基于无人机图像的水稻地上部生物量估算  

Estimation of Aboveground Rice Biomass by Unmanned Aerial Vehicle Imaging

作　　者：舒时富[1] 李艳大[1] 曹中盛 孙滨峰 叶春[1] 吴罗发[1] 朱艳[2] 丁艳锋[2] 何勇[3] SHU Shifu;LI Yanda;CAO Zhongsheng;SUN Binfeng;YE Chun;WU Luofa;ZHU Yan;DING Yanfeng;HE Yong(Institute of Agricultural Engineering,Jiangxi Academy of Agricultural Sciences/Jiangxi Province Engineering Research Center of Intelligent Agricultural Machinery Equipment/Jiangxi Province Engineering Research Center of Information Technology in Agriculture,Nanchang,Jiangxi 330200,China;Nanjing Agricultural University,Nanjing,Jiangsu 210095,China;College of Biosystems Engineering and Food Science,Zhejiang University,Hangzhou,Zhejiang 310029,China)

机构地区：[1]江西省农业科学院农业工程研究所/江西省智能农机装备工程研究中心/江西省农业信息化工程技术研究中心,江西南昌330200 [2]南京农业大学,江苏南京210095 [3]浙江大学生物系统工程与食品科学学院,浙江杭州310029

出　　处：《福建农业学报》2022年第7期824-832,共9页Fujian Journal of Agricultural Sciences

基　　金：江西省重点研发计划项目(20192BBF60052、20212BBF61013、20212BBF63040、20202BBFL63046、20202BBFL63044);国家自然科学基金项目(31460320);国家“万人计划”青年拔尖人才项目(2020-2023);江西省“双千计划”项目(2020-2022)。

摘　　要：【目的】为探究无人机图像估算水稻地上部生物量(Aboveground biomass,AGB)的可行性,明确各图像特征与水稻AGB的定量关系,构建基于图像特征的水稻AGB估算模型。【方法】通过实施2个品种和4个施氮水平的小区试验,于分蘖期、孕穗期和齐穗期测定水稻AGB,同步采用无人机搭载数码相机获取水稻图像并提取颜色指数和纹理特征,分析其在不同生育期与水稻AGB之间的相关性,构建定量估算模型,并对模型进行检验。【结果】颜色指数中红蓝差值(r-b)与水稻AGB之间的相关性最好,纹理特征参数(G-mean)与水稻AGB之间的相关性最高;基于红蓝差值(r-b)和G-mean构建的水稻AGB双指数模型优于单一指数模型,全生育期估算模型y=2544.507+5054.243x_(1)−145.543x_(2)−556.553x_(1)x_(2)+27379.41x_(1)^(2)+3.927x_(2)^(2),建模决定系数(R^(2))为0.9202,模型检验的决定系数(R^(2))为0.9112。【结论】基于颜色指数(r-b)和纹理特征参数(G-mean)融合构建的AGB估算模型可准确的估算水稻AGB,在水稻长势快速无损监测和精确管理中具有应用价值。Objective Feasibility of using images generated by unmanned aerial vehicle(UAV)to estimate the aboveground biomass(AGB)on a rice field was evaluated for crop production prediction.Methods On fields of two different varieties of rice fertilized with 4 varied nitrogen applications,AGB of rice plants at tillering,booting,and full heading stages were recorded by using the UAV imaging technology.Data on color and texture measurements were extracted from the images to correlate with corresponding AGB.A mathematic model was constructed,tested,and validated for prediction accuracy.Result On color,the red and blue differentiation(r-b)of the images highly correlated with the AGB;on texture,it was the G-mean.A prediction model was thus obtained for the entire growth period as y=2544.507+5054.243_(x1)−145.543_(x2)−556.553_(x1x2)+27379.41_(x1)^(2)+3.927_(x2)^(2),which had a correlation coefficient(R^(2))of 0.9202 and a test determination coefficient of 0.9112.Conclusion The prediction model based on r-b and G-mean derived from the UAV images performed satisfactorily in monitoring the AGB for the entire growth period of rice in the field.It was conceivably applicable for the farming operation and crop management.

关 键 词：水稻 地上部生物量 无人机图像 估算模型. 

分 类 号：S511[农业科学—作物学]