RESEARCH AND APPLICATION OF CROP PEST MONITORING AND EARLY WARNING TECHNOLOGY IN CHINA

The importance of food security,especially in combating the problem of acute hunger,has been underscored as a key component of sustainable development.Considering the major challenge of rapidly increasing demands for both food security and safety,the management and control of major pests is urged to secure supplies of major agricultural products.However,owing to global climate change,biological invasion(e.g.,fall armyworm),decreasing agricultural biodiversity,and other factors,a wide range of crop pest outbreaks are becoming more frequent and serious,making China,one of the world’s largest country in terms of agricultural production,one of the primary victims of crop yield loss and the largest pesticide consumer in the world.Nevertheless,the use of science and technology in monitoring and early warning of major crop pests provides better pest management and acts as a fundamental part of an integrated plant protection strategy to achieve the goal of sustainable development of agriculture.This review summarizes the most fundamental information on pest monitoring and early warning in China by documenting the developmental history of research and application,Chinese laws and regulations related to plant protection,and the National Monitoring and Early Warning System,with the purpose of presenting the Chinese model as an example of how to promote regional management of crop pests,especially of cross border pests such as fall armyworm and locust,by international cooperation across pest-related countries.

Application of Remote Sensing Technology in Monitoring Forest Diseases and Pests

Forest diseases and pests affect the forest health and forestry production, the monitoring of forest diseases and pests by remote sensing has great advantages and potential. The principles, the technical methods and the main aspects of monitoring forest diseases and pests by remote sensing are described, and the application prospect of this technology is forecasted.

Monitoring and Tracking on Immune Antibody of Sheep Peste des Petits Ruminants

Peste ties petits ruminants is a kind of acute eontagious disease infecting goats anti sheep. In this study, antibtly monitoring and tracking of healthy goat and sheep immunized by peste des petits ruminants vaccine in Changping District of Beijing City were conducted, aiming at providing a reference for the devel- opment of effective immunization procedures.

Monitoring Thosea sinensis Walker in Tea Plantations Based on UAV Multi- Spectral Image

Thosea sinensis Walker(TSW)rapidly spreads and severely damages the tea plants.Therefore,finding a reliable operational method for identifying the TSW-damaged areas via remote sensing has been a focus of a research community.Such methods also enable us to calculate the precise application of pesticides and prevent the subsequent spread of the pests.In this work,based on the unmanned aerial vehicle(UAV)platform,five band images of multispectral red-edge camera were obtained and used for monitoring the TSW in tea plantations.By combining the minimum redundancy maximum relevance(mRMR)with the selected spectral features,a comprehensive spectral selection strategy was proposed.Then,based on the selected spectral features,three classic machine learning algorithms,including random forest(RF),support vector machine(SVM),and k-nearest neighbors(KNN)were used to construct the pest monitoring model and were evaluated and compared.The results showed that the strategy proposed in this work obtained ideal monitoring accuracy by only using the combination of a few optimized features(2 or 4).In order to differentiate the healthy and TSW-damaged areas(2-class model),the monitoring accuracies of all the three models were computed,which were above 96%.The RF model used the least number of features,including only SAVI and Bandred.In order to further discriminate the pest incidence levels(3-class model),the monitoring accuracies of all the three models were computed,which were above 80%,among which the RF algorithm based on SAVI,Band_(red),VARI__(green),and Band_(red_edge) features achieve the highest accuracy(OAA of 87%,and Kappa of 0.79).Considering the computational cost and model accuracy,this work recommends the RF model based on a few optimal feature combinations to monitor and distinguish the severity of TSW in tea plantations.According to the UAV remote sensing mapping results,the TSW infestation exhibited an aggregated distribution pattern.The spatial information of occurrence and severity can offer effective guidance for precise control of the pest.In addition,the relevant methods provide a reference for monitoring other leaf-eating pests,effectively improving the management level of plant protection in tea plantations,and guaranting the yield and quality of tea plantations.

