Experimental Study on Working Performance of Rice Rope Direct Seeding Machine

Structure and sowing principles of rice rope direct seeding machine are introduced. In order to test the machine＇ s working performance, such as compacting effect, sowing depth, influence of sowing device to rice rope, etc., field experiments were conducted. It is concluded that mean slip ratio of compacting wheel 1 is 4.44%, wheel 2 is 5.58%, wheel 3 is 7.81%, and wheel 4 is 6.96%; mean depth of planting is 29.72 mm, and mean variability coefficient of planting depth is 6.39%. Maximum variability coefficient of planting depth is 8.40%. Rice rope＇s snapping is closely related with the machine＇s speed and guide thread wheel by sowing device orthogonal experiments. Test results show that the machine has a rational design, safe work and meets to the requirements of planting. This study has laid the foundation for further studying the project.

Selection for high quality pepper seeds by machine vision and classifiers

This research aimed to improve selection of pepper seeds for separating high-quality seeds from low-quality seeds. Past research has shown that seed vigor is significantly related to the seed color and size, thus several physical features were identified as candidate predictors of high seed quality. Image recognition software was used to automate recognition of seed feature quality using 400 kernels of pepper cultivar 101. In addition, binary logistic regression and a neural network were applied to determine models with high predictive value of seed germination. Single-kernel germination tests were conducted to validate the predictive value of the identified features. The best predictors of seed vigor were determined by the highest correlation observed between the physical features and the subsequent fresh weight of seedlings that germinated from the 400 seeds. Correlation analysis showed that fresh weight was significantly positively correlated with eight physical features： three color features （R, a＊, brightness）, width, length, projected area, and single-kernel density, and weight. In contrast, fresh weight significantly negatively correlated with the feature of hue. In analyses of two of the highest correlating single features,＇ germination percentage increased from 59.3 to 71.8% when a＊〉3, and selection rate peaked at 57.8%. Germination percentage increased from 59.3 to 79.4%, and the selection rate reached 76.8%, when single-kernel weight 〉0.0064 g. The most effective model was based on a multilayer perceptron （MLP） neural network, consisting of 15 physical traits as variables, and a stability calculated as 99.4%. Germination percentage in a calibration set of seeds was 79.1% and the selection rate was 90.0%. These results indicated that the model was effective in predicting seed germination based on physical features and could be used as a guide for quality control in seed selection. Automated systems based on machine vision and model classifiers can contribute to reducing the costs and labor required in the selection of pepper seeds.

Machine vision inspection of rice seed based on Hough transform

A machine vision system was developed to inspect the quality of rice seeds. Five varieties of Jinyou402, Shanyou10, Zhongyou207, Jiayou and IIyou were evaluated. The images of both sides of rice seed with black background and white background were acquired with the image processing system for identifying external features of rice seeds. Five image sets consisting of 600 original images each were obtained. Then a digital image processing algorithm based on Hough transform was developed to inspect the rice seeds with incompletely closed glumes. The algorithm was implemented with all image sets using a Matlab 6.5 procedure. The results showed that the algorithm achieved an average accuracy of 96% for normal seeds, 92% for seeds with fine fissure and 87% for seeds with incompletely closed glumes. The algorithm was proved to be applicable to different seed varieties and insensitive to the color of the background.

Exploring the Efficiency of Experimental Construction of Sorting Ginned Cotton Seed Machine

In this paper, research has been conducted to increase the quantity of fiber produced in the enterprise by creating a sorting device for spun seeds, dividing them into fractions by geometric dimensions, and by re-ginning, separating those with long fibers. A new model was developed for geometric sorting of cotton seeds in the harvest, and experiments determined its effectiveness and the optimal values of the factors affecting the efficiency using mathematical modeling. Based on the results of the study, graphs of the influence of factors on device performance and on device efficiency were constructed.

The Development and Application of 2BDQ-8 Rice Direct Sowing Machine

[Objective] The aim was to introduce the development and application of 2BDQ-8 rice direct sowing machine and provide a theoretical basis for rice mechanization production. [Method] 2BDQ-8 rice direct sowing machine was used for the promotion test in field of several cities and counties in Jiangsu Province,and artificial rice planting and mechanization rice planting were compared to explore the production and economic situation. [Result] 2BDQ-8 rice direct sowing machine had advantages such as high efficiency and low cost,the rice direct sowing machine saved about 30% compared to the artificial rice planting and mechanization rice planting,and the overall efficiency was significant. [Conclusion] 2BDQ-8 rice sowing machine was a production technology that had low cost and high efficiency,which should be widely applied.

