苹果疏除幼果与成熟果果胶品质的比较

【目的】研究苹果疏除幼果与成熟果果胶品质的差异,为苹果资源的高效综合利用提供参考。【方法】以苹果疏果期的疏除幼果和成熟果榨汁后的果渣为原料,采用传统酸法提取苹果果胶,对2种果胶的得率、酯化度、纯度、总酚含量、褐变度、色值、特性黏度和黏均分子量进行测定,比较其差异并分析果胶的超微结构。【结果】苹果疏除幼果果胶的得率、酯化度、纯度、特性黏度及黏均分子量均小于成熟果果胶,但总酚含量和褐变度均大于成熟果果胶;成熟果果胶的色泽比苹果疏除幼果果胶好。苹果疏除幼果与成熟果果胶的表面结构存在明显差异。【结论】苹果疏除幼果果胶品质不如成熟果果胶。     

苹果原料中酵母菌的分离鉴定

利用酵母粉-葡萄糖琼脂培养基对15个批次苹果原料样本中的酵母菌进行分离，并用API 20C AUX菌种鉴定系统进行鉴定，得到的25株酵母菌分别属于假丝酵母菌属、酿酒酵母菌属和克勒克酵母菌属的6个种：克柔/平常假丝酵母菌(Candida krusei)13株，酿酒酵母菌(Saccharomyces cerevisiae)5株，克勒克酵母菌属酵母菌(Kloeckera spp.)2株，木蓝假丝酵母菌(C.magnoliae)2株，热带假丝酵母菌(C.tropicalis)和乳酒假丝酵母菌(C.kefyr)各1株，鉴定准确率均超过95％，其中模式菌酿酒酵母的鉴定准确率为98.7％，相似度1.00；鉴定评价均为“好”。该系统能够快速、准确地鉴定苹果原料中的酵母菌。     

Lane detection and tracking in PCR gel electrophoresis images

This article presents a new scheme that aims to track the center of and detect lanes without any human interventions as the first step of the automated tool to analyze DNA fingerprints represented in PCR gel electrophoresis images. Although several research results have been previously reported to track the centers of and detect the lanes using projection profiles, due to the curve of the lanes it was not completed yet. To resolve the problem, we estimated the average lane width using k-means clustering algorithm and conducted subsequent local image processing. In the subsequent local image processing, we partitioned an input image into small images and found local maxima (potential lane centers) on the vertical projection in each partitioned image. Then, the lanes were composed by connecting the local maxima. 38 PCR gel images including 1235 lanes were used to evaluate the performance of the proposed scheme. They were divided into two groups including 10 training images and 28 testing images. The proposed scheme finally achieved the performance of F-measure of 1.000 computed from precision of 0.998 and recall of 1.000. Experimental results have shown that the proposed scheme is able to track the center of and detect lanes without any human intervention and it may be used as an automated tool to help researchers to analysis PCR gel electrophoresis images.     

Curved and straight crop row detection by accumulation of green pixels from images in maize fields

This study proposes a new method for detecting curved and straight crop rows in images captured in maize fields during the initial growth stages of crop and weed plants. The images were obtained under perspective projection with a camera installed onboard and conveniently arranged at the front of a tractor. The final goal was the identification of the crop rows which are crucial for precise autonomous guidance and site-specific treatments, including weed removal based on the identification of plants outside the crop rows. Image quality is affected by uncontrolled lighting conditions in outdoor agricultural environments and by gaps in the crop rows (due to lack of germination or defects during planting). Also, different plants heights and volumes occur due to different growth stages affecting the crop row detection process. The proposed method was designed with the required robustness to cope with the above undesirable situations and it consists of three sequentially linked phases: (i) image segmentation, (ii) identification of starting points and (iii) crop row detection. The main contribution is the ability of the method to detect curved crop rows as well as straights rows even with irregular inter-row spaces. The method performance has been tested in terms of accuracy and time processing.     

