基于无人机图像纹理和表型参数的夏玉米水分胁迫诊断  

Diagnosis of summer maize water stress based on UAV image texture and phenotypic parameters

作　　者：谢坪良 张智韬 巴亚岚[1,2] 董宁 左西宇 杨宁 陈俊英 程智楷[1,2] 张蓓 杨晓飞 XIE Pingliang;ZHANG Zhitao;BA Yalan;DONG Ning;ZUO Xiyu;YANG Ning;CHEN Junying;CHENG Zhikai;ZHANG Bei;YANG Xiaofei(Key Laboratory of Agricultural Soil and Water Engineering in Arid and Semiarid Areas,Ministry of Education,Northwest A&F University,Yangling 712100,China;College of Water Resources and Architectural Engineering,Northwest A&F University,Yangling 712100,China)

机构地区：[1]西北农林科技大学旱区农业水土工程教育部重点实验室,杨凌712100 [2]西北农林科技大学水利与建筑工程学院,杨凌712100

出　　处：《农业工程学报》2024年第10期136-146,共11页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家自然科学基金项目(52179044、52279047);国家重点研发计划项目(2022YFD1900404)。

摘　　要：农田水分胁迫是影响作物生长发育和产量品质的重要原因。及时准确地诊断作物水分胁迫状况,对于实现精准灌溉、提高作物抗逆性和产量等具有重要意义。为优化夏玉米水分胁迫诊断方法和提高诊断精度,该研究以夏玉米为对象,利用无人机搭载六通道多光谱传感器获取2022年夏玉米拔节期和抽雄期的遥感影像数据,且同步采集夏玉米气孔导度和表型参数数据,监督分类剔除冗余背景后使用灰度共生矩阵计算得到冠层植被指数和图像纹理信息,通过贝叶斯信息准则和全子集筛选法筛选出敏感的植被指数、图像纹理和表型参数及其组合,结合极限学习机、随机森林和反向传播神经网络3种机器学习方法构建夏玉米气孔导度预估模型,并基于最优气孔导度预估模型绘制夏玉米水分胁迫状况反演图。结果表明,多光谱图像的夏玉米冠层反射率与气孔导度呈弱负相关,植被指数和表型参数与气孔导度呈显著正相关,不同波段的图像纹理均与气孔导度有较高的相关性。植被指数用于评估植被整体健康和水分状况,图像纹理用于捕捉作物空间分布、纹理和结构特征,表型参数用于立体反映作物生理和形态信息,它们在诊断作物水分胁迫的机理上具有互补性。基于植被指数、图像纹理和表型参数构建的反向传播神经网络模型是夏玉米水分胁迫诊断的最佳模型(决定系数为0.841,均方根误差为0.043 mol/(m^(2)·s),平均绝对误差为0.034 mol/(m^(2)·s)),并显著改善了对气孔导度较低值的低估情况。绘制的夏玉米水分胁迫状况反演图呈现出广泛的应用潜力,能够便捷准确地诊断作物水分胁迫状况,以优化灌溉策略,调整资源分配。研究结果可为夏玉米的水分胁迫诊断提供一种可行而准确的方法。Water stress has been one of the most serious threat to the crop growth,development and yield quality in agricultural fields.Timely and accurate diagnosis of crop water stress can greatly contribute to the precision irrigation for the crop resilience and yield.In this study,the research object was taken from the summer maize in the typical dryland agricultural area of northwest China.A six-channel multispectral sensor was mounted on a drone to obtain the remote sensing image data of summer maize at the nodulatione and staminate pulling stage in 2022.At the same time,the stomatal conductance and phenotypic parameters of summer maize were also collected.The background was removed by supervised classification.The canopy vegetation index and image texture were obtained using the gray-scale covariance matrix.The sensitive vegetation index,image texture and phenotypic parameters and their combinations were screened out by the Bayesian information criterion and full subset filtering.The summer maize stomatal conductance estimation model was constructed to combine the three types of machine learnings:the extreme learning machine,the random forest,and the back-propagation neural network.The optimal model was mapped to estimate the stomatal conductance.The Pearson correlation coefficient of vegetation index and stomatal conductance were significantly positively correlated,whereas,the canopy reflectance of summer maize was weakly negatively correlated.Different types of image textures at different wavelengths were correlated with the stomatal conductance,and the highest correlation was found in the 550 nm band.The Pearson correlation coefficients between morphological structure phenotypes(plant height,stem thickness and leaf area)and stomatal conductance of summer maize were 0.72,0.58 and 0.69,respectively,where the three types of phenotypic parameters data were correlated well with stomatal conductance.Vegetation indices with spectral reflectance data were used to assess the overall health and moisture status of the vegeta

关 键 词：无人机 水分胁迫 气孔导度 植被指数 图像纹理 表型参数 

分 类 号：S127[农业科学—农业基础科学] TP79[自动化与计算机技术—检测技术与自动化装置]