快速检测柑桔黄龙病病原的研究

应用ＰＣＲ技术对柑桔黄龙病病原ＤＮＡ进行体外扩增,可建立一套快速,准确,有效的检测该病原的方法,研究结果表明：ＰＣＲ技术对柑桔黄龙病病原的检测具有很强的特异性,只有感染了黄龙病病原的样品,ＰＣＲ才呈阳性反应。文章报道了应用ＰＣＲ技术能检测已带病但尚未症状的柑桔黄龙病病株,该检测技术可以对柑桔黄龙病进行早期诊断,及是地控制带病苗木的传播,这对选育无病苗木及病害的综合治理具有很高的应用价值。     

利用PCR检测柑桔黄龙病病原研究(英文)

对引起柑桔黄龙病的类菌原体（ＢＬＯ）的ＰＣＲ检测方法进行了研究．结果表明,利用ＰＣＲ方法不但能对ＢＬＯ、ＤＮＡ进行离体扩增,而且对各种不同来源的感病植物样品也能很好地进行检测．本文首次报道了从未显症的植物上用ＰＣＲ方法检测到了ＢＬＯ,技接试验证实了该方法的可靠性．正是由于本方法可在病原侵染早期进行检测这一优点,从而可控制带病苗木的扩散．该技术也可被用于无毒苗木的筛选,从而控制病害发生．该技术对ＢＬＯ引起的黄龙病的检测不但简单、有效而且实用     

柑橘栽培品种（系）DNA指纹图谱库的构建

 【目的】构建柑橘栽培品种（系）的DNA指纹图谱数据库,为建立柑橘种苗纯度及真实性鉴定技术体系和技术规范奠定基础。【方法】利用SSR标记和ISSR标记对102个柑橘栽培品种（系）进行DNA指纹分析,筛选适合的特征引物,构建柑橘栽培品种（系）的指纹图谱数据库。【结果】从200对SSR引物中筛选到重复性好、多态性丰富的12对引物作为柑橘品种（系）鉴定的特征引物,12对SSR特征引物组合可鉴别42个品种（系）;在此基础上,对未出现SSR特征指纹的60个品种（系）进行ISSR指纹分析,从40个ISSR引物中筛选到2个可用于品种鉴定的特征引物,结合SSR标记,可快速、准确地鉴别70个柑橘的品种（系）。并利用这12对SSR特征引物和2个ISSR特征引物构建了70个柑橘栽培品种（系）的DNA特征指纹图谱数据库。【结论】SSR和ISSR标记适于构建柑橘栽培品种DNA指纹图谱库;指纹图谱库的建立为柑橘种苗纯度及真实性鉴定奠定了基础。     

柑桔黄龙病PCR检测技术体系优化分析

根据柑桔黄龙病病菌亚洲种16S rDNA基因序列设计特异引物,建立了检测柑桔黄龙病的快速PCR方法,并研究了PCR体系的各主要组分对PCR反应的影响,确立了黄龙病菌的PCR检测优化体系。应用PCR检测方法对大田植株及柑桔脱毒苗进行抽样检测,结果表明:PCR检测优化体系具有快速、灵敏度高、重复性好、检测成本低等优点,适用于柑桔黄龙病的早期诊断及无病毒苗木的大量检测。     

四种杂柑全果饮品的加工适应性与贮藏特性

为比较胡柚、瓯柑、高橙和甜橘柚等4种杂柑制成全果饮品的加工适应性与贮藏特性,筛选适合全果饮品加工的杂柑品种,将4种杂柑按课题组前期工艺加工成全果饮品,于40 ℃避光保存,每隔20 d取样用于抗坏血酸、总酸、总酚含量、抗氧化能力、色泽和黏度等指标检测,并对前3次的样品进行感官评价。贮藏期间,各品种的抗坏血酸含量下降,总酸含量基本保持不变,黏度、总酚含量和抗氧化能力先升后降,色泽均加深且色差趋于相近。高橙的抗坏血酸、总酸和总酚含量比其他品种高。不同品种杂柑的全果饮品各有利弊:甜橘柚适用于开发年轻化产品;胡柚和高橙含有较高的活性成分,适用于功能饮品开发,适当贮藏后口感得到提升;瓯柑适用于开发成老幼皆宜的产品。     

‘Extended spectral angle mapping (ESAM)’ for citrus greening disease detection using airborne hyperspectral imaging