基于四行精密播种的玉米直播机设计与试验

为了解决播种玉米种子精度低的问题,设计了一款基于四行精密播种的玉米直播机,可实现玉米种子快速、精准播种。试验表明:该玉米直播机移动性能好、播种速度快、种植深度和每穴种子数量更加准确,封土情况也大幅改善,符合设计需求,具有较高的可靠性和可行性。

轻简化栽培稻田杂草发生趋势与防控技术

随着水稻轻简化栽培技术的应用与推广,东北稻区、西北稻区、长江流域稻区和华南稻区等水稻主产区的杂草种群演替加快,稗草、千金子和马唐等恶性杂草频繁发生,杂草抗药性水平上升迅速。若杂草防控不及时、不彻底,极易对水稻生产构成严重威胁。为此,对水稻直播、机插和抛秧等轻简化栽培稻田杂草的草相特征、发展趋势和防除方法等进行了概述,以期为制定合理、高效的轻简化栽培稻田杂草综合防控技术、水稻用农药减量增效除草方案,延缓杂草抗药性产生与发展提供借鉴。

水稻种膜覆膜直播机的设计与试验

水稻机械化直播是一种轻简、高效、可持续发展的栽培方式,水稻精量穴直播技术有效破解了水稻传统直播中的多项技术难题,为水稻机械化生产提供了一种先进的轻简化栽培技术,但现有的水稻精量穴直播技术播种效果不理想,影响该项技术的推广应用。为此,设计了一种水稻种膜覆膜直播机。直播机采用水稻种膜覆膜,将事先按农艺要求设定株距和穴距、粘附水稻种籽粒的可降解地膜铺设于整理好的土壤上,省去了复杂的播种装置,解决了机械式排种器在播种作业过程中对水稻种籽粒的机械损伤问题。田间试验表明:该机采光面度合格率82%,采光面展平度96%,采光面机械破损程度31%,地膜纵向拉伸率7%,膜边覆士宽度合格率91%,膜边覆土厚度合格率92%,地膜漏覆土程度0.8%,地膜折边宽度合格率91%,地膜贴合程度96%,铺膜中心线偏差18mm,生产率0.38hm^(2)/h,各项测试指标均能满足相应的国家标准及设计要求。

蒲公英种子与绒毛脱分机设计与试验

针对蒲公英的种子与绒毛分离困难,分离过程中劳动量大、人工成本高等问题,设计两级脱分的蒲公英种子与绒毛脱分机。整机结构由一级脱分装置、二级脱分装置、负压除杂装置、传动装置以及进料口组成。对一级脱分和二级脱分的筛网进行结构设计和计算,得出设计参数:一级锥形筛网高度为277 mm,母线长为320 mm,顶圈筛孔个数为87个,底圈筛孔的个数为185个,二级冲孔筛的直径为520 mm,筛孔数为6928个。以喂入量和毛刷转速为试验因素,以蒲公英种子的脱净率、破损率为试验指标进行试验,得到脱分机的最佳工作状态:当毛刷转速为320 r/min、喂入量为0.0037 kg/s时,脱净率为87.6%,破损率为2.8%。蒲公英种子与绒毛脱分机整体结构合理、运行状况稳定,满足设计要求。

基于Seeded-Kmeans和SVM的分类算法

支持向量机(support vector machines,SVM)在人像识别、文本分类等模式识别问题中有广泛的应用,可以有效地解决一些实际生活中的分类问题。针对半监督两分类问题,提出了基于Seeded-Kmeans和SVM的分类算法(SK-SVM)。用SeededKmeans算法对无标签点进行处理,使其获得初始标签,再选取有效的标签点加入已有带标签点中,构成新的带标签训练集,最后结合SVM进行分类。选取UCI中的8个数据集进行数值实验,基于Seeded-Kmeans和SVM的分类算法的有效性得到了验证。