柑橘木虱YOLO v8-MC识别算法与虫情远程监测系统研究

柑橘木虱是黄龙病的主要传播媒介,其发生与活动可对柑橘果园造成毁灭性后果。为实现木虱虫情的高效监测,设计了一种集诱捕拍照、耗材更新、害虫识别与结果展示于一体的智能监测系统。设计了具备诱虫胶带自动更新、虫情图像实时获取功能的诱捕监测装置;应用选点裁剪、Mosaic数据增强(Mosaic data augmentation,MDA)和CA(Coordinate attention)注意力机制,改进了YOLO v8木虱识别模型;开发了Web和手机APP客户端,可实现虫情数据的可视化展示与远程控制。模型测试阶段,改进后的YOLO v8-MC召回率、F1值及精确率分别达到91.20%、91%、90.60%,较基准模型分别提升5.47、5、4.64个百分点;迁移试验中,模型召回率、F1值及精确率分别达到88.64%、87%、84.78%,且系统工作状态良好,满足野外使用需求。开发的智能监测系统能有效实现果园木虱虫情的远程监测,可为此类虫害防治管理提供有效手段。

水稻害虫:研究进展与展望

水稻是我国主要的粮食作物,病虫害的发生直接威胁水稻生产安全。水稻害虫种类多,灾变机制复杂,绿色防控新技术开发难度大。深入研究水稻害虫的监测和早期预警技术、发育与生殖调控机制、化学通信机制和新型防控技术等,不仅有助于阐明水稻害虫发生灾变机制,还将为害虫综合治理提供新的有效的防控靶点和防控技术。近年来,我国学者在水稻害虫研究领域取得显著进展,在多个研究方向取得重要成果,本专辑汇集了一些最新研究进展。本文概述了目前关于水稻害虫研究的最新进展,介绍了本专辑论文的主要研究内容,并提出了如下值得更深入关注的3个研究方向:(1)水稻害虫智能化监测和早期预警;(2)水稻害虫灾变机制及演变规律;(3)水稻害虫绿色防控靶点挖掘及可持续防控体系构建和应用。

Pheromone Monitoring in the Granaries of Uzbekistan

The pheromonitoring of pest insects population was carried out in the granary conditions. The results of pheromonitoring let us detect the pest insect’s location at lowest abundance, while visual inspections are not effective, and to determine the data of effective location in granary, proportions traps and pheromone preparative dose. The aggregation pheromone of the synthetic analogue Sitophilus weevils had been carried out, according to the general procedure for preparation of aldol product. Especially for increasing attractiveness of pheromone traps, different types of food baits were tested. The results were observed with using of germinated wheat grains moisten by wheat germ oil. The corn oil was more attractive and effective for attractiveness of whole pest insects in granary. The wheat germ oil and burdock oil was preferred, as food bait, especially for weevils. The special perforated construction of the pheromone trap for pest insects usage has been developed and recommended for storage areas. In a condition of granary, successful testing of pheromone monitoring makes it possible to establish the timing and necessity of quantitative chemical treatments.

诱捕器在储粮害虫治理中的应用现状及展望

在储粮害虫防治中,由于长期、不合理地使用磷化氢和防护剂,导致害虫抗药性的产生。因此,需要开发绿色高效的防治替代方法。将信息素、食物引诱剂和灯光与诱捕器结合诱捕害虫,可实现害虫的大量诱杀和在线监测,在储粮害虫治理中具有很好的应用前景。进一步提高诱捕器的诱捕效率,以及建立诱捕器监测结果与害虫种群发生动态的联系是推广诱捕器应用的技术关键。通过分析诱捕器在储粮害虫治理中应用时存在的问题,提出解决途径,并指出未来研究的方向,以期为诱捕器的开发和应用推广提供参考。

枸杞病虫害绿色防控科技创新进展及发展趋势

枸杞病虫害具有种类多、危害重、发生规律复杂、防控难度大等特点。综述70年来枸杞病虫害从单一的化学农药防治到绿色防控的4个发展阶段,枸杞病虫害防控取得显著成效:建立了宁夏全域枸杞病虫害监测预报技术体系和信息化应用平台,集成建立了枸杞病虫害绿色防控技术体系并在宁夏全域应用。目前,枸杞产业高质量发展与生态保护的重大需求给枸杞病虫害防控带来新的挑战和发展机遇,生态调控成为枸杞病虫害防控科研创新的重大课题,构建以区域生态调控技术为核心,集成信息化监测预警、新型生物农药与天敌产品、农药精准靶向应用等多因子协同增效的枸杞病虫害绿色技术防控体系和治理新模式成为重要的发展目标。

