Identification of Neofabraea species causing bull's eye rot of apple in Poland and their direct detection in apple fruit using multiplex PCR

Based on partial sequence analysis of the β‐tubulin gene, 19 isolates of fungi causing bull's eye rot on apple in Poland were classified into species: Neofabraea alba, N. perennans and N. kienholzii. To the authors’ knowledge, the detection of N. kienholzii is the second in Europe and the first in Poland. Species affiliation of these fungi was confirmed by a new species‐specific multiplex PCR assay developed on the basis of previously published methods. The new protocol allowed for the specific identification of bull's eye rot‐causing species, both from pure cultures and directly from the skin of diseased or apparently healthy apples. In 550 samples of diseased fruits collected from nine cold storage rooms located in three regions of Poland, in 2011 and 2012, N. alba was detected as the predominant species causing bull's eye rot, occurring on average in 94% of the tested samples. Neofabraea perennans was found in a minority of apple samples, N. kienholzii was found only in two apple samples, while N. malicorticis was not detected in any sample tested. In tests on 120 apparently healthy fruits, only N. perennans was detected in a single sample. The results of genetic diversity analyses of bull's eye rot‐causing fungi based on the β‐tubulin gene sequence and an ISSR (inter‐simple sequence repeat) PCR assay with two primers were consistent, showing the expected segregation of tested isolates with respect to their species boundaries. However, the genetic distance between N. perennans and N. malicorticis was very low, as reported previously.     

病原菌诱导黄瓜对其主要病害的抗病反应

以5种黄瓜主要病原菌作为诱导抗病因子,研究其对黄瓜主要病害的作用,结果发现黄瓜经病原菌诱导后,可以产生对诱导病原菌及其它病原菌引起病害的交互保护作用,并且诱导的交互保护作用与诱导浓度、诱导间隔期、不同品种存在相关性.诱导效果不随诱导接种浓度的升高而升高.在黑星病菌对霜霉病菌的诱导作用中,以浓度为1×102个/ml的黑星病菌孢子悬浮液的诱导效果最好,诱导间隔期为48h黄瓜黑星病菌对霜霉病的交互保护作用最明显,抗性品种的交互保护作用明显好于感病品种;炭疽病菌可诱导黄瓜有效抑制褐斑病的发生,但挑战接种褐斑病菌后,却促进了炭疽病的发生.诱导接种炭疽病菌后再挑战接种褐斑病菌12d,对褐斑病的防效为92.07%.     

田阳县水稻纹枯病病情指数与病株率关系数学模型的初步研究

依据田阳县2002—2012年水稻纹枯病观测田的病情系统调查记载表数据,采用分层随机抽样法抽取100对数据,构建了病情指数y与病株率x数学预测模型y=0．2919x-0．1478（％,0〈x〈20）,应用于预测预报时可减少调查工作量,提高工作效率。     

Periodicity and gradients in dispersal ofAlternaria linicola in linseed crops

Conidia ofAlternaria linicola produced on infected linseed crops were mainly dispersed by wind. The numbers of conidia in the air above linseed crops collected by a Burkard spore sampler were greatest between 1200 h and 1300 h, when the relative humidity was lowest. Although numbers of conidia collected decreased with increasing height within and above the crop canopy, air-borneA. linicola conidia were present up to 80 cm above the crop canopy. Conidia ofA. linicola were transported by wind up to at least 40 m downwind from an artificial line inoculum source, but their numbers decreased with increasing distance from the source. In 1991, 1992, and 1993, the dispersal ofA. linicola conidia above linseed crops followed a seasonal periodicity which was influenced by weather conditions and cultural practices. The greatest numbers of conidia were collected during July, August and early September and coincided with periods favourable for sporulation and with an increase in the incidence of the disease in the senescent crop. Air-borneA. linicola conidia produced on point or line inoculum sources (naturally infected linseed stem debris) were responsible for the spread of the disease in linseed crops. In 1992 and 1993, the disease was first detected downwind from the sources, but by the end of the growing seasons, it had spread in all directions and up to 20 m and 60 m from the sources, respectively. Disease gradients were initially steep near the inoculum sources but they became flatter with time due to the secondary spread of the disease.     

Effect of apple cultivars and storage periods on the virulence of Neofabraea spp.

