Inventarisation of natural enemies in witloof chicory fields: preliminary results

Due to residues on the witloof chicory heads the use of insecticides is forbidden during forcing. At this moment a suitable chemical control of the leafminer Napomyza cichorii is carried out in the field, based on the phenology and the population density and taking into account the economic threshold level. No chemical treatment is recognized against the root aphid Pemphigus bursarius. For monitoring these pests during the growing season water traps were placed on observation fields, scattered over the Belgian endive production area. The large numbers and the wide variety of other insects caught in the traps in recent years indicate that there is a potential of natural enemies. An inventarisation of the native enemies in witloof chicory fields was started recently using yellow-coloured water traps and pitfall traps; at the same time field observations were done. In all the observation fields we found besides Carabidae and Staphilinidae also Coccinellidae and Syrphidae in large numbers. The numbers and the diversity of Hymenoptera in the water traps were very large. Apart from the field observations witloof chicory heads were investigated during harvest. Parasitized pupae were collected and kept in a climate room. Dacnusa leptogaster (Braconidae), a specific parasite of the witloof chicory fly, hatched. This parasite was also found in the water traps during the growing season.     

6种颜色粘虫板对春季菜地昆虫的诱集效应

为筛选对春季蔬菜害虫具有较好诱集效果的粘虫板,促进蔬菜害虫绿色防控技术的研究与推广,采用红色、黄色、绿色、蓝色、白色和黑色6种颜色的市售粘虫板对菜地昆虫进行诱集试验.结果显示,粘虫板诱集的害虫以尖眼蕈蚊Bradysia minpleuroti、小菜蛾Plutella xylostella、端大蓟马Megalurothrips distalis和黄蓟马Thrips flavus为优势种;黄曲条跳甲Phyllotreta striolata、南美斑潜蝇Liriomyza huidobrensis、灰地种蝇Delia platura、棉蚜Aphis gossypii、小绿叶蝉Empoasca flavescens、菜粉蝶Pieris rapae为常见种.试验中蓝色粘虫板诱集的灰地种蝇的数量显著高于其它处理组(P<0.05);黄色粘虫板诱集的南美斑潜蝇、棉蚜、小绿叶蝉的数量显著高于其它处理组(P<0.05);蓝色和白色粘虫板诱集的端大蓟马、黄蓟马的数量显著高于其它处理组(P<0.05);蓝色粘虫板诱集到的尖眼蕈蚊的数量显著低于其它处理组(P<0.05).各种颜色粘虫板诱集昆虫的益害比较低,诱集益虫总数仅占诱集昆虫总数的1.64％,表明该时段使用粘虫板防治菜地害虫,对益虫伤害较小,对保护利用天敌昆虫具有重要意义.本研究结果为科学使用粘虫板防治菜地害虫提供理论依据.     

昆虫趋色性及诱虫色板的研究和应用进展

昆虫与植物之间色彩通讯是彼此信息交流的渠道之一,基于害虫趋色性研发的诱虫板作为一种绿色防控手段,已广泛用于农林害虫的虫情监测、预测预报、大量诱杀以及天敌诱集和指引,效果良好.本文概述了昆虫趋光趋色机理、诱虫板诱杀害虫机制和诱虫板色彩、形状、大小、高度、密度、方向、诱虫时长、植株形态和害虫生理状态等对于诱捕效果的影响,总结了诱虫板在茶园、菜地和大棚等作物环境中的多种实用技术,解析了诱虫板与昆虫性诱剂、植物源引诱剂等配合使用方法及其功效,评价了诱虫板治虫的优缺点并提出改进措施,讨论了诱虫板诱效的评价方法和成本核算.展望了新一代诱虫板研发方向、诱虫板与信息素等产品在有害生物综合治理(IPM)中的集成应用和诱虫板产业前景.     