Hyperspectral (HS) imaging is becoming more important for agricultural applications. Due to its high spectral resolution, it exhibits excellent performance in disease identification of different crops. In this study, a novel method termed ‘extended spectral angle mapping (ESAM)’ was proposed to detect citrus greening disease (Huanglongbing or HLB), which is a very destructive disease of citrus. Firstly, the Savitzky–Golay smoothing filter was used to remove spectral noise within the data. A mask for tree canopy was built using support vector machine, to separate the tree canopies from the background. Pure endmembers of the masked dataset for healthy and HLB infected tree canopies were extracted using vertex component analysis. By utilizing the derived pure endmembers, spectral angle mapping was applied to differentiate between healthy and citrus greening disease infected areas in the image. Finally, most false positive detections were filtered out using red-edge position. An experiment was carried out using an HS image acquired by an airborne HS imaging system, and a multispectral image acquired by the WorldView-2 satellite, from the Citrus Research and Education Center, Lake Alfred, FL, USA. Ground reflectance measurement and coordinates for diseased trees were recorded. The experimental results were compared with another supervised method, Mahalanobis distance, and an unsupervised method, K-means, both of which showed a 63.6 % accuracy. The proposed ESAM performed better with a detection accuracy of 86 % than those two methods. These results demonstrated that the detection accuracy using HS image could be enhanced by focusing on the pure endmember extraction and the use of red-edge position, suggesting that there is a great potential of citrus greening disease detection using an HS image.     

柑橘品种鉴定的SSR标记开发和指纹图谱库构建

【目的】筛选出一套SSR核心引物,用于构建柑橘品种的DNA指纹图谱库,为柑橘种苗认证、品种登记和植物新品种保护提供技术支撑。【方法】以柑橘属8个主要栽培种类为试材进行PCR扩增,扩增产物经琼脂糖凝胶电泳检测,筛选出多态性丰富、扩增条带稳定的引物对部分参试品种进行PCR扩增,扩增产物通过变性聚丙烯酰胺凝胶电泳进行进一步的引物筛选;筛选得到的引物进行荧光标记,并选用部分柑橘品种进行PCR扩增,扩增产物利用基因分析仪检测,分别计算各引物的PIC值、等位基因数目,选取一套多态性丰富的适合进行荧光毛细管电泳检测的SSR引物组合,进而对所有参试品种进行分析,构建柑橘品种的DNA指纹图谱库,同时利用筛选的SSR标记对参试品种进行鉴定。【结果】初期以柑橘属8个种的代表品种(太田椪柑、沙田柚、沅江酸橙、大红甜橙、邓肯葡萄柚、枸橼、费米耐劳柠檬和墨西哥来檬)为材料,用362对SSR引物进行PCR扩增,扩增产物经4%琼脂糖凝胶电泳检测,初步筛选出80对种间多态性高、扩增稳定的SSR引物;进一步从8个种类里选择不同类型的64个柑橘品种,用上述筛选出的80对SSR引物进行PCR扩增,扩增产物经6%变性聚丙烯酰胺凝胶电泳检测,再筛选出60对品种类型间多态性高、扩增稳定、条带清晰的SSR引物,并分别进行荧光标记;另以168份柑橘品种为材料对上述60对SSR引物进行PCR扩增,经荧光毛细管电泳检测,依据各引物的PIC值、等位基因数目、峰图读取难易程度、扩增稳定性及染色体位置等,最终筛选出21对SSR引物作为指纹图谱库构建的核心引物。为消除不同仪器、不同试验批次等引起的误差,为21对SSR引物各主要等位变异选择相应的参照品种。根据各引物标记的荧光颜色及扩增片段大小进行合理组合,21对SSR引物可分成5组进行多重电泳,共采集500份柑橘品种的DNA指纹。利用SSR标记,有111份品种仅用1对引物即可鉴别,86份品种用2对引物组合即可鉴别,24份品种用3对引物组合即可鉴别,另外279份品种虽无法单独一一鉴别,但可分为87个小组,组内品种无法区分,组间品种易鉴别。【结论】利用筛选出的21对SSR核心引物,构建了包含500份柑橘品种的DNA指纹图谱库,筛选出部分品种的特异标记,可以鉴别221份柑橘品种和87个品种组合,构建了基于毛细管电泳平台的柑橘SSR分子标记品种鉴定体系。     