苹果壳色单隔孢溃疡病菌TaqMan实时荧光PCR检测方法

目的 针对我国检疫性植物病原真菌苹果壳色单隔孢溃疡病菌Botryosphaeria stevensii,建立实时荧光PCR检测方法。方法 根据苹果壳色单隔孢溃疡病菌及其近似种的β微管蛋白基因(β-tubulin基因)保守序列设计1对特异性引物和1条TaqMan MGB探针,分别以病菌DNA和β-tubulin基因靶标序列重组质粒DNA为阳性标准品检验探针的特异性和灵敏度。结果 探针BsP267对苹果壳色单隔孢溃疡病菌菌株表现出特异性阳性扩增,且与近缘种及其他常见的果腐病菌无交叉反应,扩增效率为105.858%,探针检测DNA的灵敏度达到1 fg/μL。结论 本研究建立的苹果壳色单隔孢溃疡病菌实时荧光PCR检测法具有高特异性和灵敏度,可用于该病害的防控检测和检疫工作。     

苹果茎沟病毒的RT-PCR检测及其在我国苹果产区的分布

为开发苹果茎沟病毒(Apple stem grooving virus,ASGV)田间样本RT-PCR检测方法以及揭示我国AS-GV的发生情况,本研究以染病的苹果组培苗为试材,对ASGV RT-PCR检测方法进行了优化并利用优化的检测体系对我国苹果产区ASGV的发生情况进行了检测。结果表明:引物ASGVS特异性最好,优化的RT-PCR检测体系能够在4,7和11月份检测果树树皮组织的带毒情况,灵敏度达到能够检测2.5×10-2μg新鲜样本,适于苹果ASGV田间样本检测。通过对我国苹果产区ASGV的检测发现,被检测的13个省(市/区)苹果上几乎都有ASGV发生,只有甘肃省泾川样本未检测到该病毒,采集的327个样本带毒率达73.7%。     

河北农业大学高水平科技论文统计与分析

采用统计分析的方法,对河北农业大学2003--2007年被美国三大英文检索系统收录（SCI、EI、ISTP）和河北农业大学认定的一级学报上发表的学术论文,按总量,学院分布,论文作者年龄、职称、学位分布情况逐一进行了统计分析。分析结果从一个侧面反映了河北农业大学科学研究现状,并对其有关问题进行了探讨。     

基于颜色矩的土豆、玉米、苹果叶片病害异常检测

农作物病害是影响粮食产量的重要因素之一。目前,大部分研究以已知病害作为数据来源,使用传统机器学习和深度学习方法进行病害识别与分类,这种模型构建方法需要大量的病害数据,而当新发病害出现时,很可能因为检测不到而错过最佳预警时间。为解决该问题,本文拟提出一种仅使用正常农作物叶片数据集作为训练数据便可检测出叶片病害异常的方法。具体地,本研究提出一种基于k-means++聚类与图像分块的农作物叶片病害异常检测方法,通过图像去噪、图像分割、图像截取等预处理操作后,提取图像的颜色矩特征,对训练集进行k-means++聚类,构建比对模型并设置阈值,从而确定测试集异常与否。试验使用的土豆、玉米与苹果数据集均下载于Kaggle网站。通过调整聚类数与分块数,在土豆、玉米和苹果数据集上,识别准确率分别达到了89%、95%、95%以上,并且在玉米和苹果两种数据集上的漏警率为0。     

苹果树主要病害内生拮抗真菌的筛选及拮抗特性

【目的】筛选苹果树腐烂病菌Valsa malimiyabe与苹果树炭疽病菌Glomeralla cingalata的内生拮抗真菌,并研究其抑菌效果。【方法】分离发病地区健康苹果树的根、茎及其根际土壤中的拮抗内生真菌,室内抑菌试验测定其对苹果树腐烂病菌和苹果树炭疽病菌的抑制作用;发酵培养试验检测其内生真菌中抑菌物质的耐热性。【结果】分离到的18株内生真菌分属3个属,其中茎分离到的内生真菌的种类和数量较多;对峙试验表明,对苹果树腐烂病菌和炭疽病菌的抑菌率高于60%的有8株,其中菌株J2、J16、J17对苹果树腐烂病的抑制率分别为82.75%、84.31%和82.35%,菌株T1对苹果树菌株炭疽病的抑制率为80.67%。拮抗真菌J2、J16、J17、J24、T1发酵液对2种病原菌分生孢子萌发的抑制率均不小于39.55%,最高达69.75%;在121℃时,J11和T1无菌滤液的热稳定性较好,而J2无菌滤液在100°C时就失去了生物活性。【结论】拮抗真菌J2、J11、J16、J17、T1均对苹果树腐烂病菌及苹果树炭疽病菌有较强拮抗作用,可为开展这2种病害的生物防治研究提供潜在资源。     