Bacterial Diseases on Witloof Chicory Crops in Europe

Distribution of principal phytopathogenic bacterial populations on witloof chicory crops in Europe is reported. Different symptoms caused by phytopathogenic bacterial populations on different parts of witloof chicory plants are described. The first report of Pseudomonas fluorescens (Trevisan) Migula and occurrence of Erwinia carotovora (Jones, 1901) bacterial populations on witloof chicory cv. Rosso di Chioggia crops in Italy (Latium region), are reported. Risks of disease during different phases of production of this crop and phytosanitary measures to reduce the incidence of disease caused by these pathogens are discussed.     

Efficient monitoring of maize orange leafhopper, <Emphasis Type="Italic">Cicadulina bipunctata</Emphasis> (Hemiptera: Cicadellidae), and small brown planthopper, <Emphasis Type="Italic">Laodelphax striatellus</Emphasis> (Hemiptera: Delphacidae), in forage maize fields using yellow sticky traps

The monitoring of insect pests in fields of forage maize is difficult because plants are tall and grow at a high density. We investigated the effectiveness of colored sticky traps and appropriate conditions for monitoring insect pests in forage maize fields. Large numbers of the maize orange leafhopper, Cicadulina bipunctata Melichar (Hemiptera: Cicadellidae), and the small brown planthopper, Laodelphax striatellus Fallen (Hemiptera: Delphacidae), were collected during the experimental period with yellow and blue sticky traps placed in summer crop forage maize fields. A greater number of insects were trapped in yellow traps relative to blue traps. Traps located at a lower height (40 cm above the ground) attracted larger numbers of C. bipunctata, whereas L. striatellus did not demonstrate a height-dependent preference. These results indicated that yellow-colored sticky traps located at low height are effective for collecting C. bipunctata and L. striatellus simultaneously. Seasonal occurrence data obtained by the yellow sticky traps showed clearer seasonal occurrences than that obtained by two previously developed methods, suction and light traps, indicating that sticky traps are effective for monitoring the seasonal occurrence of these two insects in forage maize fields.     

Machine vision algorithm for whiteflies (Bemisia tabaci Genn.) scouting under greenhouse environment

One of the main problems in greenhouse crop production is the presence of pests. Detection and classification of insects are priorities in integrated pest management (IPM). This document describes a machine vision system able to detect whiteflies (Bemisia tabaci Genn.) in a greenhouse by sensing their presence using hunting traps. The extracted features corresponding to the eccentricity and area of the whiteflies projections allow to establish differences among pests and other insects on both the trap surfaces and dust generated artefacts. Because of whiteflies geometrical characteristics, it was possible to design an efficient (related to manual counting) machine vision algorithm to scout and count units of this pest within a greenhouse environment. These algorithm results show high correlation indexes for both, sticky screens (R² = 0.97) and plant leaf situations (R² = 1.0). The machine vision algorithm reduces the scouting time and the associated human error for IPM‐related activities.     

植物-昆虫间的化学通讯及其行为控制

在植物与昆虫间的化学通讯中植物气味物质起着决定性的作用,它调控着昆虫的多种行为,诸如引诱昆虫趋向寄主植物,刺激昆虫取食,引导昆虫选择产卵场所,进行传粉和防御昆虫等。有些植物则当受到食植性昆虫危害时会释出一些引诱害虫天敌的化学信号。这些化学信号是一些挥发性萜类混合物,天敌昆虫就以此来区分受害和未受害植株。尽管目前在害虫综合治理中,昆虫信息素的应用越来越显得比天然植物气味源更受重视,但是必须指出的是,昆虫信息化合物首次成功地使用于植物保护的却是天然植物气味源。在利用植物气味源作害虫测报和防治中,近年来一种简单价廉的粘胶诱捕器己成为多种害虫的标准测报工具。在害虫综合治理中利用植物气味源的技术显然是具有不可估量的潜力。文中提出了利用基因工程技术来改造植物,使植物能释放特定的驱避剂或其它控制昆虫行为的特殊气味物质的新概念。     

