摘要
分蘖期根外追肥是水稻生产的重要田间管理环节,也是水稻生长中的第一个需肥高峰期,追肥效果直接影响分蘖数以及中后期长势。为了探究利用无人机遥感构建施肥量处方图指导农用无人机对分蘖期水稻精准追肥,在保障水稻产量的前提下降低化肥施用量,该研究在水稻分蘖期追肥窗口期,利用无人机遥感诊断与农用无人机精准作业相结合,采用无人机高光谱技术建立水稻分蘖期施肥量处方图,结合农用无人机作业参数对待施肥地块进行栅格划分,确定精准施肥量,并通过农用无人机进行精准施肥。结果表明:利用特征波段选择与特征提取的方式在450~950nm范围内共提取5个水稻高光谱特征变量用于水稻氮素含量的反演;利用粒子群优化的极限学习机(Particle Swarm Optimization-Extreme Learning Machine,PSO-ELM)构建的水稻氮素含量反演模型效果要好于极限学习机(Extreme Learning Machine,ELM)反演效果,模型决定系数为0.838;结合待追肥区域反演氮素含量(N_r),标准田氮素含量(N_(std))、氮肥浓度(p)、水稻地上生物量(B_(std))、水稻覆盖度(C_(std))、化肥利用率(k)及转化率(u)等构建了农用无人机追肥量决策模型,与对照组相比,利用该研究构建的处方图变量施肥方法使氮肥追施量减少27.34%。研究结果可为寒地水稻分蘖期农用无人机精准变量追肥提供数据与模型基础。
The extra-root topdressing of rice at the tillering stage is one of the key steps in the management of rice production;it is also an important stage of fertiliser demand during the entire cycle of rice growth.The efficiency of extra-root topdressing directly affects the number of rice tillers and their growth in the middle and final stages.Due to the rapid advancement of the UAV technology in recent years,agricultural UAV are used for fertiliser spraying in the fields,which not only increased the rice yield but also reduced labour intensity and labour costs to a large extent,and greatly improved the efficiency of rice field management.In order to explore the use of UAV remote sensing to construct prescription maps to guide agricultural UAV to accurately topdressing rice at the tillering stage,realieze the field-scale nutritional diagnosis and UAV precise spraying,optimize fertilizer consumption,and ensure maximum rice yield,in this research,combining UAV remote sensing diagnosis with precision operation of agricultural UAV,UAV hyperspectral technology was used to establish the prescription maps of fertilization amount in rice tillering stage,combined with the operation parameters of agricultural UAV,grid division of fertilizing plots was carried out to determine the amount of precise fertilization,and precision fertilization was carried out by agricultural UAV.The consistent and desired end-member hyperspectral information of the ground features in the rice field were extracted to retrieve the nitrogen content of riceand a rice tillering stage fertilisation prescription map was established based in this,and the fertilization formula map of rice at tillering stage was established.According to the fertilizer amount prescription map,the operation parameters of agricultural UAV were set,and the plots to be fertilized were divided into grids to determine the spraying amount of each grid topdressing operation,and the precision topdressing was carried out by agricultural UAV.Dajiang spirit 4 RTK UAV was used to obtain the orthophoto image of the test fields with spatial information,the actual position of each topdressing grid was determined,and the variable spraying was realized by controlling the working voltage of the liquid medicine pump by PID algorithm.During the spraying process,droplet test cards were arranged on the ground at the same time to calculate the droplet coverage and other parameters such as droplet coverage rate.The results showed that five hyperspectral characteristic variables of rice were extracted in the 450-950 nm band by the method of feature band selection and feature extraction,the effects of rice nitrogen content inversion model constructed by Particle Swarm Optimization Extreme Learning Machine(PSO-ELM)was better than that of Extreme Learning Machine(ELM),and the coefficient of determination was 0.838 and the root mean square error was 0.466.The rice yield of UAV variable topdressing was basically the same as that of traditional topdressing,but the amount of pure nitrogen decreased by 27.34%.The study results can provide data and model basis for the precision variable topdressing of agricultural UAV in the tillering stage of rice in cold regions.
作者
于丰华
曹英丽
许童羽
郭忠辉
王定康
Yu Fenghua;Cao Yingli;Xu Tongyu;Guo Zhonghui;Wang Dingkang(College of Information and Electrical Engineering,Shenyang Agricultural University,Shenyang 110866,China;Liaoning Agricultural Information Engineering Technology Research Center,Shenyang 110866,China)
出处
《农业工程学报》
EI
CAS
CSCD
北大核心
2020年第15期103-110,共8页
Transactions of the Chinese Society of Agricultural Engineering
基金
国家重点研发计划(2016YFD0200600)
辽宁省教育厅科技人才“育苗”项目(LSNQN201903)。
关键词
无人机
遥感
水稻
高光谱
精准施肥
UAV
remote sensing
rice
hyperspectral
precision fertilization
