纵向刨切生产薄木中刀具与产品质量的关系

本文探讨了纵向刨切机生产薄木过程中刀具的材料、形状及刀具与压尺的相对位置等因素对产品质量的影响。     

浅谈开放式数控系统及其在线切割机床上的应用

介绍了开放式体系结构数控系统的概念、特点及分类,并简要介绍了开放式数控系统在电火花线切割机上的应用。     

带锯机杠杆压砣式张紧系统的研究

根据以前对带锯机杠杆压砣式张紧系统振动的分析,设计了一新型装置,并用试验进行了对比分析,证实采用改进后的杠杆压砣系统可大大降低锯条的振动。为改善带锯机的动态特性提供了一条有效的途径。     

液压传动技术在烟草打顶机上的应用

针对烟草中后期田间管理阶段的打顶及喷施抑芽剂时,作业人员劳动强度大、生产效率低,严重影响烟草产量和质量的现状,研制出高地隙三轮自走式全液压烟草打顶机。该机采用T型车架及高架式机体,液压驱动行走、转向、制动系统,以及液压操纵烟草打顶、喷施药剂系统。该打顶机液压系统传递功率大、结构紧凑、布置灵活,且易于实现无级调速及过载保护,便于控制、操作简便,可高效地完成烟草打顶及喷施药剂作业。     

灌木平茬机沙地通过性仿真研究

为研究灌木平茬机在科尔沁沙地的通过性能,基于ADAMS/ATV建立了其虚拟样机模型及符合沙地特性的平地、坡路、垂直障碍、凹坑、正弦路面等虚拟地形,进行了灌木平茬机在典型路面上行驶的仿真分析。结果表明,灌木平茬机能够平稳通过所建立的虚拟地形,在沙地上具有较好的通过性能,转向平稳。根据仿真结果确定灌木平茬机主动轮的最佳直径为280 mm、转向半径为0.75 m。     

瓦楞纸板自动横切机控制系统设计

针对国内瓦楞纸板生产效率低、剪损大的缺点,研制开发了瓦楞纸板自动横切系统,详细分析了其工作原理、系统构成和功能实现.现场使用表明：该系统具有自动化程度高,切割精度高,产品成本低等优点.     

Investigating the effects of simulated transport vibration on tomato tissue damage based on vis/NIR spectroscopy

Visible and near infrared (vis/NIR) spectroscopy combined with chemometrics were investigated to evaluate the effects of simulated transport vibration levels on damage of tomato fruit. A total of 280 tomato samples were randomly divided into 5 groups; each group was subjected to vibration at different acceleration levels. A total of 230 samples (46 from each group) were selected as a calibration set; whereas 50 samples (10 from each group) were selected as a prediction set. Raw spectra, differentiation (the first derivative) spectra, extended multiplicative scatter correction (EMSC) processed spectra and standard normal variant combined with detrending (SNV–DT) processed spectra were used for calibration models. SNV–DT processed spectra had the best performance using for partial least squares (PLS) analysis. The PLS analysis was implemented to calibrate models with different wavelength bands including visible, short-wave near infrared (SWNIR) and long-wave near infrared (LWNIR) regions. The best PLS model was obtained in the vis/NIR (600–1600 nm) region. Using a grid search technique and radial basis function (RBF) kernel, four least squares support vector machine (LS–SVM) models with different latent variables (7, 8, 9, and 10 LVs) were compared. The optimal model was obtained with 9 LVs and the correlation coefficient (r), root mean square error of prediction (RMSEP) and bias for the best prediction by LS–SVM were 0.984, 0.137 and 0.003, respectively. The results showed that vis/NIR spectroscopy could be applied as a reliable and rapid method for predicting the effect of vibration levels on tissue damage of tomato fruit.     

The use of infrared spectroscopy and machine learning tools for detection of Meloidogyne infestations

Plant parasitic nematodes are generally soilborne pathogens that attack plants and cause economic losses in many crops. The infested plants show nonspecific symptoms or, often, are symptomless; therefore, diagnosis is performed by taking soil and root tissue samples. Here, we show that a combination of different infrared spectra analysis and machine learning algorithms can be used to detect plant parasitic nematode infestations before symptoms become visible, using leaves instead of roots and soil as samples. We found that tomato and guava plants infested with Meloidogyne enterorlobii produced different spectral patterns compared to uninfested plants. Using partial spectra from 1,450 to 900/cm as the "fingerprint region", principal component analyses indicated that after 5 (tomatoes) or 8 weeks (guava), plants with no visible symptoms of infestations were positively diagnosed. To improve the early detection response, we used machine learning modelling. A support vector machine (SVM) was used to obtain more robust, accurate models. The SVM model contained 34 support vectors, 17 for each level. The overall performance of the model was >97% and the total accuracy was significantly higher, demonstrating the absence of chance prediction. The best prediction of infestation was obtained at the second and fourth weeks for tomatoes and guavas, respectively, reducing the diagnostic time by half. The combined application of these techniques reduces the processing time from field to laboratory and shows enormous advantages by avoiding root and soil sampling.     

基于边缘检测和区域定位的玉米根茎导航线提取方法

基于玉米根茎图像信息，提出一种基于边缘检测和区域定位的玉米根茎导航线提取方法。首先，利用最大类间方差法自动分割2G-R-B灰度图像，得到二值化图像，结合形态学处理、位置/面积去噪方法提高二值化图像质量，对去噪图像按列累加得到列像素累加曲线；针对传统方法得到的特征点中伪特征点较多的问题，引入高斯滤波器平滑累加曲线，并运用极值法减少玉米根茎伪特征点的干扰，在提取玉米茎秆边缘直线时，提出基于最远茎秆成像宽度的双侧边缘判别思路，通过扫描每条边缘直线的四边形封闭邻域有效剔除伪边缘直线；最后，根据边缘直线二次定位玉米的根茎区域范围，并剔除伪特征点，采用最小二乘线性拟合方法准确提取导航线。试验表明，本文算法处理一幅1280像素×720像素图像耗时约236ms，特征点拟合准确率为92%。与传统方法相比，本文算法精度高、实时性好，在缺苗、杂草较多和株距不标准的情况下仍具有较强的鲁棒性。     

玉米行间导航线实时提取

针对高地隙植保机底盘玉米田间植保作业压苗严重的现象,该研究提出了基于车轮正前方可行走动态感兴趣区域(Region of Interest,ROI)的玉米行导航线实时提取算法。首先将获取的玉米苗带图像进行像素归一化,采用过绿算法和最大类间方差法分割玉米与背景,并通过形态学处理对图像进行增强和去噪;然后对视频第1帧图像应用垂直投影法确定静态ROI区域,并在静态ROI区域内利用特征点聚类算法拟合作物行识别线,基于已识别的玉米行识别线更新和优化动态ROI区域,实现动态ROI区域的动态迁移;最后在动态ROI区域内采用最小二乘法获取高地隙植保机底盘玉米行间导航线。试验表明,该算法具有较好的抗干扰性能,能够很好地适应较为复杂的田间环境,导航线提取准确率为96%,处理一帧分辨率为1 920像素×1 080像素图像平均耗时97.56 ms,该研究提出的算法能够为高地隙植保机车轮沿玉米垄间行走提供可靠、实时的导航路径。