秋末苏南茶园昆虫的群落组成及其趋色性

苏南地区名茶荟萃,而虫害历来较为严重。秋末选丹阳市一片无公害茶园,使用纯白、桃红、墨绿、果绿、湖蓝、天蓝、素馨黄、芽绿、土黄、桔黄、大红和紫色12种粘性色板诱虫。结果表明:① 4日内捕获7目42科85种30455头昆虫,其中优势类群是同翅目、膜翅目和双翅目,三者个体数分别占总个体数的86.5%、8.8%和2.5%。②主要害虫是假眼小绿叶蝉和茶蚜,分别占总个体数的15%和71.5%。③ 捕获的中华蜜蜂占总个体数8.2%。④捕获天敌昆虫1034头,其中,瓢虫类32头,占3.1%,主要种类是异色瓢虫和黄斑盘瓢虫;草蛉类582头,占56.3%,包括中华草蛉、大草蛉和丽草蛉;伞裙追寄蝇和蚕饰腹寄蝇等5种寄生蝇类111头,占10.7%;门氏食蚜蝇和黑带食蚜蝇等7种食蚜蝇类110头,占10.6%;螟蛉瘤姬蜂和花胸姬蜂等5种姬蜂类、茶尺蠖绒茧蜂和单白绵绒茧蜂等7种茧蜂类个体数分别占3.9%和9.4%。⑤ 芽绿、素馨黄和桔黄色板显著地引诱假眼小绿叶蝉和茶蚜;芽绿、素馨黄色板显著地引诱姬蜂和茧蜂;芽绿、土黄和果绿色板明显地引诱草蛉类;果绿、天蓝和紫色色板引诱较多的蝇类和蚊类等双翅目昆虫;素馨黄引诱的各类昆虫种数最多;纯白板上各类昆虫的多样性指数最大,表明其对许多昆虫都有引诱效应。秋末时节查明即将越冬的害虫和天敌昆虫种类、数量和益害比、以及优势种害虫和优势种天敌数量,探明多种色彩引诱益、害虫的差异,对于有效实施无公害封园防治,以压低越冬基数有重要意义。     

Analyses of mitochondrial DNA structure and expression in three cytoplasmic male-sterile chicories originating from somatic hybridisation between fertile chicory and CMS sunflower protoplasts

Cytoplasmic male-sterile (CMS) chicories have been previously obtained by somatic hybridisation between fertile industrial chicory protoplasts and CMS sunflower protoplasts. In this study, we compared three different CMS chicory cybrids that originated from three different fusion events. The cybrids were backcrossed with different witloof chicories in order to transfer the three male-sterile cytoplasms from an industrial chicory nuclear environment to a witloof chicory nuclear context. Southern hybridisation, using different mitochondrial genes as probes, revealed that the three cybrid mitochondrial genomes were different and that they were stable throughout backcrossing generations regardless of the pollinator. However, pollinators were found to influence floral morphologies – with one being able to restore fertility – showing that nuclear context can affect the sterility of the cybrids. PCR and RFLP analyses revealed that the orf522 sequence, responsiblefor CMS in PET1 sunflower, was present in two out of the three cytoplasms studied, namely 411 and 523, but was absent from the other cytoplasm, 524. We thus concluded that orf522 is not responsible for CMS in the 524 cybrid. Although the orf522 gene is present in the 411 and 523 cytoplasms, it is probably not responsible for the sterile phenotype of these cybrids. Received: 3 June 1998 / Accepted: 30 April 1999     

