应用昆虫远程智能监测系统监测苹果园害虫的技术

【目的】探索昆虫远程智能监测系统在苹果园害虫监测中的应用技术。【方法】采用双机双摄图像采集系统收集每个时间段诱集昆虫的清晰图像,并传送至系统,在系统客户端进行识别记录。【结果】昆虫远程智能监测系统通过平台设定参数,自动完成昆虫的诱集、灭杀、拍照、传输、收集等工作,客户端人工识别、计数较准确,与实际害虫发生吻合度高。【结论】昆虫远程智能监测系统可实现苹果园多种主要害虫的远程实时监测,减少了监测的人力物力。电脑神经网络通过平台提供的昆虫图片的学习、特征提取、结构构建和模型评估,最终可实现昆虫的自动识别计数。     

害虫自动识别与计数技术研究进展与展望

害虫种类的识别与发生数量的获取是精准预测的重要基础,对害虫综合治理具有重要的决策意义。传统的害虫识别与计数方法效率低,难以满足植保现代化的要求。本文综述了害虫自动识别与计数技术的研究进展,讨论了声音信号、图像技术、红外传感器在害虫自动识别与计数中应用的优势和不足,对提高害虫自动识别与计数技术准确率的解决措施提出了初步的建议。提出今后研究重点应将上述多种技术进行结合,不同技术之间相互取长补短,建立综合性的监测技术体系,以提高害虫识别与计数的准确率;同时应用三维技术解决害虫二维图像识别算法普适性的问题。     

梨小食心虫自动监测识别计数系统研制

针对果园梨小食心虫监测中存在的人工计数调查费力且欠准确的问题,本研究设计了一种基于性诱的害虫自动监测装置,通过构建配套的图像处理系统实现了害虫自动识别计数。应用诱芯与粘虫板相结合进行害虫诱捕;机器视觉定时采集粘虫板图像;基于Visual C#与Matlab混合编程,构建害虫自动识别计数系统。并在桃园应用研制的性诱害虫自动监测系统对梨小食心虫监测结果进行比较。结果显示:自动监测装置的平均诱捕率为89. 58%;梨小食心虫平均识别准确率为94. 11%。表明该自动监测系统的害虫诱捕率和识别准确率均高,提高了害虫监测识别的效率,在害虫监测中具有广阔的应用前景。     

重要呼吸道传染病智慧化监测预警与有效应对策略探讨

全球新发突发传染病不断出现,特别是新型冠状病毒肺炎疫情暴发,对人类健康和社会发展造成巨大影响.本文通过不同渠道收集有关资料,试图梳理我国呼吸道传染病监测预警发展和现状,探讨采用大数据、区块链、人工智能等高新技术,建立智慧化新发突发呼吸道传染病监测预警平台.运用统一关键数据标准、口径,打通部门之间数据壁垒,建立数据共享机制,实时自动采集与传染病有关的数据信息.利用大数据、人工智能、云计算等技术,建立相关数据模型,强化数据分析应用,达到早期识别和预警,做到遏止突发事件的发生发展和传播.同时,在疫情防控过程中能够实时获得疫情的发生发展趋势,及时提出科学精准的有效防控策略.不断提升我国应对突发公共卫生事件的能力和水平,保障我国生物安全.     

农作物有害生物防控:成就与展望

我国农作物有害生物具有种类多、危害重、发生规律复杂、防控难度大等特点,相关理论和技术创新、防控体系构建与应用是保障农业安全生产的迫切需求.新中国成立70年来,我国植物保护领域取得了一系列的科技成就,农作物有害生物防控从单一的人为干预起步,经历化学农药防治为主,再到绿色防控综合治理的发展阶段,其中较为突出的成就包括:掌握了农作物重大病虫流行灾变规律;揭示了重要农业有害生物的致害机理;研发了一批防控产品;建立了作物病虫害监测预警及防控技术体系.随着产业变革与科技进步,我国植保科技领域将迎来新的挑战和发展机遇.未来的植保理论和技术研究应围绕下列3方面展开:新型生产模式及气候变化下的有害生物发生规律,全球化趋势下的有害生物检测预警技术,绿色可持续的有害生物综合治理新模式.建立现代植保技术体系将保障我国的粮食安全、环境安全和农业可持续发展.     