Empowering fall webworm surveillance with mobile phone-based community monitoring: a case study in northern China

Recent advances in information and communication technologies, such as mobile Internet and smart- phones, have created new paradigms for participatory environment monitoring. The ubiquitous mobile phones with capabilities such as a global positioning system, camera, and network access, offer opportunities to estab- lish distributed monitoring networks that can perform a wide range of measurements for a landscape. This study examined the potential of mobile phone-based community monitoring of fall webworm （Hyphantria cunea Drury）. We built a prototype of a participatory fall webworm monitoring System based on mobile devices that stream- lined data collection, transmission, and visualization. We also assessed the accuracy and reliability of the data collected by the local community. The system performance was evaluated at the Ziya commune of Tianjin municipality in northern China, where fall webworm infestation has occurred. The local community provided data with accuracy comparable to expert measurements （Willmott＇s index of agreement 〉0.85）. Measurements by the local community effectively complemented remote sensing images in both temporal and spatial resolution.

Precise Agriculture:Effective Deep Learning Strategies to Detect Pest Insects

Pest insect monitoring and control is crucial to ensure a safe and profitable crop growth in all plantation types,as well as guarantee food quality and limited use of pesticides.We aim at extending traditional monitoring by means of traps,by involving the general public in reporting the presence of insects by using smartphones.This includes the largely unexplored problem of detecting insects in images that are taken in noncontrolled conditions.Furthermore,pest insects are,in many cases,extremely similar to other species that are harmless.Therefore,computer vision algorithms must not be fooled by these similar insects,not to raise unmotivated alarms.In this work,we study the capabilities of state-of-the-art(SoA)object detection models based on convolutional neural networks(CNN)for the task of detecting beetle-like pest insects on nonhomogeneous images taken outdoors by different sources.Moreover,we focus on disambiguating a pest insect from similar harmless species.We consider not only detection performance of different models,but also required computational resources.This study aims at providing a baseline model for this kind of tasks.Our results show the suitability of current SoA models for this application,highlighting how FasterRCNN with a MobileNetV3 backbone is a particularly good starting point for accuracy and inference execution latency.This combination provided a mean average precision score of 92.66%that can be considered qualitatively at least as good as the score obtained by other authors that adopted more specific models.

植保无人机对水稻病虫害防治应用效果分析

植保无人机通过搭载各类传感器和作业设备,实现对农田作物的病虫害监测、识别和防治,与传统人工施药和检测方式相比,植保无人机具有作业效率高、作业范围广、作业精度高等优点,正逐渐在农业生产中发挥重要作用。该文通过对植保无人机在水稻病虫害防治中的应用效果进行综述分析,总结了现存问题,并对未来发展趋势进行展望,得到植保无人机在病虫害防治过程中具有作业效率高、施药精准、环境友好等特点,可为实现精准农业、提高农业产值提供参考。

棉花病虫害遥感监测研究进展

病虫害严重威胁棉花的产量与质量,阻碍棉花产业的发展,传统人工田间调查病虫害发生状况的方法难以满足大范围病虫害监测预报的需求。遥感监测技术可以无损获取棉花表型信息,能快速、大面积解译病虫害发生程度及空间分布信息,已发展成为棉花病虫害监测的重要途径。本文阐述了棉花病虫害近地、低空、卫星遥感监测的研究进展,总结了现有发展面临的问题,并对未来的研究方向和应用前景进行了展望,以期为棉花病虫害遥感监测研究提供指导和建议。