海南省降香黄檀害虫种类、分布及危害症状调查

【背景】降香黄檀被确定为海南省省树后,种植面积迅速增大,但对于其病虫害的监测与防控方面的研究较少。【方法】采用人工采集结合灯光诱集的方法,调查降香黄檀种植区的害虫种类,同时对其危害症状进行了详细描述。【结果】海南降香黄檀害虫共计19种,确定了其中18种的学名,并掌握了各害虫的分类学地位、危害部位及症状。害虫在降香黄檀植株各个组织部位均有分布,其中以叶部的分布数量较大,茎干次之。苗期以食叶性害虫居多;5年生以内的植株上蛀干害虫数量显著增多;老龄植株上的茎干害虫较少。【结论与意义】海南省降香黄檀各生育期均有害虫危害,日后仍需加强对其害虫监测与防控技术的研究。     

Survey of predators and sampling method comparison in sweet corn

Natural predation is an important component of integrated pest management that is often overlooked because it is difficult to quantify and perceived to be unreliable. To begin incorporating natural predation into sweet corn, Zea mays L., pest management, a predator survey was conducted and then three sampling methods were compared for their ability to accurately monitor the most abundant predators. A predator survey on sweet corn foliage in New York between 1999 and 2001 identified 13 species. Orius insidiosus (Say), Coleomegilla maculata (De Geer), and Harmonia axyridis (Pallas) were the most numerous predators in all years. To determine the best method for sampling adult and immature stages of these predators, comparisons were made among nondestructive field counts, destructive counts, and yellow sticky cards. Field counts were correlated with destructive counts for all populations, but field counts of small insects were biased. Sticky cards underrepresented immature populations. Yellow sticky cards were more attractive to C. maculata adults than H. axyridis adults, especially before pollen shed, making coccinellid population estimates based on sticky cards unreliable. Field counts were the most precise method for monitoring adult and immature stages of the three major predators. Future research on predicting predation of pests in sweet corn should be based on field counts of predators because these counts are accurate, have no associated supply costs, and can be made quickly.     

973项目“重大农业害虫猖獗危害的机制及可持续控制的基础研究”简介

2006年9月,国家重点基础研究发展计划(973)农业领域2006年度项目“重大农业害虫猖獗危害的机制及可持续控制的基础研究”经科技部批准正式立项启动。该项目以全系统管理思想为指导,在基因、个体、种群、生态系统等不同层次,阐明我国重大农业害虫种群分化与暴发的分子基础,解析害虫与寄主作物及天敌间的相互作用机制,建立害虫监测与预警系统,提出重大农业害虫可持续控制的新途径和新方法,为我国农业减灾、经济的可持续发展奠定科学基础。项目的主要研究内容包括:害虫生长发育与生殖调控的分子机制;害虫对环境胁迫的适应机制;杀虫药剂诱导害虫再猖獗的机制;害虫与寄主植物的协同进化;天敌与害虫的互作及控害机制;作物-害虫-天敌食物网关系及其调控机理;重大害虫区域性暴发监测与预警。项目的总体目标为:阐明害虫生长发育、种群分化的分子基础,揭示害虫种群调节的内在机制;解析作物、害虫及天敌间的互作机制,丰富和发展植物-害虫-天敌协同进化理论;阐明主要害虫区域性灾变机理,发展害虫预警新技术;发展与环境相容的、增强自然控害功能的新技术,提出重大农业害虫可持续控制的新途径和新方法;凝炼一支害虫控制基础研究的创新团队,丰富和发展我国害虫管理的科学理论与实践,提升我国有害生物防控的原始创新和集成创新能力,扩大国际影响。     

不同颜色黏虫板对小绿叶蝉的诱集效果评价

小绿叶蝉为果园主要害虫之一.本文选用了10种颜色的黏虫板对桃园小绿叶蝉进行诱集试验.结果表明黄色黏虫板诱虫效果最佳,绿色次之,而淡蓝色、紫色和深蓝色诱虫效果差.不同颜色的黏虫板对蜂类、蜘蛛和瓢虫等天敌均有杀伤作用,另诱集得到其他害虫5种.通过分析不同色板的诱捕生态效益表明:黄色黏虫板最佳,次为绿色.     

Sticky trap monitoring of a pest–predator system in glasshouse tomato crops: are available trap colours sufficient?