陕西省苹果汁中脂环酸芽孢杆菌的分离与鉴定

【目的】明确陕西省生产苹果汁中脂环酸芽孢杆菌(Alicyclobacillus)的分布情况。【方法】对健康苹果苹果汁和软腐苹果腐烂部位中的嗜酸耐热菌进行了分离、纯化,并以1株酸土脂环酸芽孢杆菌(A.acidoterrestris)DSM3922为标准菌株,对分离菌株进行了个体形态、菌落形态观察和嗜酸性、耐热性、生理生化特性试验,最后用表型聚类法分析了供试菌株的亲缘关系。【结果】分别从苹果汁、腐烂苹果中分离到50和5个疑似菌落,嗜酸耐热特性试验表明,其中有14株属嗜酸耐热菌,且均是芽孢杆菌,大小为2.0~5.0μm;其菌落表面湿润,中心突起,有同心圆,半透明,不易挑起,形状圆形,直径为1.5~5 mm;接触酶、脲酶检验结果均呈阳性,都可利用肌醇、甘油、L-山梨糖为惟一碳源,且利用葡萄糖均不能产酸产气。鉴定结果确定分离菌株均属于脂环酸芽孢杆菌属的不同种,其中TAB-3号菌与标准菌株最为接近。【结论】陕西省苹果汁中的耐热菌,除酸土脂环酸芽孢杆菌以外,还有脂环酸芽孢杆菌属的其他菌,其主要分属于A.acidoterrestris、A.acidocaldarius、A.pomorum3个种。     

Passion fruit detection and counting based on multiple scale faster R-CNN using RGB-D images

The accurate and reliable fruit detection in orchards is one of the most crucial tasks for supporting higher level agriculture tasks such as yield mapping and robotic harvesting. However, detecting and counting small fruit is a very challenging task under variable lighting conditions, low-resolutions and heavy occlusion by neighboring fruits or foliage. To robustly detect small fruits, an improved method is proposed based on multiple scale faster region-based convolutional neural networks (MS-FRCNN) approach using the color and depth images acquired with an RGB-D camera. The architecture of MS-FRCNN is improved to detect lower-level features by incorporating feature maps from shallower convolution feature maps for regions of interest (ROI) pooling. The detection framework consists of three phases. Firstly, multiple scale feature extractors are used to extract low and high features from RGB and depth images respectively. Then, RGB-detector and depth-detector are trained separately using MS-FRCNN. Finally, late fusion methods are explored for combining the RGB and depth detector. The detection framework was demonstrated and evaluated on two datasets that include passion fruit images under variable illumination conditions and occlusion. Compared with the faster R-CNN detector of RGB-D images, the recall, the precision and F1-score of MS-FRCNN method increased from 0.922 to 0.962, 0.850 to 0.931 and 0.885 to 0.946, respectively. Furthermore, the MS-FRCNN method effectively improves small passion fruit detection by achieving 0.909 of the F1 score. It is concluded that the detector based on MS-FRCNN can be applied practically in the actual orchard environment.

