的红、绿和蓝通道的平均DN(digitalnumber)值,选取25个植被指数,同时与各个试验小区冬小麦不同器官氮含量进行相关性分析,筛选数码影像变量。由于植被指数之间耦合度较高,因此采用主成分分析对原始数据进行成分提取,提取特征向量参与建模,最后利用多元线性回归分析建立氮素反演模型,通过决定系数(R2)、均方根误差(RMSE)和归一化的均方根误差(nRMSE)3个指标筛选出最佳模型,探究各器官氮素含量与数码变量的相关性。结果表明,实验室实测氮素含量与UAV数码影像氮素反演结果及基本一致。在反演模型构建精度方面,3种数据处理结果整体>部分>植被指数,反演效果叶氮>植株氮>茎氮。以冬小麦挑旗期为例,叶片氮含量整体信息提取验证模型的R2、RMSE和nRMSE分别为0.85、0.235和6.10%,比部分信息提取验证模型的R2高0.14,RMSE和nRMSE分别降低0.068和1.77个百分点;比植被指数信息提取验证模型的R2高0.43,RMSE和nRMSE分别降低0.141和3.67个百分点。研究表明,基于UAV数码影像利用多元线性回归构建冬小麦氮素含量反演模型,对试验小区整体提取作物信息的方式反演冬小麦叶氮含量效果最好,相比传统反演方法,模型稳定性更高,可为冬小麦田间水肥决策管理提供参考。