二十二份毛花猕猴桃种质资源果实品质的主成分分析与综合评价

为了更好地了解毛花猕猴桃种质资源的多样性,测定了浙江省丽水市境内22份毛花猕猴桃种质的8个品质指标,进行了变异分析、主成分分析、聚类分析和综合评价。结果表明,果实品质指标差异较大,单果质量的变异系数最大(38%);果实横径的变异系数最小(11%);聚类分析结果表明,22份毛花猕猴桃种质可以分为两大类,其中第一类种质果实的可溶性固形物含量、可滴定酸含量、维生素C含量均高于第二类,第二类种质果实的固酸比稍高于第一类;主成分分析表明,前三个主成分贡献率达85.04%,可综合反映果实品质信息;综合评价结果显示,LD-18、L-74、L-27、L-19、L-73这5个单株果实综合性状表现优良。综上所述,丽水市境内毛花猕猴桃种质资源果实性状具有丰富的多样性,为新品种的选育和果实品质改良提供了重要的参考依据。     

水稻赖氨酸含量测定方法改进及其最优动态分类研究

赖氨酸是水稻营养成分中第一限制性氨基酸。为优化赖氨酸的测定方法,设置了本试验,通过超声振荡器取代普通振荡器,进行300W超声功率下酰化用时及染料结合反应用时的研究。结果表明,最佳酰化用时为15 min,染料结合用时为90 min。与国标法相比,优化后的方法酰化用时变长、染料结合用时变短,测定总用时明显缩短,但测定结果和试验精度更高。可见,采用超声波振荡代替传统振荡器振荡能改进赖氨酸的测定方法。另外,采用最优动态聚类法对赖氨酸含量进行分类,能防止人为分类的不确定性,做到分类方案的最优化。     

Cotton Image Segmentation Algorithm Based on Fusion Method of Markov Random Field and Quantum Particle Swarm Cluster

[Objective] The aim of this study was to improve the cotton image segmentation accuracy in a picking robot image processing system. [Method] An image segmentation algorithm based on a fusion method of Markov random field and quantum particle swarm optimization clustering was proposed. The process of the proposed algorithm is as follows: first, transform the RGB (red, green, blue) images into grayscale; second, use it to segment these images; finally, the threshold of the connected area is set on the basis of the segmented image to obtain the target area. Then, the cotton front image and the cotton side image are selected from the images collected from different angles. The segmentation experiment was carried out by using this algorithm, and compared with the Otsu algorithm, the fuzzy C-means algorithm, the quantum particle swarm image segmentation algorithm and the Markov random field image segmentation algorithm. [Result] The results showed that the segmentation accuracy and peak signal to noise ratio of the proposed algorithm were 98.94% and 77.48 dB. When compared with the Otsu algorithm, fuzzy C-means algorithm, quantum particle swarm optimization algorithm and Markov random field algorithm, the average segmentation accuracy and peak signal to noise ratio of the proposed algorithm increased by 2.47%–4.56%, and 9.81–13.11 dB, respectively. [Conclusion] The proposed algorithm had higher segmentation accuracy and higher peak signal to noise ratio than the other algorithms tested.     

基于Web电子商务平台的食用菌产品共词聚类算法

针对食用菌产品电子商务平台的数据挖掘问题,采用了共词聚类算法进行主题聚类,在密度聚类算法的基础上,使用了移动非密集单元的处理方法实现了共词聚类。该算法克服了传统聚类算法裁剪造成的精度误差,提高了聚类结果的精确度,应用在电子商务平台的效果良好。     

基于变分模态分解-BA-LSSVM算法的配电网短期负荷预测

配电台区日负荷序列呈现为既包含变化趋势、又含有波动细节的不规则曲线,该文借助变分模态分解(variational mode decomposition,VMD)将包含这些信息的原始日负荷序列分解为不同频率尺度的子序列,并结合一系列复杂的环境因素,分别利用不同的最小二乘支持向量机(least squares support vector machine,LSSVM)模型进行负荷预测,最后将基于不同频率分量的预测结果相加得到最终的日负荷预测结果。为了提高LSSVM预测能力,采用蝙蝠算法(bat algorithm,BA)对各LSSVM的参数进行寻优,同时,该文分析了影响负荷变化的环境因素,设计了一套因素归一化方法,预测过程考虑了环境因素的影响。仿真结果表明,该文提出的考虑复杂环境因素的预测思想及对历史日负荷进行VMD分解、BA优化、LSSVM预测的组合预测方法能有效提高短期日负荷预测的准确性。     