     

观赏及砧木用苹果品种的SRAP-PCR反应体系优化及其多态性检测

通过建立和优化PCR扩增体系,开展观赏及砧木用苹果品种的SRAP分子标记遗传多样性研究,根据所获得的多态位点探究不同材料间的亲缘关系。研究结果表明SRAP-PCR最佳反应体系为(20μL):模板DNA30ng,1×Buffer,Mg2+1.5 mmol/L,dNTP 0.3 mmol/L,引物各0.4μmol/L,TaqDNA聚合酶1.0 U。应用筛选出的12对多态性丰富的SRAP引物组合对8个观赏和砧木用苹果品种进行遗传多样性分析,共得到97条扩增谱带,其中多态性谱带78条,多态性比率80.4%,平均每个引物组合产生6.5个多态性位点,供试材料间遗传变异相对丰富。运用NTSYS软件中的UPGMA算法分析,各材料的遗传相似系数为0.62~0.92。树状图中,芭蕾苹果‘舞乐’、‘舞佳’聚为同组,遗传变异较小;砧木种质‘CX3’、‘CX5’遗传差异相对较大;‘秋幸’、‘秋实’具有较近的遗传关系。此研究结果可为观赏及砧木用苹果种质资源的品种鉴定及分子标记辅助育种提供参考。     

Automatic knot detection and measurements from X-ray CT images of wood: A review and validation of an improved algorithm on softwood samples

An algorithm to automatically detect and measure knots in CT images of softwood beams was developed. The algorithm is based on the use of 3D connex components and a 3D distance transform constituting a new approach for knot diameter measurements.The present work was undertaken with the objective to automatically and non-destructively extract the distributions of knot characteristics within trees. These data are valuable for further studies related to tree development and tree architecture, and could even contribute to satisfying the current demand for automatic species identification on the basis of CT images.A review of the literature about automatic knot detection in X-ray CT images is provided. Relatively few references give quantitatively accurate results of knot measurements (i.e., not only knot localisation but knot size and inclination as well).The method was tested on a set of seven beams of Norway spruce and silver fir. The outputs were compared with manual measurements of knots performed on the same images.The results obtained are promising, with detection rates varying from 71% to 100%, depending on the beams, and no false alarms were reported. Particular attention was paid to the accuracy obtained for automatic measurements of knot size and inclination. Comparison with manual measurements led to a mean R² of 0.86, 0.87, 0.59 and 0.86 for inclination, maximum diameter, length and volume, respectively.     

苹果L-半乳糖脱氢酶基因 cDNA全长的克隆与序列分析

L-半乳糖脱氢酶(L-Galactose dehydrogenase,GalDH)是维生素C合成的L-半乳糖途径中,催化L-半乳糖生成L-半乳糖内酯的关键酶。根据GenBank中登录的的GalDH cDNA序列设计1对扩增引物,以嘎拉苹果叶片为材料,采用RT-PCR法扩增出GalDH cDNA全长。将获得的基因片段克隆到pMD18-T载体上,转入大肠杆菌DH5α筛选阳性克隆,经酶切和PCR鉴定,并对插入片段进行序列分析,结果表明,本试验获得的cDNA片段长为1 111 bp,是苹果GalDH cDNA全长。     

绿茶、苹果、甘草复合保健饮料的生产工艺

研究了绿茶、苹果、甘草复合保健饮料的生产工艺.应用模糊数学设计方案和模糊数学评判确定了该饮料的配方,并应用正交试验设计方案确定复配稳定剂的配方.试验结果表明,绿茶复合保健饮料的最佳组合方式为C2D1B2A2,即澄清的绿茶、苹果、甘草混合汁用量30%,蔗糖2.6%,蜂蜜2.0%,柠檬酸钾0.2%,水为65.2%(上述百分比均为质量分数).复合稳定剂的最佳组合为A3B3C1,即0.70%羧甲基纤维素钠+0.40%黄原胶+0.20%κ-卡拉胶.     

紫心甘薯、黑莓、苹果复合果蔬汁的研制

[目的]确定紫心甘薯、黑莓、苹果复合果蔬汁的最佳配方及稳定剂的选择与用量,研制出风味独特、营养健康的新型复合果蔬汁饮料.[方法]通过3因素3水平正交试验结合感官评定确定复合果蔬汁的最佳配方,比较不同稳定剂对复合果蔬汁稳定性的影响,并测定复合果蔬汁的主要营养成分.[结果]复合果蔬汁最佳配方为:紫心甘薯汁、黑莓汁、苹果汁的体积配比为5:3:2,蔗糖30 g/L,并添加1.2 g/L耐酸性羧甲基纤维素钠(CMC-Na).[结论]以紫心甘薯汁、黑莓汁、苹果汁混合制备复合果蔬汁,能使糖含量高的紫心甘薯汁和酸度很高的黑莓汁有效互补,再结合苹果汁的风味,其口味、色泽、营养具佳,稳定性良好.     

