用冠层叶色偏态分布模式RGB模型预测大豆产量  

Prediction of soybean yield by using RGB model with skew distribution pattern of canopy leaf color

作　　者：张佩[1,2] 陈郑盟 马顺登 尹帝 江海东[1] Zhang Pei;Chen Zhengmeng;Ma Shundeng;Yin Di;Jiang Haidong(Key Laboratory of Crop Physiology and Ecology in Southern China,Ministry of Agriculture,Jiangsu Collaborative Innovation Center for Modern Crop Production,National Engineering and Technology Center for Information Agriculture,Nanjing Agricultural University,Nanjing 210095,China;Jiangsu Meteorological Bureau,Nanjing 210008,China;Longyan Company of Fujian Provincial Tobacco Corporation,Longyan 364000,China)

机构地区：[1]南京农业大学农业部作物生理生态与生产管理重点实验室、江苏省现代作物生产协同创新中心、国家信息农业工程技术中心,南京210095 [2]江苏省气象局,南京210008 [3]福建省烟草公司龙岩市公司,龙岩364000

出　　处：《农业工程学报》2021年第9期120-126,共7页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家重点研发计划课题(2018YFD1000900);江苏省“333工程”高层次人才培养科研项目和江苏省气象局科技项目(KM201905)。

摘　　要：为了探索加色混色(Red-Green-Bule,RGB)模型偏态分布模式在大豆产量预测上的可行性,并验证其在不同肥料运筹、不同品种上的通用性,该研究选用曲茎和徐豆18两个大豆品种,设计了不同种植密度和氮肥水平的大田裂区试验,以无人机搭载数码摄像机,在花期及以后的2个重要生殖生长期采集大豆冠层数据。研究证实了大豆冠层数码图像的光学三原色RGB模型色阶遵循偏态分布,并利用偏态分析得到5类共20个叶色偏态参数。花期、荚期和鼓粒期的冠层叶色偏态参数普遍存在明显差异,其中表征叶色深浅的均值、中位数、众数从花期至鼓粒期呈现先降后升的变化趋势,而表征叶色偏向性的偏度和表征叶色集中度的峰度则普遍呈现相反的变化趋势。基于偏态参数构建的大豆产量预测模型的预测准确度平均达91.30%(建模组),对氮肥运筹验证组的预测准确度平均为87.33%,对不同品种验证组的预估准确度虽然低于建模组和氮肥运筹验证组,但也接近80%。这说明RGB模型偏态参数可准确地描述不同生育期大豆冠层叶色状况,基于偏态参数构建的产量预测模型有了更多的冠层颜色信息输入,对选用同品种但采用不同氮肥运筹措施和选用不同品种下的产量预测准确率均较高,可广泛用于不同生产条件的大豆产量预测,具有较好的适用性与较高的推广价值。With the increasing maturity of digital imaging technology and the increasing popularity of high resolution camera equipment,the advantages of high resolution and low cost have prompted the use of digital imaging technology to conduct more qualitative and quantitative descriptions of phenotypic traits for plant appearance.The RGB model is the most commonly used color representation for digital images.In order to explore the feasibility of using color gradation distribution parameters of the RGB model in soybean yield prediction,and to verify the universality of the method in different fertilizer operations and varieties,two soybean varieties,Qujing and Xudou 18,were selected to design field fissure experiments with different densities and nitrogen fertilizer levels in this study.Digital cameras were carried by Unmanned Aerial Vehicle(UAV)to collect soybean canopy digital images during three important reproductive growth stages.The results showed that the cumulative distribution of canopy color gradation of soybean at the florescence,pod-setting and grain-filling stages,all conformed to the skewed distribution,and a total of 20 Color Gradation Skewness-Distribution(CGSD)parameters were obtained by skew analysis.These parameters simultaneously described the shade and distribution of the canopy leaf color.The 20 CGSD parameters were significantly different among the florescence,pod-setting and grain-filling stages.And the variation trend of color depth parameters(mean,median,and mode)was opposite to that of the distribution parameters(skewness and kurtosis).The prediction model of soybean yield by using prediction model multiple stepwise regression method was constructed based on CGSD parameters with P value of 0.012.The model had high estimation accuracy in both the modeling group and the verification groups.The prediction accuracy of the model in modeling group reached 91.30%on average;the average prediction accuracy of 18 plots in the nitrogen operation research validation group was 87.33%.Although the prediction

关 键 词：UVA 氮 图像分析 大豆 RGB图像 偏态参数 产量预估 

分 类 号：S565.1[农业科学—作物学]