新时代植保植检工作新探索

植保植检工作如何适应社会发展的需要,不断更新工作思路,不断提高植保植检工作水平与效率,是现代植保人必须面对与解决的现实而迫切的课题.本文以重庆市秀山县为例,分析了植保植检技术人员年龄老化、队伍能力整体弱化以及经费保障问题、体制问题、管理问题等植保植检工作面临的困局和问题;提出了植保植检法制化、队伍稳定化、职能专业化、技术研究前沿化、技术应用推广适用化与集成化、农作物病虫害防治协作化、装备现代化、投入多元化、宣传培训常态化等植保植检工作新思路.     

基于性诱和深度学习的草地贪夜蛾成虫自动识别计数方法

【目的】探究深度学习在草地贪夜蛾Spodoptera frugiperda成虫自动识别计数上的可行性,并评估模型的识别计数准确率,为害虫机器智能监测提供图像识别与计数方法。【方法】设计一种基于性诱的害虫图像监测装置,定时自动采集诱捕到的草地贪夜蛾成虫图像,结合采集船形诱捕器粘虫板上草地贪夜蛾成虫图像,构建数据集;应用YOLOv5深度学习目标检测模型进行特征学习,通过草地贪夜蛾原始图像、清除边缘残缺目标、增加相似检测目标(斜纹夜蛾成虫)、无检测目标负样本等不同处理的数据集进行模型训练,得到Yolov5s-A1, Yolov5s-A2, Yolov5s-AB, Yolov5s-ABC 4个模型,对比在不同遮挡程度梯度下的测试样本不同模型检测结果,用准确率(P)、召回率(R)、F1值、平均准确率(average precision, AP)和计数准确率(counting accuracy, CA)评估各模型的差异。【结果】通过原始图像集训练的模型Yolov5s-A1的识别准确率为87.37%,召回率为90.24%,F1值为88.78;清除边缘残缺目标图像集训练得到的模型Yolov5s-A2的识别准确率为93.15%,召回率为84.77%,F1值为88.76;增加斜纹夜蛾成虫样本图像训练的模型Yolov5s-AB的识别准确率为96.23%,召回率为91.85%,F1值为93.99;增加斜纹夜蛾成虫和无检测对象负样本训练的模型Yolov5s-ABC的识别准确率为94.76%,召回率为88.23%,F1值为91.38。4个模型的AP值从高到低排列如下：Yolov5s-AB>Yolov5s-ABC> Yolov5s-A2>Yolov5s-A1,其中Yolov5s-AB与Yolov5s-ABC结果相近;CA值从高到低排列如下：Yolov5s-AB>Yolov5s-ABC>Yolov5s-A2>Yolov5s-A1。【结论】结果表明本文提出的方法应用于控制条件下害虫图像监测设备及诱捕器粘虫板上草地贪夜蛾成虫的识别计数是可行的,深度学习技术对于草地贪夜蛾成虫的识别和计数是有效的。基于深度学习的草地贪夜蛾成虫自动识别与计数方法对虫体姿态变化、杂物干扰等有较好的鲁棒性,可从各种虫体姿态及破损虫体中自动统计出草地贪夜蛾成虫的数量,在害虫种群监测中具有广阔的应用前景。     

实验猴身份自动识别、自动捕捉、电子芯片数据信息管理系统的研制

目的通过电子芯片植入与识别技术和计算机管理系统对接,建立每只实验猴的数据档案,其中包括遗传谱系、生理数据、实验记录。方法通过建立实验猴的电子通道,以非接触方式通过计算机管理,应用射频识别信号系统,实现实验猴身份快速自动识别、自动捕捉、数据读写、电子档案信息管理、数据远程传输的目的,减少人为因素对实验猴的干扰和引起的应激反应。结果该系统的建立极大的减少了实验和饲养实验猴过程中捕捉猴的困难,身份识别困难,减少了对工作人员和实验猴的伤害,减轻了工作量,减低了人为因素对实验结果的干扰和动物应激反应对实验数据的影响。电子档案具有终身有效,随猴携带,全球唯一序列号,不可复制,不可仿制,标签唯一性。结论实验猴身份自动识别、自动捕捉、电子芯片数据信息管理系统的研制成功,为实验动物个体电子档案建立和群体数据库的管理提供了一种高速、有效、科学、可靠的计算机管理手段,改变通道模式即可应用于大型凶猛动物或不适合人接触捕捉动物的识别和管理,具有广泛的应用前景,目前在国内外尚无同类系统。     