Modelling agronomic images for weed detection and comparison of crop/weed discrimination algorithm performance

A new method for weed detection based on modelling agronomic images taken from a virtual camera placed in a virtual field is proposed. The aim was to measure and compare the effectiveness of the developed algorithms. Two sets of images with and without perspective effects were simulated. For images with no perspective, based on Gabor filtering and on the Hough transform, the performance of two crop/inter-row weed discrimination algorithms were tested and compared. The method based on the Hough transform is, in any case, better than the one based on Gabor filtering. For images with perspective effects only, an algorithm based on the Hough transform was tested and an extension to real images is discussed. These tests were done by a comparison between the weed infestation rate detected by these algorithms and the true one. This evaluation was completed with a crop/weed pixel classification and it demonstrated that the algorithm based on a Hough transform gave the best results (up to 90%).     

油菜隐性核不育系从低芥向双低转育的技术

用双低品种对不育系转育测交后，在F2代分离出的不育株上再用其品种回交，然后在B1F2（回交种的F2）代群体中选可育株自交，自交株通过室内硫甙粗测分析淘汰种植B1F3的低硫株行，花期选择出现1／4不育株率的株系进行系内兄妹交，再种植鉴定，选50％左右不育株率的株系成对测交。用3年6代（含夏繁）育成了不育株率为51．8％、硫甙含量为31．2μmol/g的不育系－油研7号双低不育系。其选择群体在100－500株的范围，双低株系的入选株率在5％以上，不育系稳定的入选株系率可达60％左右。     

结合先验形状信息和序贯学习的心血管内超声外弹力膜检测

目的 针对心血管内超声(IVUS)图像中钙化斑块、声影等干扰因素影响外弹力膜(EEM)轮廓检测准确性的问题,提出结合先验形状信息和序贯学习分类的心血管内超声外弹力膜检测的改进算法。方法 首先用多类多尺度序贯学习(M²SSL)将IVUS图像分割七大不同组织;然后在分类结果的基础上,结合血管先验形状信息筛选出外弹力膜轮廓的关键点;最后,结合IVUS图像的梯度和相位信息,采用Snake模型,获得最终的EEM轮廓。结果 临床采集22组IVUS序列,挑选出具有代表性的153帧图像做实验。统计数据显示:本文算法检测结果的平均Jacc指标为88.5%,满足临床诊断要求,性能优于国内近年来较好的算法。结论 本文的EEM自动检测算法简单有效,相比国内已有算法,提高了对钙化、纤维斑块以及声影区域的识别能力,对含钙化斑块、纤维斑块或血管中心偏移的高频IVUS图像具有较高的适用性。     

Immature peach detection in colour images acquired in natural illumination conditions using statistical classifiers and neural network

Detection of immature peach fruits would help growers to create yield maps which are very useful tools for adjusting management practices during the fruit maturing stages. Machine vision algorithms were developed to detect and count immature peach fruit in natural canopies using colour images. This study was the first effort to detect immature peach fruit in natural environment to the authors’ knowledge. Captured images had various illumination conditions due to both direct sunlight and diffusive light conditions that make the fruit detection task more difficult. A training set and a validation set were used to develop and to test the algorithms. Different image scanning methods including finding potential fruit regions were developed and used to parse fruit objects in the natural canopy image. Circular Gabor texture analysis and ‘eigenfruit’ approach (inspired by the ‘eigenface’ face detection and recognition method) were used for feature extraction. Statistical classifiers, a neural network and a support vector machine classifier were built and used for detecting peach fruit. A blob analysis was performed to merge multiple detections for the same peach fruit. Performance of the classifiers and image scanning methods were introduced and evaluated. Using the proposed algorithms, 84.6, 77.9 and 71.2 % of the actual fruits were successfully detected using three different image scanning methods for the validation set.