基于改进YOLO v5n的工厂化育秧田间铺盘装置设计与试验

针对目前工厂化育秧育苗田间铺盘自动化程度低、成本高等问题,设计了一种全自动双边轨道式田间铺盘装置,并配备苗床异常凸起视觉检测模块。首先对铺盘结构工作原理进行分析,之后对铺盘装置满载作业状况进行结构设计、受力分析和仿真分析。为了防止苗床异常凸起导致铺盘时秧盘倾斜,影响炼苗成活率,提出了一种基于CBAM-YOLO v5n的苗床异常凸起目标识别算法,改进后的YOLO v5n算法添加了注意力机制,对苗床异常凸起目标检测准确率、召回率和平均精度均值分别为98.1%、91.7%和94.9%,相对于原模型分别提高1.2、1.7、0.9个百分点。对设计的铺盘样机进行了正交试验,试验结果表明,当铺盘高度为90 mm、铺盘机构转速为550 r/min、铺盘箱平移速度为0.14 m/s时,铺盘成功率最高为96.4%,植入机器视觉模块后,铺盘成功率可达99.3%。设计的铺盘装置可有效降低人工铺盘劳动强度,降低铺盘劳动成本。

葵花脱籽机关键部件有限元分析

机架作为葵花脱籽机关键部件,其结构设计合理与否直接关系着整机的安全性和可靠性。该文基于三维设计、分析软件对葵花脱籽机机架进行静力学分析和模态分析,通过SolidWorks 2019软件构建机架三维模型,借助Simulation插件对机架进行静力学分析,通过分析得出机架最大应力和应变均符合设计要求,机架最大应变1.96 mm。其次,借助ANSYS Workbench 2021对机架进行12阶自由模态分析,通过分析得出机架固有频率为67.03 Hz,机架固有频率大于电机运行频率50 Hz,整机运行时机架和电机不会发生共振,所研究内容可为葵花脱籽机结构优化提供参考。

菠菜播种收割一体机结构设计与有限元分析

针对现有菠菜收割机留根、铲根深浅不一以及播种机撒播效率低、线绳播种成本高等问题,通过理论计算和SolidWorks建模分析,设计了一种犁地深度可调、整株收割、定量等距播种的菠菜播种收割一体机。采用Simulation模块对优化的机架和犁进行有限元分析发现,机架的大部分最大应力点位于机架前端的输料口处,在制造时需提高此处的焊接质量;犁须选用耐腐蚀性好的材料以减少其在腐蚀性土壤环境中的损坏。通过综合比较和有限元分析发现,机架所受最大应力均小于玻璃钢屈服极限,若通过合理成型设计弥补玻璃钢的成形性差等问题,有望使其成为未来农机具中犁制造的优选材料。研究结果可为菠菜播种收割一体机设计和关键部件优化提供一定理论参考。

我国蔬菜精量直播装备技术现状与发展趋势

蔬菜生长周期短,经济效益高,蔬菜产业已成为我国农业发展的重要支撑,但因其种子粒径较小、形状不规则、机械播种难度大等特点,导致目前蔬菜播种机械化程度不高,生产效率较低,严重制约蔬菜产业的进一步发展。以部分典型蔬菜播种机为例,阐述国内外蔬菜播种机的研究现状及先进蔬菜播种技术,指出目前我国蔬菜直播机存在排种器通用性较差、播种效率和精度较低、电气智能化程度不高等问题,蔬菜播种机研发仍处于初级阶段。认为我国未来蔬菜直播机的发展方向将以大型气力式和小型清洁能源的机械式为主,并建议制定农艺标准及规范,提高播种机的通用性、加快新技术应用,为精量蔬菜播种机的研制提供参考。

气力集排式水稻直播机动力底盘设计与试验

针对水稻机械化直播田间作业环境非结构化、作业对象生物性和作业功能复杂的现状,设计了一种仿形防滑移复式多功能的气力集排式水稻直播机动力底盘,其包括多功能机架、镇压筒和开沟器等零部件。通过对关键部件机架的静力学、动力学结构仿真分析和重心分析,采用6阶模态振型有限元分析计算了动力底盘与牵引拖拉机的共振频率,验证了设计的合理性。田间试验结果表明,直播机行间播种变异系数为4.27%~6.16%,实现了开沟覆土镇压仿形一体作业,底盘地轮滑移率为7.68%,可满足气力集排式水稻直播机工作的稳定和效率要求。

种芽生物营养强化机的设计与试验

针对营养功能性农产品供给不足和生物营养强化技术装备落后等问题,设计了种芽生物营养强化机。采取提高组分浓度差、压力差,以及透水交变真空等快速催芽技术,解决营养组分向种芽内高效传质问题。经绿豆芽富叶酸营养强化试验和发芽糙米富钙营养强化试验研究,结果表明:种芽生物营养强化机可生产高营养价值农产品,其生产的绿豆芽中叶酸含量可达1.486 mg/kg,发芽糙米中钙含量可达757 mg/kg,营养强化效果良好。

