摘要
探究消费级无人机多光谱影像对不同生态点、不同品种水稻氮营养监测建模精确度和普适度的影响,对于实现区域水稻氮营养精确管理与应用有重要意义。该研究分别在云南省西双版纳勐遮镇(供试品种:云粳37)与重庆市北碚区(供试品种:极优6135)2个试验点设置不同氮水平田间试验,利用大疆精灵4多光谱无人机于水稻分蘖期、拔节期和抽穗期采集水稻冠层多光谱图像,采用凯氏定氮法测定水稻植株冠层氮含量(canopy nitrogen content,CNC)并计算地上部氮累积量(plant nitrogen accumulation,PNA);分别利用植被指数、偏最小二乘回归(partial least squares regression,PLSR)、随机森林(random forest,RF)、反向传播神经网络(back-propagation neural network,BPNN)对单一试验点、单品种和不同试验点、多品种水稻建立氮营养监测模型并探究模型的迁移能力。拔节期和抽穗期的模型精度较高(归一化植被指数NDVI或近红外归一化植被指数NNVI,R2为0.68~0.88),而分蘖期的模型精度欠佳(NDVI,R2为0.53~0.79),且模型迁移能力均较差;通过RVI(ratio vegetation index)建立的单品种水稻全生育期地上部氮累积量监测的精度较高且迁移能力较好。基于PLSR、RF和BPNN构建的模型精度高于植被指数模型,其中基于RF的多品种全生育期冠层氮含量和地上部氮累积量监测模型精度最高,R2分别为0.84和0.94,均方根误差分别为0.28%和10.09 kg/hm2。研究结果可为无人机多光谱影像技术对不同生态点、不同品种的水稻全生育期氮营养监测提供理论依据和技术支持。
An accurate and universal monitoring model of nitrogen nutrition can greatly contribute to the precise management and application of regional rice.In this study,a systematic investigation was carried out to explore the impact of the images on the monitoring models.The consumer-grade multispectral images were captured by unmanned aerial vehicles(UAV)from the different ecological sites and rice varieties.A series of field experiments were conducted at two test sites:Mengzhe Town,Xishuangbanna,Yunnan Province(with trial variety Yunjing 37)and Beibei District,Chongqing City(with trial variety Jiyu 6135)in South China,with the varying nitrogen levels.Four multispectral drones(DJI Phantom)were used to capture the multispectral images of the rice canopy during tillering,jointing,and heading stages.The nitrogen content in rice plant canopies(CNC)was measured to calculate the above-ground nitrogen accumulation(PNA)using the Kjeldahl method.The nitrogen nutrition monitoring models were established using vegetation indices,partial least-squares regression(PLSR),random forest(RF),and backpropagation neural network(BPNN)for the single trial site,single variety,different trial sites,and multiple varieties of rice.The transferability was also explored in the models.The monitoring models were established for the CNC and PNA during each growth stage of rice for the single trial site and single variety with the high accuracy(normalized difference vegetation index NDVI or near-infrared normalized vegetation index NNVI,where the coefficient of determination was 0.68-0.88).However,the inaccurate model was obtained with the vegetation index during the tillering stage(NDVI,the coefficient of determination was 0.53-0.79),indicating the low transferability of all models.It was difficult for the vegetation indices to construct the monitoring models for the canopy nitrogen content throughout the entire growth period of rice.But a monitoring model was established for the above-ground nitrogen accumulation of single-variety rice in the perio
作者
凌琪涵
孔发明
宁强
魏勇
柳展
代明珠
周宇
张跃强
石孝均
王洁
LING Qihan;KONG Faming;NING Qiang;WEI Yong;LIU Zhan;DAI Mingzhu;ZHOU Yu;ZHANG Yueqiang;SHI Xiaojun;WANG Jie(College of Resources and Environment,Southwest University,Chongqing 400716,China;Interdisciplinary Research Center for Agricultural Green Development in Yangtze River Basin,Chongqing 400716,China;Menghai Manxiang Yuntian Agricultural Development Co.,LTD.,Menghai 666205,China;National Monitoring Station of Soil Fertility and Fertilizer Efficiency on Purple Soils,Chongqing 400716,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2023年第13期160-170,共11页
Transactions of the Chinese Society of Agricultural Engineering
基金
云南现代农业绿色关键技术创新与平台建设项目(202102AE090053)
国家自然科学基金项目(31801932)。
关键词
无人机
植被指数
机器学习
多光谱成像
氮营养监测
水稻
UAV
vegetation index
machine learning
multi-spectral imaging
nitrogen nutrition monitoring
rice