基于光谱信息的柑橘黄龙病无损检测及分级模型构建

柑橘黄龙病(Huanglongbing,HLB)是世界柑橘生产上最具毁灭性的病害,给果农和相关产业造成了巨大的损失.以柑橘叶片为载体,利用高光谱图像技术采集柑橘叶片表面的高光谱图像,用ENVI4.7进行图像处理,提取感兴趣区域(Region of Intest,ROI),统计感兴趣区域平均光谱数据,并进行相关植被植物的运算,最后通过PLS-DA(Partial Least Squares Discrimination Analysis)判别法进行鉴别并分类.结果表明:基于平均光谱值和植被指数的PLS-DA判别模型都能对健康、缺锌和HLB叶片进行鉴别.其中基于平均光谱值的PLS-DA模型鉴别健康柑橘叶片样品的灵敏度为100％,特异度为100％,准确度为100％;鉴别缺锌柑橘叶片样品的灵敏度为80.6％,特异度为91.7％,准确度为88.9％;鉴别HLB叶片的灵敏度为89.3％,特异度为88.3％,准确度为88.9％.基于植被指数的PLS-DA判别模型鉴别健康柑橘叶片样品的灵敏度为100％,特异度为100％,准确度为100％;鉴别缺锌柑橘叶片样品灵敏度为92.5％,特异度为89.3％,准确度为90.1％;鉴别HLB叶片的灵敏度为86.4％,特异度为95.3％,准确度为90.1％.识别正确率较高,说明利用高光谱进行柑橘黄龙病病情分类是可行的.     

模糊聚类分析在色木孔洞缺陷位置声波检测中的应用

为了实现木材孔洞缺陷位置的检测,提出了一种基于模糊聚类分析的新型木材声波无损检测方法。针对端部孔、无孔洞和中间孔木材试件,运用敲击法采集木材声波信号,提取时频特征向量作为样本数据,运用基于传递闭包的模糊相似矩阵对训练样本进行聚类分析,建立不同类别的模糊模式库,采用最大隶属度原则对待测样本进行识别。结果表明:此方法克服了模糊聚类单一分析方法的不确定性,实现了多指标定量化的检测;该方法能够有效地对色木孔洞缺陷位置进行检测,且准确率较高,检测端部孔样本的准确率为84%,无孔洞样本准确率为94%,中间孔样本准确率为92%。     

湖北公安县柑橘果实品质分析与模糊综合评判

对公安县新近引进栽培的12个主要柚类品种和9个杂柑品种进行品质测定,并依据可溶性固形物、总酸、固酸比、可食率、含水量、Vc等6个主要品质指标,运用模糊综合评判法对其进行综合评定。结果表明,不同品种在主要品质指标上存在较大差异,柚类品种以沙田柚、永嘉早香柚红肉溪蜜柚、福建溪蜜柚表现较优,杂柑品种中以山下红、不知火、南香和秋辉表现较优。建议对柑橘果实品质进行客观评价时使用模糊综合评判法。     

春甜桔、柠檬和葡萄柚在广州地区的引种表现及栽培技术

对春甜桔、尤力克柠檬和星路比葡萄柚在广州"南肺"果树保护区的引种表现试验结果表明,3个水果品种均表现生长快、早结丰产、品质好,适宜在广州地区推广种植;同时还介绍了春甜桔、尤力克柠檬和星路比葡萄柚的主要栽培技术.     

检疫性柑橘冬生疫霉和丁香疫霉的三重 PCR 同步检测

全世界危害柑橘的疫霉有14种,其中冬生疫霉Phytophthora hibernalis和丁香疫霉Phytophthora syringae为我国规定禁止进境的两种检疫性柑橘真菌病害,目前尚未见有此两种病原菌的同步分子检测报道.该研究根据14种柑橘疫霉的18SrRNA,ITS,heat shock protein 90(HSP 90)等基因分别设计了疫霉属的通用引物和此两种检疫性疫霉的特异引物,通过进行反应体系优化,建立了同时检测柑橘上两种检疫性疫霉的特异三重PCR检测方法,并进行了灵敏度试验.用15个柑橘疫霉菌株与甜橙的混合DNA做模板进行模拟带菌试验,结果表明该三重PCR分子检测能实现两种检疫性疫霉菌的同步特异检测,这两种检疫性柑橘疫霉提供了特异、可靠、便捷、适于口岸应用的检测方法,可有效促进柑橘类水果的快速通关.     

