机采籽棉收购环节含杂率快速检测系统研制  

Rapid measurement system for the impurity rate of machine-picked seed cotton in acquisition

作　　者：万龙 庞宇杰 张若宇[1,2] 江英兰 张梦芸[1,2] 宋方丹 常金强 夏彬 Wan Long;Pang Yujie;Zhang Ruoyu;Jiang Yinglan;Zhang Mengyun;Song Fangdan;Chang Jinqiang;Xia Bin(College of Mechanical and Electrical Engineering,Shihezi University,Shihezi 832003,China;Key Laboratory of Northwest Agricultural Equipment,Ministry of Agriculture and Rural Affairs,Shihezi 832003,China;Zhengzhou Cotton&Jute Engineering Technology and Design Research Institute of China CO-OP,Zhengzhou 450004,China)

机构地区：[1]石河子大学机械电气工程学院,石河子832003 [2]农业农村部西北农业装备重点实验室,石河子832003 [3]中华全国供销合作总社郑州棉麻工程技术设计研究所,郑州450004

出　　处：《农业工程学报》2021年第6期182-189,共8页Transactions of the Chinese Society of Agricultural Engineering

基　　金：国家重点研发计划(2018YFD0700403);兵团重点领域科技攻关计划(2020AB006)。

摘　　要：籽棉收购过程中含杂率检测工序繁杂、劳动强度大、效率低,不利于籽棉的快速检测分级,严重影响籽棉收购效率。该研究开发了一种适于收购环节的机采籽棉含杂率快速检测系统。系统由驱动传输单元、压棉单元、传感单元、机器视觉系统、PLC控制系统组成。首先利用大杂清理机清除籽棉中的棉杆和铃壳等大密度杂质(大杂),对去大杂后的籽棉进行称量后送至机器视觉系统,采用RGB双面成像方法获取籽棉样本图像,分析计算图像中的杂质面积,预测去除大杂的籽棉含杂率和小杂质量,最后结合计算的大杂质量预测籽棉样本总含杂率。其中,RGB图像处理中使用同态滤波、主成分分析(Principal Component Analysis,PCA)变换和局部自适应阈值方法提升图像的可分割性;比较了线性回归(Linear Regression,LR)和支持向量机回归(Support Vector Regression,SVR)2种回归模型的准确率,确定较优的回归模型为LR,总含杂率决定系数R^(2)为0.95,均方根误差RMSE为0.58%,最后利用100个籽棉样品对系统性能进行验证,实测值与预测值之间平均绝对误差为0.36个百分点,单个样本含杂率检测程序处理时间为48.38 s。结果表明该系统具有较高的预测准确率和效率。Impurity separation test is widely used to detect the impurity rate of seed cotton in purchasing,due mainly to the fact that some foreign matter can be picked manually.However,the average consuming time for a cotton sample test is 20-30 minutes.A new testing procedure is,therefore,necessary to rapidly detect the impurities rate,and thereby to classify the seed cotton for high production efficiency.In this study,an intelligent measurement system was developed using machine vision to detect the impurity rate of machine-picked seed cotton in acquisition.The whole system consisted of a drive transmission unit,a cotton pressing unit,a sensor unit,a machine vision system,and a control system.A cleaning machine for large impurities was utilized to mechanically remove the cotton stems and hulls from the 500 g sample of seed cotton.The cotton sample without large impurities was weighed,and then transported into the machine vision system.The surface of seed cotton was automatically compacted by the pressing unit,aiming to reduce the influences of uneven brightness and shadows in subsequent image acquisition.The RGB double-sided imaging was selected to acquire the image of seed cotton.The homomorphic filtering and Principal Component Analysis(PCA)were selected to preprocess the collected RGB images.A local adaptive threshold was then utilized to segment the preprocessed images into the impurities and cotton.After that,the segmented regions of impurities were calculated to predict the weight of small impurities.The total weight of impurities was equal to the predicted value of small impurities and the measured value of large impurities.The rate of small impurities was the ratio of predicted value to the sample weight without large impurities.The final impurity rate was achieved for the total weight of impurities in the total 500 g sample of seed cotton.Linear Regression(LR)and support vector regression(SVR)models were used to compare the predicted accuracy.The LR achieved a better performance,where the determination coeffici

关 键 词：机器视觉 籽棉 含杂率 检测系统 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]