Monitoring of pest presence and population development in the crop during the season is essential for integrated pest management. Although many tools, for instance coloured sticky traps, have been developed, the full advantage of available information is rarely taken into account in decision‐making. The reasons behind include high workload in practice but also the poorly studied relationships between trap catches and populations in the crop. Here, we investigate whether commercially available coloured sticky traps can be used as tool to monitor population densities of a pest–predator system in glasshouse tomato. The response of Macrolophus pygmaeus (Rambur) (Hemiptera, Miridae) to blue and yellow sticky traps was tested in laboratory and glasshouse experiments. The results indicate that M. pygmaeus can be monitored equally well with both trap colours. The number of trapped insects showed good correlation with the population densities on the crop. Under growing conditions, more M. pygmaeus were trapped on blue compared with yellow sticky traps. However, due to the known preference of Trialeurodes vaporariorum (Westwood) (Hemiptera, Aleyrodidae), yellow traps should be used for a combined pest–predator monitoring.     

Site-Specific Cassette Exchange Systems in the Aedes aegypti Mosquito and the Plutella xylostella Moth

Genetically engineered insects are being evaluated as potential tools to decrease the economic and public health burden of mosquitoes and agricultural pest insects. Here we describe a new tool for the reliable and targeted genome manipulation of pest insects for research and field release using recombinase mediated cassette exchange (RMCE) mechanisms. We successfully demonstrated the established ΦC31-RMCE method in the yellow fever mosquito, Aedes aegypti, which is the first report of RMCE in mosquitoes. A new variant of this RMCE system, called iRMCE, combines the ΦC31-att integration system and Cre or FLP-mediated excision to remove extraneous sequences introduced as part of the site-specific integration process. Complete iRMCE was achieved in two important insect pests, Aedes aegypti and the diamondback moth, Plutella xylostella, demonstrating the transferability of the system across a wide phylogenetic range of insect pests.     

Improving accuracy and efficiency in seagrass detection using state-of-the-art AI techniques

Seagrasses provide a wide range of ecosystem services in coastal marine environments. Despite their ecological and economic importance, these species are declining because of human impact. This decline has driven the need for monitoring and mapping to estimate the overall health and dynamics of seagrasses in coastal environments, often based on underwater images. However, seagrass detection from underwater digital images is not a trivial task; it requires taxonomic expertise and is time-consuming and expensive. Recently automatic approaches based on deep learning have revolutionised object detection performance in many computer vision applications, and there has been interest in applying this to automated seagrass detection from imagery. Deep learning–based techniques reduce the need for hardcore feature extraction by domain experts which is required in machine learning-based techniques. This study presents a YOLOv5-based one-stage detector and an EfficientDetD7–based two-stage detector for detecting seagrass, in this case, Halophila ovalis, one of the most widely distributed seagrass species. The EfficientDet-D7–based seagrass detector achieves the highest mAP of 0.484 on the ECUHO-2 dataset and mAP of 0.354 on the ECUHO-1 dataset, which are about 7% and 5% better than the state-of-the-art Halophila ovalis detection performance on those datasets, respectively. The proposed YOLOv5-based detector achieves an average inference time of 0.077 s and 0.043 s respectively which are much lower than the state-of-the-art approach on the same datasets.     

Fluorescent Pseudomonas Isolates Pathogenic on Witloof Chicory Leaves

Nineteen phytopathogenic fluorescent Pseudomonas isolates were isolated from diseased witloof chicory (Cichorium intybus L. var. foliosum Hegi). They were compared with six Ps. fluorescens strains from culture collections, using SDS-PAGE of total cell proteins. The fluorescent pseudomonads examined showed seven different fingerprint types. The major group of phytopathogenic fluorescent pseudomonas revealed a fingerprint type which was frequently found on healthy chicory roots and leaves too. Some, but not all, of the isolates from healthy plants produced typical disease symptoms upon artificial inoculation of etiolated chicory leaves. Infectivity titrations showed a reduced efficiency of the latent pathogen PGSB-7228 to cause disease symptoms on the chicory leaves. The virulence of the latent pathogen is induced in the presence of wounded chicory tissue.     