Bull’s eye rot is a typical quiescent postharvest apple disease in major fruit-growing areas. The susceptibility of different apple cultivars to Neofabraea spp. (N. vagabunda and N. malicorticis) was assessed, with Granny Smith showing the most resistance and Cripps Pink the most susceptibility. To assess the factors involved in conidial germination, Neofabraea spp. were grown on crude protein extracts (CPEs) collected from apple fruits at different storage periods. Fungal germ tube growth rate and pathogenic enzyme (cellulase and xylanase) activity were assessed. Results showed that CPEs collected after 2 and 4 months of storage progressively stimulated conidial germination and germ tube elongation, while a lesser effect was observed from CPEs after 1 month of storage. Xylanase proved to be the main degrading enzyme secreted by all the isolates, while cellulase was produced only by N. vagabunda isolates. Overall, the isolate ID02 was the most virulent, based on more rapid germ tube elongation and greater activity of the lytic enzymes.     

Automated identification of sugar beet diseases using smartphones

Cercospora leaf spot (CLS) poses a high economic risk to sugar beet production due to its potential to greatly reduce yield and quality. For successful integrated management of CLS, rapid and accurate identification of the disease is essential. Diagnosis on the basis of typical visual symptoms is often compromised by the inability to differentiate CLS symptoms from similar symptoms caused by other foliar pathogens of varying significance, or from abiotic stress. An automated detection and classification of CLS and other leaf diseases, enabling a reliable basis for decisions in disease control, would be an alternative to visual as well as molecular and serological methods. This paper presents an algorithm based on a RGB‐image database captured with smartphone cameras for the identification of sugar beet leaf diseases. This tool combines image acquisition and segmentation on the smartphone and advanced image data processing on a server, based on texture features using colour, intensity and gradient values. The diseases are classified using a support vector machine with radial basis function kernel. The algorithm is suitable for binary‐class and multi‐class classification approaches, i.e. the separation between diseased and non‐diseased, and the differentiation among leaf diseases and non‐infected tissue. The classification accuracy for the differentiation of CLS, ramularia leaf spot, phoma leaf spot, beet rust and bacterial blight was 82%, better than that of sugar beet experts classifying diseases from images. However, the technology has not been tested by practitioners. This tool can be adapted to other crops and their diseases and may contribute to improved decision‐making in integrated disease control.     

基于深度学习的病虫害智能化识别系统

我国农作物种植覆盖面广、分散度高,病虫害发生种类多、区域性发生规律复杂,传统的人工鉴定技术从效率、能力与精度方面均难以满足新形势下重大病虫测报要求。针对这一实践需求,以测报灯下害虫图像数据库(约18万张)、田间病虫害图像数据库(约32万张)为基础,构建了基于深度学习方法的病虫害种类特征自动学习、特征融合、识别和位置回归计算框架,并研发了移动式病虫害智能化感知设备和自动识别系统。通过近2年的精确度和实操运行效率检验,该系统在自然状态下对16种灯下常见害虫的识别率为66%～90%,对38种田间常见病虫害(症状)的识别率为50%～90%。随基础数据库的不断丰富、神经网络深层特征提取的不断完善,该系统有望进一步提高识别准确率,从而真正实现田间病虫害识别自动化、智能化和高效率。     

改进杂交晚稻育秧方式预防水稻黑条矮缩病

对比试验结果表明,杂交晚稻秧田不施或少施基肥和苗肥,培育青色、青黄色健壮秧苗,适当增加大田插本数,可以显著减少水稻黑条矮缩病发病率,增加产量。在对照区平均丛发病率12.8%,平均单产435.5kg/亩情况下,秧田不施肥,大田每丛插2本苗的丛发病率可控制在1%左右,亩产增加100kg上下。     

土壤消毒在辽宁省设施蔬菜土传病害上的应用

设施蔬菜生产面积不断扩大,由于农民过分追求产量和效益,对土壤过度消耗,导致设施蔬菜生产中土传病害越来越严重.解决土传病害成了设施蔬菜生产中的一大难题,选择抗病品种、农业防治、土壤消毒等方法都是解决土传病害的重要途径.该文详细介绍了土壤消毒技术的作用原理、操作方法,科学分析了土壤消毒技术在实际应用中存在的问题以及正确使用...     

桃红叶植原体检测及鉴定

对表现红叶的桃植株进行植原体16SrRNA基因PCR扩增,得到1.2kb的特异片段.将此片段与pGEM T Easy载体连接并转化到大肠杆茵DH5α感受态细胞中.通过酶切、PCR鉴定,对筛选得到的重组阳性克隆进行序列测定及同源性比较分析,确定该株系属于翠菊黄化植原体组(Aster yellows group,16SrI).在国内首次报道了翠菊黄化组中的植原体侵染桃树.