基于深度学习的作物基因组学和遗传改良

随着世界人口的不断增长、食物需求量的不断增加,以及气候的不断变化,如何提高农作物产量已成为人类面临的一个巨大挑战。传统设计育种耗时长、效率低,已经不能满足新时代的育种需求。随着基因型和表型数据成本的不断降低,以及各种组学数据的爆炸式增长,人工智能技术作为能够在大数据中高效率挖掘信息的工具,在生物学领域受到了广泛关注。人工智能指导的设计育种将大大加快育种的效率,给育种带来革命性的变化。介绍了人工智能特别是深度学习在作物基因组学和遗传改良中的应用,并进行了总结与展望,以期为智能设计育种提供新的思路。     

利用固定翼无人机监测松材线虫病疫点枯死松树的初步研究

无人机航摄监测森林病虫害是一个新的研究热点。为探究无人机航摄在松材线虫病监测中的应用,本研究于2017年11月利用小型固定翼无人机采集了广东省河源市新丰江库区松材线虫病疫点的航摄影像,总面积1425.9 hm~2。固定翼无人机搭载了1台可见光数码相机和1台多光谱数码相机,能同时采集枯死松树的可见光和近红外的航摄影像。利用LAMapper软件对航摄图像进行空中三角测量和像素匹配,获得可见光正射影像和多光谱正射影像。使用ERDAS软件生成影像的归一化植被指数(NDVI)。然后将带有地理信息的完整影像自动导入GIS系统进行异常点识别和几何矫正,导出最终的影像数据。最后,对影像进行分析,并根据植被指数(NDVI)对图像进行分类。分析结果显示,航摄的疫点中共自动识别1486株枯死松树,并获得了其分布地图及坐标点位置。验证结果表明监测的准确率达到80%以上,坐标点精度达到2-3 m。本研究结果具有低成本、自动化、可靠、客观、高效和及时等优点,可为大面积监测松材线虫病的发生现状和流行动态、评估防控效果和灾害损失提供技术支撑。     

A method for automatic real-time detection and counting of fruit fly pests in orchards by trap bottles via convolutional neural network with attention mechanism added

Fruit fly pests seriously affect the quality and safety of various melons, fruits, and vegetable crops. Many farmers lack sufficient knowledge of the level of pest occurrence, which leads to the over-use of pesticides, resulting in environmental pollution and quality degradation of agricultural products. The combination of artificial intelligence and agricultural technology can continuously and dynamically monitor pests in orchards, help scientific researchers and fruit farmers master pest data in time, reduce the use of artificial and pesticides, and achieve scientific early warning and prevention of pests. In this paper, the sexual attractant is placed in the pest trap bottle, the optical flow method, U-Net semantic segmentation, and the YOLOv5 algorithm with SE, CBAM, CA, and ECA attention mechanisms are used to detect and count live Bactrocera cucurbitae on the bottle surface, and then we use Hough circle detection method to detect the entrance position of the trap bottle, and finally count the number of B. cucurbitae entering the trap bottle in combination with the position information of B. cucurbitae and the entrance of the trap bottle. The experimental results show that: the accuracy of counting B. cucurbitae on the surface of the trap bottle can reach 93.5%, and the accuracy of counting B. cucurbitae entering the trap bottle can reach 94.3%, which can dynamically monitor the number of pests in the orchard in real time.     