哒螨灵在柑桔及土壤中残留的气相色谱测定

[目的]建立哒螨灵在柑桔及土壤中残留的气相色谱分析方法,为评价哒螨灵在柑桔上使用的安全性提供科学依据.[方法]样品以甲醇提取,经石油醚液—液萃取和弗罗里硅土柱净化后,用带μ-ECD的气相色谱仪进行测定.[结果]桔皮、桔肉、全果及土壤样品中哒螨灵的最低检出浓度均为0.001 mg/kg,平均回收率为84.78%~101.18%,变异系数为2.31%~8.68%.[结论]建立的方法操作简便,分离效果好,准确度和精密度均满足农药残留分析的技术要求,可用于柑桔及土壤中哒螨灵残留量的检测.     

Citrus fruits maturity detection in natural environments based on convolutional neural networks and visual saliency map

Citrus fruits do not ripen at the same time in natural environments and exhibit different maturity stages on trees, hence it is necessary to realize selective harvesting of citrus picking robots. The visual attention mechanism reveals a physiological phenomenon that human eyes usually focus on a region that is salient from its surround. The degree to which a region contrasts with its surround is called visual saliency. This study proposes a novel citrus fruit maturity method combining visual saliency and convolutional neural networks to identify three maturity levels of citrus fruits. The proposed method is divided into two stages: the detection of citrus fruits on trees and the detection of fruit maturity. In stage one, the object detection network YOLOv5 was used to identify the citrus fruits in the image. In stage two, a visual saliency detection algorithm was improved and generated saliency maps of the fruits; The information of RGB images and the saliency maps were combined to determine the fruit maturity class using 4-channel ResNet34 network. The comparison experiments were conducted around the proposed method and the common RGB-based machine learning and deep learning methods. The experimental results show that the proposed method yields an accuracy of 95.07%, which is higher than the best RGB-based CNN model, VGG16, and the best machine learning model, KNN, about 3.14% and 18.24%, respectively. The results prove the validity of the proposed fruit maturity detection method and that this work can provide technical support for intelligent visual detection of selective harvesting robots.

     

Immature green citrus fruit detection and counting based on fast normalized cross correlation (FNCC) using natural outdoor colour images

A fast normalized cross correlation (FNCC) based machine vision algorithm was proposed in this study to develop a method for detecting and counting immature green citrus fruit using outdoor colour images toward the development of an early yield mapping system. As a template matching method, FNCC was used to detect potential fruit areas in the image, which was the very basis for subsequent false positive removal. Multiple features, including colour, shape and texture features, were combined in this algorithm to remove false positives. Circular Hough transform (CHT) was used to detect circles from images after background removal based on colour components. After building disks centred in centroids resulted from both FNCC and CHT, the detection results were merged based on the size and Euclidian distance of the intersection areas of the disks from these two methods. Finally, the number of fruit was determined after false positive removal using texture features. For a validation dataset of 59 images, 84.4 % of the fruits were successfully detected, which indicated the potential of the proposed method toward the development of an early yield mapping system.     

Citrus disease recognition based on weighted scalable vocabulary tree

Citrus Huanglongbing (HLB) is a destructive disease in citrus production that causes huge economic damage to citrus producers and related industries in the world. Early and accurate detection of HLB is a critical management step to control the spread of this disease. However, existing HLB detection methods cannot be widely adopted in citrus production due to long-time and high-cost detection period in specific laboratory environments. In view of this, a fast-response and low-cost computer vision technique is investigated for diagnosing HLB in citrus leaves. Specifically, the Gaussian mixture density (GMD) is performed to extract the leaf object from the citrus image, followed by the feature extraction and recognition of the existence of HLB in the leaf based on scalable vocabulary tree. A citrus leaf image dataset is constructed, and the experimental results show that the proposed HLB recognition method with GMD object extraction performs 95–100 % accuracy within 1 s.     

INA细菌种类及其冰核活性与“不知火”果实霜冻关系的研究

根据四川省杂柑上采集受冻标本20份,分离到16株冰核细菌菌株,经细菌学鉴定确认属于2个属3个种:E.ananas Serrano1928,6株,占37.5%;P.syringaevan Hall1902,6株,占37.5%;P.viridiflava(Burkholder1930)Dowson1939,4株,占25%。且E.ananas的冰核活性小于P.syringae和P.viridiflava。这是杂柑上关于INA细菌的首次报道。并且通过研究INA细菌与"不知火"果实霜冻关系结果表明,在低温胁迫下,INA细菌能较大幅度提高果皮相对电导率,增大细胞原生质膜渗透性,是诱发和加重"不知火"果实霜冻的重要因素。