基于机器视觉的胡麻种子自动化考种方法

[目的/意义]胡麻种子的周长、面积、长短轴和千粒重是胡麻考种过程中常用的参数,对于胡麻的育种、栽培,以及种子品质和性状的评估都具有重要的意义。[方法]针对胡麻种子自动化考种时出现的数据统计错误率高、效率低等问题,基于机器视觉研究胡麻种子的轮廓特点、探索形态特征的测量方法,针对籽粒重叠现象提出基于融合角点特征的轮廓拟合图像分割方法,设计胡麻种子自动化考种数据实时分析系统,最终实现胡麻种子自动化考种的研究。本研究在工业相机获取的胡麻种子图像上进行试验。[结果和讨论]提出的自动化考种方法对不同品种胡麻种子的统计识别准确率达97.28%,百粒种子平均处理时长69.58 ms,相较于极限腐蚀算法、基于距离变换的分水岭算法,平均计算准确率比极限腐蚀算法提升19.6%,平均运算时间低于直接使用分水岭算法所需时间。[结论]自动化考种方法具有更好的计算准确率和处理速度,能够更准确地批量获取胡麻种子的形态学特征参数,使测量误差能够保持在10%以内,可为今后胡麻考种相关工作提供技术支撑,助力相关产业发展。

包衣柠条种子气力喷播机供种装置仿真与试验

为解决目前喷播机供种装置不适应包衣种子的问题,结合物理与仿真试验的方法,以喷播包衣柠条种子为对象,以降低排种破碎率、提高排种均匀性为目标,设计了一种4BQD-40型喷播机螺旋式供种装置。首先,根据喷播机已确定的电机参数、最大喷播量和种子休止角度等对供种装置进行参数计算及结构设计;然后,建立3组不同参数配置的供种装置离散元仿真模型并进行排种过程仿真,根据其排种过程中种子的受力情况及排种连续性情况得到螺旋叶片直径80mm、螺旋叶片螺距60mm、螺旋轴直径28mm、排种口直径60mm为最优参数配比。在相同的工作要求下,对螺旋式与槽轮式供种装置排种过程进行离散元仿真,在相同供种量组条件下进行二者的破碎率测定,结果表明:原有槽轮式供种装置5种供种量下的破碎率依次为17.68%、14.90%、12.88%、12.45%、11.78%;相同供种量下,螺旋式供种装置的破碎率依次为2.11%、2.65%、2.91%、3.01%、3.37%,表明包衣柠条种子螺旋式供种装置排种破碎率明显降低。

压缩籽棉对棉籽破碎率及纤维品质的影响

为探究压缩对籽棉品质的影响因素与规律,以含水率、含杂率及压缩密度为试验因素,以棉籽破碎率、纤维长度、马克隆值、伸长率、反射率、黄度、整齐度、纤维强度为指标进行研究。结果表明:含水率、含杂率及压缩密度对棉籽破碎率影响显著(P<0.05),棉籽破碎率随压缩密度增大而增大,随含水率增加而降低;压缩密度仅对棉籽破碎率和反射率有显著影响(P<0.05);纤维长度随压缩密度、含水率增加而增加,随含杂率增加而减小;马克隆值随含杂率及含水率增加而增加,随压缩密度增加而减小;伸长率随含杂率增加而增加,随压缩密度及含水率增加而减小;反射率随压缩密度增大而增大,随含杂率增大而减小;黄度随含杂率增加而降低,随含水率增大而增大。

种子破损率快速检测方法研究

旋耕播种机推广鉴定时,需要人工检测种子的破损率。为提高检测效率,以小麦种子为例,对种子破损率快速检测方法进行研究。设计种子破损率自动检测平台,可一次性采集50 g小麦种子图像,基于图像处理技术和机器学习方法,提取小麦种子图像13个形状特征和8个纹理特征,建立基于特征的种子破损识别模型;识别的破损种子图像与种子质量的关系,建立基于图像的破损种子质量预测模型,按照鉴定大纲要求实现小麦种子破损率的快速检测。对江苏省“丹阳001”“A888”“泰州014”“无锡004”4个品种的小麦种子破损率进行试验测试,每个品种3次取样测定。结果表明:4个品种小麦种子破损率自动检测的平均相对误差分别为0.08%、0.07%、0.06%、0.08%,检测的相对均方根误差为0.08%,检测平均时长为5.216 s。该研究实现小麦种子破损率自动、快速检测,节省农机鉴定时间,推动农机鉴定过程的标准化和智能化。