草地有害啮齿动物监测专家系统设计介绍

主要介绍了青藏高原草地有害啮齿动物监测专家系统的建造原理和方法。该系统由综合数据库、知识库、推理机、知识编辑语言及系统建造支持环境等部分组成,文中详细说明各部分的主要功能以及与其他部分的相互关系。该系统可对青藏高原有害动物种群动态进行长期监测,快速准确地预测预报草地植被受害状况,并根据生态环境特点,对有害啮齿动物综合治理进行规划,为用户提供长期治理的技术和多种可供选择的可行性方案。     

Application of an image and environmental sensor network for automated greenhouse insect pest monitoring

This work presents an automated insect pest counting and environmental condition monitoring system using integrated camera modules and an embedded system as the sensor node in a wireless sensor network. The sensor node can be used to simultaneously acquire images of sticky paper traps and measure temperature, humidity, and light intensity levels in a greenhouse. An image processing algorithm was applied to automatically detect and count insect pests on an insect sticky trap with 93% average temporal detection accuracy compared with manual counting. The integrated monitoring system was implemented with multiple sensor nodes in a greenhouse and experiments were performed to test the system’s performance. Experimental results show that the automatic counting of the monitoring system is comparable with manual counting, and the insect pest count information can be continuously and effectively recorded. Information on insect pest concentrations were further analyzed temporally and spatially with environmental factors. Analyses of experimental data reveal that the normalized hourly increase in the insect pest count appears to be associated with the change in light intensity, temperature, and relative humidity. With the proposed system, laborious manual counting can be circumvented and timely assessment of insect pest and environmental information can be achieved. The system also offers an efficient tool for long-term insect pest behavior observations, as well as for practical applications in integrated pest management (IPM).     

Performance evaluation of a machine vision system for insect pests identification of field crops using artificial neural networks

Feature extraction is a crucial part of advanced image recognition systems. In this research, an autonomous detection device was designed and developed for insect pest detection to improve the ability of intelligent systems in order to annihilate harmful insect pests in agricultural crop fields. Device included a dark chamber, a CCD digital camera, a LDR lightening module and a personal computer. The proposed programme for precise insect pest detection was based on an image processing algorithm and artificial neural networks (ANNs). After image acquisition, the insect pests’ images were extracted from original images with Canny filtration. Afterwards, four morphological and three textural features from the obtained images were measured and normalised. Performance of ANN model was tested successfully for Beet armyworm (Spodoptera exigua) recognition in images using back-propagation supervised learning method and inspection data. Results showed that proposed system was able to identify S. exigua in the images from other species. Such this machine vision system can be used in autonomous field robots to achieve a modern farmer’s assistant.     

An artificial neural network approach to identify fungal diseases of cucumber (Cucumis sativus L.) plants using digital image processing

Nowadays, artificial intelligence solutions such as digital image processing and artificial neural networks (ANN) have become important applicable techniques in phytomonitoring and plant health detection systems. In this research, an autonomous device was designed and developed for detecting two types of fungi (Pseudoperonospora cubensis, Sphaerotheca fuliginea) that infect the cucumber (Cucumis sativus L.) plant leaves. This device was able to recognise the fungal diseases of plants by detecting their symptoms on plant leaves (downy mildew and powdery mildew). For leaves of cucumber inoculated with different spores of the fungi, it was possible to estimate the amount of hour post inoculation (HPI) by extracting leaves’ image parameters. Device included a dark chamber, a CCD digital camera, a thermal camera, a light dependent resistor lightening module and a personal computer. The proposed programme for precise disease detection was based on an image processing algorithm and ANN. Three textural features and two thermal parameters from the obtained images were measured and normalised. Performance of ANN model was tested successfully for disease recognition and detecting HPI in images using back-propagation supervised learning method and inspection data. Such this machine vision system can be used in robotic intelligent systems to achieve a modern farmer’s assistant in agricultural crop fields.     

