基于遗传——蚁群算法的农田微地形特征土方调配路径研究  

Study on earthwork reallocation path for micro⁃topographic features of farmlands based on genetic⁃ant colony algorithm

作　　者：金鑫 李瀚远 杜蒙蒙 姬江涛 Ali Roshanianfard Jin Xin;Li Hanyuan;Du Mengmeng;Ji Jiangtao;Ali Roshanianfard(School of Agricultural Equipment Engineering,Henan University of Science and Technology,Luoyang,471003,China;Department of Agriculture and Natural Resources,University of Mohaghegh Ardabili,Ardabil,566199,Iran)

机构地区：[1]河南科技大学农业装备工程学院,河南洛阳471003 [2]莫哈格达阿德比利大学农业与自然资源系,阿尔达比勒566199

出　　处：《中国农机化学报》2024年第8期270-275,共6页Journal of Chinese Agricultural Mechanization

基　　金：国家重点研发计划(2019YFE0125500);河南省高等学校重点科研计划(20A416001)。

摘　　要：近年来极端天气与自然灾害频发,导致农田损毁,造成农田内部出现微地形特征(凸起特征及洼地特征),影响耕作。针对上述问题,基于高精度农田数字地形模型,通过遗传—蚁群算法提出一种规划农田微地形特征土方调配路径的方法。首先,基于航拍图像获取高精度农田数字地形模型,根据地形因子综合隶属度提取16个凸起特征和9个洼地特征,并分别计算挖填方量为0.885 m^(3)和0.884 m^(3)。其次,以土方量调配成本为决策目标,建立挖、填方区域为路径搜索节点,利用蚁群算法获得初始可行解,通过遗传算法中的适应度函数对解进行初步优化,最后,根据交叉操作和变异操作对解进行二次优化,获得最优土方调配路径。结果表明,该方法经232次迭代获取全局最优解,相较于传统蚁群算法调配成本下降2.1%。为精准平整农田微地形特征作业提供方法支持。In recent years,due to the frequently occurred extreme weather and natural disasters,considerable amount of farmlands have been destructed,resulting in the emergence of micro⁃topographic features(bump features and concave features)within farmlands,thereby affecting farming.In order to solve the above problems,based on high⁃precision farmland digital terrain model and genetic ant colony algorithm,a method of soil allocation path planning for farmland micro⁃terrain features was proposed.Firstly,based on the SfM(Structure from Motion)technology to process aerial images of the test field,a high⁃precision farmland digital terrain model(FDTM)was obtained,and 16 bump features and 9 concave features were extracted according to the comprehensive membership degree of terrain factors,and the cut volume as well as fill volume was calculated as 0.885 m^(3) and 0.884 m^(3),respectively.Secondly,with the cost of earthwork reallocation as the decision target,cutting and filling areas were established as path search nodes,and the ant colony algorithm was used to obtain the initial feasible solutions.Subsequently,these solutions were preliminarily optimized by using the fitness function in the genetic algorithm.Finally,the optimal earthwork reallocation path was obtained by secondary optimization of the solutions according to the crossover and variation operation.The results showed that the global optimal solution was gained after 232 iterations,and the earthwork reallocation cost was reduced by 2.1%compared with the traditional ant colony algorithm.The research results can provide references for precise land leveling with respect to micro⁃topographic features.

关 键 词：农田微地形特征 数字地形模型 土方调配 蚁群算法 遗传算法 

分 类 号：S237[农业科学—农业机械化工程]