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

【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.