榉树苗木质量分级研究

在南京、镇江、常州和无锡等江苏榉树主要分布地，选取榉树1年生播种苗苗圃12个，对1080株榉树苗木的地径和苗高调查，进行了小样方随机抽样；通过对70株苗木的地径、苗高、主根长、侧根数以及全株干重进行相关分析后，确定地径和苗高作为苗木分级的质量指标，分别用标准差法和聚类分析法对榉树苗木质量的分级标准进行丁探讨比较。结合苗木生产实际，得出1年生榉树播种苗的3级分级标准是Ⅰ级苗：地径≥1．0cm，苗高≥110．0cm；Ⅱ级苗：1．0cm〉地径Ⅰ〉0．60cm，110．0cm〉苗高≥85．0cm；Ⅲ级苗：地径〈0．60cm，苗高〈85．0cm。     

黄秋葵DUS测试主要数量性状的测量及分级

为了优化黄秋葵数量性状的测试方法及分级标准,本试验以35份黄秋葵资源为研究对象,采用不同测量方法对植株高度、主茎直径2个数量性状的观测结果进行比对;并采用最小显著差法对12个数量性状进行分级。结果表明:综合考虑小区内一致性程度及操作方便性,黄秋葵植株高度宜测量自然株高,主茎直径宜测量主茎中部直径。在现有试验材料基础上,叶片裂刻深度适合采用9级划分,果实成熟果长度适合采用不等分分级。     

国外水果分级技术

 本文介绍国外按水果大小、重量,对光线反射性,射线透过性等来进行分级装置的各种结构型式,为减少损伤而采取的湿法和干法卸装工艺以及整个分级流水线的总体布置情况.     

大豆灰斑病叶部病斑严重度的分级标准

根据单个叶片病斑面积总和与整个叶片面积的比值,以及自然侵染的最高病斑/叶面积比,将大豆灰斑病叶部病害的严重度划分为0～6七个级别,并制出各级别标准图片和绘出模式图。     

MATLAB软件在食品加工中的应用

食品加工过程中,数据处理分析解释很关键。MATLAB软件已广泛应用于图像识别、自动控制等方面,对于食品加工过程中非线性和非稳态过程,MATLAB中的一些功能,如Simulink可以对生产控制、品质评价、预测等方面进行很好地仿真。简单介绍了MATLAB软件,并综述了其在食品加工过程中分类分级、生产控制、品质评价等方面的应用。     

机器视觉检测鲜烟叶的分级装置设计

针对农民在烟叶送烤前对烟叶分级的非重视度和非客观性等问题,本文提出基于机器视觉技术的烟叶图像检测分类方法对烟叶编烟送烤前进行成熟度划分,设计了全自动化的鲜烟叶检测分级装置。该系统的机械结构由自动上样抓取烟叶机构、烟叶输送台、检测机构,分拣机构等四个部分组成。自动上样的烟叶在传送带上被CCD检测并进行图像处理。首先对图像的噪点采用邻域平均和中值滤波组合的方法进行区域去噪处理;使用最小误差阈值分割方法分离背景和烟叶,然后增强图像信号,提取感性区域的颜色信息。采用烟叶的4个特征信息(R,G,B颜色值和色调H值)来表征烟叶的级别特性。通过装置自主学习建立样本库,然后参考学习的样本库对未知样品进行检测分级。实验结果表明根据烟叶色泽的不同,相邻类型之间的色泽差异越大,分类准确度越高。检测分类的平均速度在2-3秒/片,满足现场即时检测要求。     

Determination of oil palm fresh fruit bunch ripeness—Based on flavonoids and anthocyanin content

Non-destructive and real-time oil palm fresh fruit bunch (FFB) grading systems are of major exploratory concern for researchers in the oil palm industry. The objective is to reduce time, labour, costs, and most importantly, to increase the oil extraction rate, in order to achieve a good quality of palm oil at a more acceptable price. This research investigates the potential of flavonoids and anthocyanins as a predictor to classify the degree of oil palm FFB ripeness. This paper also discusses the relationship between these predictors and the ripeness categories period. One hundred and eighty oil palm FFB samples were collected from a private plantation in Malaysia, according to three maturity categories i.e., ripe, under-ripe, and over-ripe. Each sample was randomly scanned 10 times, both front and back using a hand-held Multiplex^®3 multi-parameter fluorescence sensor. The results show that flavonoid and anthocyanin content decreased from immature to over mature oil palm FFBs. Overall, the relationship using Pearson's correlation between flavonoids and anthocyanins was r² = 0.84 and the most outstanding relationship accuracy was at the over-ripe stage, at 90%. Statistical analysis using analysis of variance (ANOVA) and pair-wise testing proved that both predictors gave significance difference between under-ripe, ripe, and over-ripe maturity categories. This shows that both predictors can be good indicators to classify oil palm FFB. Classification analysis was performed by using both predictors together and separately through several methods. The highest overall classification accuracy was 87.7% using a Stochastic Gradient Boosting Trees model and with both predictors. The other classification methods used either independent or both predictors together and gave various results ranging from 50 to 85% accuracy. This research proves that flavonoids and anthocyanins can be used as predictors of oil palm maturity classification.     

穿心莲种子质量分级标准研究

[目的]制定穿心莲（Andrographis paniculata）种子质量分级标准。[方法]测定不同来源的穿心莲种子净度、千粒重、水分和发芽率等指标,使用SPSS软件,对千粒重、发芽率数据进行K-均值聚类分析,制定分级标准。[结果]以发芽率为主,综合水分、千粒重及净度4个指标初步制定了穿心莲种子质量分级标准,把穿心莲种子质量等级分为3个等级。[结论]该研究制定的质量分级标准具有生产实用性,可作为穿心莲种子的质量控制标准。     

基于粗集的土地定级因素综合赋权法模型研究

应用粗集理论研究了土地定级中各影响因素的合理赋权问题,提出了体现主客观信息的土地定级因素综合集成赋权方法,并依据长阳住宅用地定级指标体系和样点数据,对土地定级影响因素进行了权重挖掘。     

Mass modeling of pomegranate (Punica granatum L.) fruit with some physical characteristics

Among physical characteristics, dimensions, mass, volume and projected areas are important parameters in sizing and grading systems. Fruits with the similar weight and uniform shape are desirable in terms of marketing value. Therefore, grading fruit based on weight reduces packing and handling costs and also provides suitable packing patterns. The different grading systems require different fruit sizing based on particular parameters. In this study pomegranate mass was predicted by applying different physical characteristics with linear and nonlinear models as three different classifications: (1) single or multiple variable regressions of pomegranate dimensional characteristics, (2) single or multiple variable regression of pomegranate projected areas and (3) estimating pomegranate mass based on its volume. The results showed that mass modeling of pomegranate based on minor diameter and three projected areas are the most appropriate models in the first and second classifications, respectively. In third classification, the highest determination coefficient was obtained for mass modeling based on the actual volume as R² = 0.99 whereas corresponding values were 0.93 and 0.79 for assumed pomegranate shapes (oblate spheroid and ellipsoid), respectively. In economical and agronomical point of view, suitable grading system of pomegranate mass was ascertained based on minor diameter as nonlinear relation M = 0.06c² − 4.11c + 143.56, R² = 0.91.