Automatic greenhouse insect pest detection and recognition based on a cascaded deep learning classification method

Inspection of insect sticky paper traps is an essential task for an effective integrated pest management (IPM) programme. However, identification and counting of the insect pests stuck on the traps is a very cumbersome task. Therefore, an efficient approach is needed to alleviate the problem and to provide timely information on insect pests. In this research, an automatic method for the multi-class recognition of small-size greenhouse insect pests on sticky paper trap images acquired by wireless imaging devices is proposed. The developed algorithm features a cascaded approach that uses a convolutional neural network (CNN) object detector and CNN image classifiers, separately. The object detector was trained for detecting objects in an image, and a CNN classifier was applied to further filter out non-insect objects from the detected objects in the first stage. The obtained insect objects were then further classified into flies (Diptera: Drosophilidae), gnats (Diptera: Sciaridae), thrips (Thysanoptera: Thripidae) and whiteflies (Hemiptera: Aleyrodidae), using a multi-class CNN classifier in the second stage. Advantages of this approach include flexibility in adding more classes to the multi-class insect classifier and sample control strategies to improve classification performance. The algorithm was developed and tested for images taken by multiple wireless imaging devices installed in several greenhouses under natural and variable lighting environments. Based on the testing results from long-term experiments in greenhouses, it was found that the algorithm could achieve average F₁-scores of 0.92 and 0.90 and mean counting accuracies of 0.91 and 0.90, as tested on a separate 6-month image data set and on an image data set from a different greenhouse, respectively. The proposed method in this research resolves important problems for the automated recognition of insect pests and provides instantaneous information of insect pest occurrences in greenhouses, which offers vast potential for developing more efficient IPM strategies in agriculture.     

Insect reactions to light and its applications to pest management

Insects are able to see ultraviolet (UV) radiation. Nocturnal insects are often attracted to light sources that emit large amounts of UV radiation, and devices that exploit this behavior, such as light traps for forecasting pest outbreaks, and electric insect killers, have been developed. Some diurnal species are attracted to yellow; yellow pan traps are used for conducting surveys for pest outbreaks and yellow sticky plates are used for pest control. Lamps that give off yellow illumination have been used effectively to control the activity of nocturnal moths and thus reduce damage to fruit, vegetables, and flowers. Covering cultivation facilities with film that filters out near-UV radiation reduces the invasion of pests such as whiteflies and thrips into the facilities, thus reducing damage. Reflective material placed on cultivated land can control the approach of flying insects such as aphids. Future development and use of new light sources such as light-emitting diodes is anticipated for promoting integrated pest management.     

Automated detection and classification of southern African Roman seabream using mask R-CNN

The availability of relatively cheap, high-resolution digital cameras has led to an exponential increase in the capture of natural environments and their inhabitants. Video-based surveys are particularly useful in the underwater domain where observation by humans can be expensive, dangerous, inaccessible, or destructive to the natural environment. However, a large majority of marine data has never gone through analysis by human experts – a process that is slow, expensive, and not scalable. We test a Mask R-CNN object detection framework for the automated localisation, classification, counting and tracking of fish in unconstrained underwater environments. We present a novel, labelled image dataset of roman seabream (Chrysoblephus laticeps), a fish species endemic to Southern Africa, to train and validate the accuracy of our model. The Mask R-CNN model accurately detected and classified roman seabream on the training dataset (mAP50 = 80.29%), validation dataset (mAP50 = 80.35%), as well as on previously unseen footage (test dataset) (mAP50 = 81.45%). The fact that the model performs well on previously unseen data suggests that it is capable of generalising to new streams of data not included in this research.