艾滋病防治督导与评估工作的PEST分析

采用PEST分析法对我国艾滋病防治督导与评估工作中的政治、经济、社会和技术等4种环境进行系统分析,提出艾滋病防治督导与评估工作的发展策略,为制定政策提供参考。

基于STM32的烟草害虫虫情监测预警装置

为准确掌握烟草种植中的虫情,及时防治虫害,促进烟农增收,该文提出一种基于无线传输的烟草虫情监测预警装置。该装置使用STM 32单片机作为主控制器,设计诱捕单元收集烟草害虫,使用Wi-Fi模块进行数据传输,经过调试可知,该装置可以根据一定时间内收集到的烟草害虫重量和数量,预判虫情发生的可能性并作出相应预警提示。该装置还可实现与手机通信,在APP上显示虫子数量、装置倾斜角度状态等信息,同时可以通过手机APP界面的开关按键打开或关闭诱虫装置。综上,该装置可以实现烟草害虫数量监测、虫害预警的功能。

基于物联网技术对小麦病虫害进行监测预警的研究进展

在传统的农业管理中,小麦病虫害的诊断和防治主要依赖人工进行不定期调查。该方法无法对小麦的生长状况实现实时监控,会影响小麦的健康生长和产量。通过物联网技术可以对小麦生长和病虫害发生情况进行实时监控,有助于及时发现病虫害的初期迹象,为防控治疗工作提供准确的信息和时间窗口。对物联网技术在小麦病虫害监测预警方面的应用进行深入研究,分析实际应用效果和存在问题,提出相应的改进措施和建议。

山西林业有害生物监测与防治优化措施

通过实施林业有害生物监测,能够全面了解与掌握有害生物发展情况,为林业有害生物预防与治理工作的高效落实提供重要依据。因此,必须高度重视林业有害生物的监测,发挥监测预报工作价值,提升林业有害生物防治措施的针对性、可行性,以推进生态林业健康发展。

云南瑞丽黄胸鼠肥满度季节性差异及影响因素

黄胸鼠Rattus tanezumi是中国南部常见的鼠疫传染源和农业害兽。为了解不同环境因素对黄胸鼠肥满度的影响及其个体差异程度,本研究对2009年8月—2010年5月在云南省瑞丽市捕获的819只黄胸鼠肥满度进行了分析。结果表明,黄胸鼠肥满度具有明显的偏雄性二态现象:雄性的肥满度明显高于雌性;且在不同景观、不同生境、不同季节均有显著差异。在调查的10个月内,黄胸鼠的肥满度存在明显波动,其中雄性肥满度呈现出“三峰”:最高值(第一峰)出现在10月、第二高峰值出现在次年1月、第三高峰值在次年5月;雌性肥满度呈现为“双峰”:最高值(第一峰)出现在10月、第二高峰值在次年4月。这些结果提示应在1月、4月、5月、10月、12月等月份加强对黄胸鼠的防范。

渭南市羊小反刍兽疫监测与分析

为评估渭南市羊小反刍兽疫的免疫抗体水平及病毒感染状况,对2019-2022年在渭南市采集的2 499份血清学样品和606份病原学样品,分别采用阻断ELISA方法和荧光RT-PCR方法进行免疫抗体检测和病毒核酸检测。结果显示,小反刍兽疫免疫抗体个体合格率为88.24%,区域场群合格率为91.99%,不同区域免疫抗体水平合格率均高于农业农村部的免疫要求(≥70%)。分类统计不同年份和不同场点类型的免疫抗体合格率均达到国家免疫效果评估要求。病原学样品病毒核酸均为阴性。结果表明渭南市小反刍兽疫防控效果较好,但仍需持续做好小反刍兽疫的强制免疫及监测,确保养殖安全。

多光谱无人机在林业有害生物监测中的应用

本文基于多光谱无人机搭载的传感器获取多光谱数据,结合某地区林业内部所存在的松材线虫病疫树木情况与位置信息,综合GIS技术混合建模并使用Yusense Map软件对数据实施精准配备、拼接与辐射定标,进而判定疑似疫木,并结合人工实地勘察对无人机多光谱技术在林业有害生物监测中的实际应用效果进行全面探讨,为提升我国林业地区疫木疑似的选择和林业有害生物监测等工作提供参考。