烟草疫霉SSR分子标记的开发与初步应用

为开发烟草疫霉的SSR分子标记,利用MISA软件搜索烟草疫霉基因组序列中的SSR位点,共发现1311个SSR位点,优势SSR位点为二核苷酸和三核苷酸,分别占总SSR位点的56.45%和39.36%;根据分析到的SSR位点使用Primer 5.0软件设计48对SSR引物,以7株烟草疫霉的DNA为模板对这些引物进行筛选,共获得扩增条带清晰且具有多态性的SSR引物20对,然后使用其中的6对SSR对32株烟草疫霉进行UPGMA聚类分析,遗传相似系数在0.60~1.00之间,在相似系数0.70水平上,可将其划分为3个类群,类群Ⅰ包含29个菌株,类群Ⅱ包含1个菌株,类群Ⅲ包含2个菌株,显示出较低的遗传分化水平。这些SSR引物的开发将为研究我国烟草疫霉的遗传多样性、群体遗传结构以及遗传图谱构建等奠定基础。     

基于投影寻踪分类法的甘蔗种质综合评价

基于投影寻踪分类法对40份从法国和菲律宾引进的甘蔗种质进行综合评价。方差分析结果表明,各种质间茎径、单茎质量、蔗糖分等主要工农艺性状均存在显著差异(P0.01),变异系数介于5.68%～51.55%,变异广泛。对各种质的株高、茎径、有效茎、单茎质量、锤度、甘蔗蔗糖分、蔗汁蔗糖分、简纯度和纤维分等9个主要工农艺性状指标进行投影方向和投影值大小分析,结果表明:投影方向以甘蔗蔗糖分的最高,其后依次为锤度、蔗汁蔗糖分、简纯度、单茎质量和茎径等,株高和有效茎的较低;40份种质投影值大小各异,根据投影值大小,结合聚类分析将40份种质划分为5个类群,各类群投影值介于0.00～2.50,其中,第Ⅳ类群的5份种质资源(FR97–127、FR97–53、VMC95–29、VMC96–169和VMC97–30)综合性状表现优良,可供甘蔗杂交利用。综合来看,投影寻踪分类法可作为大量甘蔗种质资源评价的有效途径。     

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.     

基于卷积神经网络的田间多簇猕猴桃图像识别方法

为实现田间条件下快速、准确地识别多簇猕猴桃果实,该文根据猕猴桃的棚架式栽培模式,采用竖直向上获取果实图像的拍摄方式,提出一种基于Le Net卷积神经网络的深度学习模型进行多簇猕猴桃果实图像的识别方法。该文构建的卷积神经网络通过批量归一化方法,以Re LU为激活函数,Max-pooling为下采样方法,并采用Softmax回归分类器,对卷积神经网络结构进行优化。通过对100幅田间多簇猕猴桃图像的识别,试验结果表明:该识别方法对遮挡果实、重叠果实、相邻果实和独立果实的识别率分别为78.97%、83.11%、91.01%和94.78%。通过与5种现有算法进行对比试验,该文算法相对相同环境下的识别方法提高了5.73个百分点,且识别速度达到了0.27 s/个,识别速度较其他算法速度最快。证明了该文算法对田间猕猴桃图像具有较高的识别率和实时性,表明卷积神经网络在田间果实识别方面具有良好的应用前景。     

毛薯的营养成分分析和聚类分析

为选育出优良的毛薯种质,本研究以海南大学薯蓣种质资源圃里的80份毛薯为材料,以河南焦作温县的垆土铁棍山药为对照,采用苯酚硫酸法、考马斯亮蓝法以及蒽酮比色法对其干物质、粗多糖、蛋白、可溶性糖和淀粉含量进行测定和比较分析。结果表明:毛薯的干物质含量介于18.62%~38.88%,粗多糖含量介于2.88%~15.82%、蛋白含量介于5.14%~10.71%、可溶性糖含量介于0.1%~13.84%、淀粉含量介于11.94%~64.28%。与对照山药相比,Ds96所测品质均优于对照,可优先选择推广。此外,毛薯各营养品质之间存在着一定的相关性,干物质与糖类存在显著负相关,与蛋白和淀粉表现为正相关。通过对80份毛薯的营养品质进行聚类分析,在欧式距离为3.17时,可将其分为八类,按类群来看,类群Ⅳ各营养价值适中,其他类群在某些营养上比较优秀,可根据需要进行优良资源选择。