摘要
针对传统的表土层土壤容重测量方法费时、耗力的问题,利用易获得的土壤物理参数实现农田大范围表土层土壤容重的快速、准确预测。通过分析表土层土壤容重与土壤表面粗糙度、土壤阻力的关系,构建了以土壤表面粗糙度和土壤阻力为输入的GBRT模型,土壤表面粗糙度利用图像处理技术获得,土壤阻力使用实验室车载式阻力测量系统获得。使用同态滤波技术对土壤表面图像进行预处理,提取图像灰度直方图的熵、平均值、方差、偏度和峰度表征图像的纹理特征参数,提取图像灰度共生矩阵的能量、熵、对比度和逆方差表征图像的区域特征参数。利用灰度关联分析,从9个表征土壤表面粗糙度的特征参数和土壤阻力中选取与表土层土壤容重关联度大于0.65的变量作为模型输入,将得到的GBRT模型预测结果与环刀法得到的结果进行相关性分析,R2达到0.8782,平均绝对误差达到0.021 g/cm^3。同时在相同的输入参数和运算环境下,与BPNN和SVR模型的预测精度和运算速度进行了对比,验证得到GBRT模型具有更高的预测精度和更短的运算时间。本文研究结果为科学指导农田表土层土壤容重的获取提供了思路。
Aiming at the time-consuming and labor-intensive problem of traditional soil bulk density measurement of topsoil,using easily available soil physical parameters to accurately and quickly predict the bulk density of topsoil in farmland.By analyzing the relationship between soil bulk density of topsoil layer and surface roughness and resistance of soil,a gradient boosting regression tree(GBRT)model with input of surface roughness and resistance of soil was constructed.The roughness of soil surface was obtained using image processing techniques.Using homomorphic filtering technology to preprocess the surface image of soil,extract the entropy,average,variance,skewness and kurtosis of the image gray histogram to characterize the texture feature parameters of image,extract the energy,entropy,contrast and inverse variance characterize the regional characteristic parameters of the image.The soil resistance was obtained using a laboratory vehicle-mounted resistance measurement system.Using gray correlation analysis,from nine characteristic parameters that characterizing the roughness of soil surface and soil resistance,the variables with bulk density of topsoil greater than 0.65 were selected as the model input.The prediction results of the GBRT model were the same as those obtained by the ring knife method.As a result of correlation analysis,the determination coefficient R2 reached 0.8782,and the average absolute error reached 0.021 g/cm^3.At the same time,under the same input parameters and computing environment,compared with the prediction accuracy and operation speed of the BPNN and SVR models,it was verified that the GBRT model had better prediction accuracy and shorter operation time.The research results can provide ideas for obtaining the bulk density of topsoil and provide theoretical support for scientific and rapid guidance of farmland.
作者
杨玮
兰红
李民赞
孟超
YANG Wei;LAN Hong;LI Minzan;MENG Chao(Key Laboratory of Modern Precision Agriculture System Integration Research, Ministry of Education, China Agricultural University, Beijing 100083, China)
出处
《农业机械学报》
EI
CAS
CSCD
北大核心
2020年第9期193-200,共8页
Transactions of the Chinese Society for Agricultural Machinery
基金
国家自然科学基金青年基金项目(31801265)。
