玉米籽粒直收籽粒特征与杂质分类识别方法  

Direct Harvesting of Maize Grains and Methods for Classification and Identification of Impurities

作　　者：吴昆 张敏[1] 王刚[1] 吴俊[1] 陈旭 Wu Kun;Zhang Min;Wang Gang;Wu Jun;Chen Xu(Nanjing Institute of Agricultural Mechanization,Ministry of Agriculture,Nanjing 210014,China)

机构地区：[1]农业部南京农机化研究所,南京210014

出　　处：《农机化研究》2023年第12期198-203,共6页Journal of Agricultural Mechanization Research

基　　金：江苏省农业科技自主创新资金项目(SCX[20]3031);中国农业科学院所级基本科研业务费项目(S202008)。

摘　　要：为提高玉米籽粒直收中的籽粒和杂质快速精准识别,提出了一种基于机器视觉的单层识别逐步分类方法。对采集的单层原始图像进行分水岭分割,依旧黏连的区域经过R+G颜色灰度化处理,构建灰度梯度并标记背景,再进行二次分水岭分割。对提取玉米籽粒和杂质的颜色、形态、纹理等共27个特征信息进行逐步判别分析,得到可区分籽粒和杂质的13个主要特征参数。基于复合特征阈值设定先杂质筛选后对穗心和茎叶类杂质类型的识别,对比分析基于BP神经网络和遗传算法优化的BP神经网络(Gabp神经网络)对玉米籽粒完整性识别,得出采用基于gabp神经网络较BP神经网络准确率提高了1%且实验结果更加稳定,特征优化后的神经网络训练和识别耗时更短。利用逐步分类识别方法对混合直收玉米籽粒进行识别,结果表明:分步识别准确率分别为97.7%、85.63%,完整玉米籽粒、破碎玉米籽粒和杂质的召回率分别为89.31%、71.43%和81.82%。提出了玉米籽粒和杂质逐步分类识别方法,能快速、准确地区别杂质、玉米完整籽粒和破碎籽粒,可为玉米籽粒直收含杂破碎在线监测系统设计提供参考。In order to improve the rapid and accurate recognition of grains and impurities in direct corn harvest,a single-layer recognition stepwise classification method based on machine vision is proposed.Watershed segmentation is performed on the collected single-layer original image,and the area that is still stuck is processed by R+G color grayscale processing to construct a grayscale gradient and mark the background,and then perform a second watershed segmentation.A total of 27 feature information including color,shape,and texture of corn kernels and impurities were extracted,and then stepwise discriminant analysis was performed to obtain 13 main feature parameters that can distinguish between kernels and impurities.Based on the composite feature threshold setting,the first impurity is screened and then the ear core and stem and leaf impurity types are identified.The comparison analysis is based on the BP neural network and the genetic algorithm optimized BP neural network(Gabp neural network)to identify the integrity of the corn kernel.Compared with the bp neural network,the accuracy rate of the gabp-based neural network is increased by 1% and the experimental results are more stable.The neural network training and recognition after feature optimization takes less time.The step-by-step classification and identification method was used to identify the mixed direct-harvest corn kernels.The experiment showed that the step-by-step recognition accuracy rates were 97.7% and 85.63%,and the recall rates of intact corn kernels,broken corn kernels and impurities were 89.31%,71.43% and 81.82%,respectively.The stepwise classification and identification method of corn kernels and impurities proposed in this paper can quickly and accurately distinguish impurities,intact corn kernels and broken kernels,which can provide reference for the design and research of the online monitoring system for direct harvest of corn kernels and broken kernels.

关 键 词：玉米籽粒 特征提取 神经网络 特征阈值 

分 类 号：S225.5[农业科学—农业机械化工程] TP391.41[农业科学—农业工程]