Spatio‐temporal analysis of plant pests in a greenhouse using a Bayesian approach

• 1 The present study aimed to propose a method that can improve our understanding of pest outbreaks and spatio‐temporal development in greenhouse crops.
• 2 The experiment was carried out in a greenhouse rose crop grown under integrated pest management (IPM) for 21 months. The main pests observed were powdery mildew, two‐spotted spider mites and western flower thrips. A quick visual sampling method was established to provide continuous monitoring of overall crop health.
• 3 A Bayesian inferential approach was then used to analyse temporal and spatial heterogeneity in the occurrence of pests. Interactions between pest dynamics and properties of spatial evolutions were exhibited revealing the influence of biotic and abiotic factors on crop health.
• 4 In the context of IPM, this information could be used to improve monitoring strategies by identifying periods or locations at risk. It could also facilitate the implementation of the whole IPM procedure through the identification of key factors that have a negative impact on overall crop health.

     

GM as a route for delivery of sustainable crop protection

Modern agriculture, with its vast monocultures of lush fertilized crops, provides an ideal environment for adapted pests, weeds, and diseases. This vulnerability has implications for food security: when new pesticide-resistant pest biotypes evolve they can devastate crops. Even with existing crop protection measures, approximately one-third yield losses occur globally. Given the projected increase in demand for food (70% by 2050 according to the UN), sustainable ways of preventing these losses are needed. Development of resistant crop cultivars can make an important contribution. However, traditional crop breeding programmes are limited by the time taken to move resistance traits into elite crop genetic backgrounds and the limited gene pools in which to search for novel resistance. Furthermore, resistance based on single genes does not protect against the full spectrum of pests, weeds, and diseases, and is more likely to break down as pests evolve counter-resistance. Although not necessarily a panacea, GM (genetic modification) techniques greatly facilitate transfer of genes and thus provide a route to overcome these constraints. Effective resistance traits can be precisely and conveniently moved into mainstream crop cultivars. Resistance genes can be stacked to make it harder for pests to evolve counter-resistance and to provide multiple resistances to different attackers. GM-based crop protection could substantially reduce the need for farmers to apply pesticides to their crops and would make agricultural production more efficient in terms of resources used (land, energy, water). These benefits merit consideration by environmentalists willing to keep an open mind on the GM debate.     

New image dataset and new negative sample judgment method for crop pest recognition based on deep learning models

Crop pests are responsible for serious economic loss around the worldwide. Accurate recognition of pests is the key to pest control and is a considerable challenge in farming. Deep learning models have shown great promise in image recognition, drawing the attention of many agricultural experts. However, the lack of pest image datasets and the inexplicability of deep learning models have hindered the development of deep learning models in the field of pest recognition. Our work provides the following four contributions: (1) We constructed a new and more effective dataset, for crop pest recognition, named IP41 comprising 46,567 original images of crop pests in 41 classes. (2) We trained three different deep learning models based on IP41, using transfer learning combined with fine-tuning. The results of the three deep learning models exceeded 80.00% recognition. (3) A negative sample judgment method was proposed to exclude the uploaded pest-free images of the user. (4) We provided reasonable visual explanations for the most critical areas of the recognition layers by using the gradient-weighted class activation mapping method. This research suggests that the recognition process focuses more on image details than the image as a whole, and that overall difference is ignored to a certain extent. These results will be helpful to future research in the field of agricultural pest recognition     

基于小波神经网络的麦蚜发生程度预测模型

【目的】建立基于小波神经网络病虫害预测预报模型,对提前采取防病防虫措施、减少农作物病虫害损失、提高农作物产量与质量具有重要意义。【方法】本研究以山西省运城市芮城县1980-2014年麦蚜发生程度和气象因子数据为基础,采用主成分分析法从40个基础气象因子中整合形成9个新的自变量输入模型,采用试凑法筛选隐含层节点数,用1980-2009年的数据进行网络训练,对2010-2014年麦蚜发生程度进行回测,建立了以Morlet小波函数为传递函数的小波神经网络模型,并与以Sigmoid函数为传递函数的BP神经网络模型进行了比较。【结果】小波和BP神经网络两种模型对训练样本的平均拟合精度均有10年以上超过80%,两者MAPE 值分别为 89.83% 和83.07%,MSE 值分别为0.0578和0.6192。【结论】两个模型都能较好地描述麦蚜发生程度;从预测精度和模型的稳定性来看,小波神经网络好于BP神经网络。