一个通用的系统故障诊断模型

狭义上,故障诊断包括两层含义即故障检测和故障定位,前者是指测试一个系统,判断其是否存在故障,而后者则是指在前者的基础上进一步定位故障点。广义上,故障诊断的内容,除了包含要判断系统有无故障以及故障产生后找到故障部位,还包含对故障特性的分析以及所采取的措施,例如让系统     

加大农业结构调整力度,推进农业规模经济发展

正农业经济发展要求扩大耕地面积和经营规模,大量使用机械,发展资本技术密集农业企业,并根据市场经济运行的要求,以市场为导向,以经济效益为中心,以农业资源开发为基础,在现有农村生产力水平和经济发展水平基础上,把分散经营的农民组织起来,聚集、装备,积累投入,调整存量,优化资源,全面提高农业生产率。使农业逐渐产业化、市场化、标准化和国际化,推进农业规模经济,实现农业四化。1、加大农业结构调整力度,大力推进农业现代化建设     

农村科技信息12396综合服务平台的构建与实现

该服务平台是为了提高农民应用信息技术的水平和科学种田的素质;加快农业信息服务技术发展,建立健全农业信息网站而建立的。使用wordpress软件,利用php语言,HTML语言以及MySql等建立一个针对农安县的农村科技信息12396综合服务平台。该平台对于提高农民科学种田的素质,使农民掌握农业现代技术,促进精准农业的推广普及,提高农民的收入,发展农村经济,加速农村现代化过程均有着重大意义。     

农民科技采用行为的社会学分析

从社会学的微观和宏观视角分析了农业技术推广中农民科技采用行为的影响因素发现,地域文化背景影响是其主要原因,农业推广中要充分利用和维持农村初级社会群体,依靠乡村精英的带领或示范,引导农民采用农业科学技术;大众传播媒介作为新生力量,保障了农业技术信息传播渠道的高效畅通;农村基层组织和农业经济合作组织是增强农民技术采用行为的基本动力,农村各项制度体系是提升农民技术采用行为的重要机制.建议将农民看作技术的主导者,由农民自己来评判技术的可行性;整合信息传播渠道,提高农民的科技采用率;理顺农村组织的关系,打造农业技术推广平台,完善农村各项制度,创造农业技术推广的良好环境.     

邓小平关于农业、农村和农民问题思想探析

国是农业大国 ,农业、农村和农民问题历来是关系国民经济的重要问题。邓小平在总结我国农业发展成功经验与失败教训的基础上 ,创造性地提出了一系列关于农业、农村和农民的思想。本文全面阐述了邓小平关于农业、农村和农民问题的思想 ,并从我国当前实际出发 ,阐明了邓小平这一思想对当前进一步深化农村改革 ,发展农村经济 ,推动我国农业和农村经济的跨世纪发展具有重大现实意义     

浅议“农业信息化”人才培养

农业是我国第一产业,农业生产、生活更是我国长期关注的焦点问题。我国大力提倡发展农业信息化。使得农村与城市之间的数字信息差距缩短。使得信息技术、计算机技术、电子通讯技术,在农业现代化生产、农业产品流通、农业知识传递等多个方面发挥作用。而农村当前现状是,传统农民对信息化技术了解少,不能理解和应用。掌握新技术新知识的"农业信息化人才"培养问题亟待解决。     

农业信息化与新农村建设

作为我国社会主义新农村建设的必要途径之一,农业信息化在推进现代化发展、农村与世界接轨、帮助农民创收等方面发挥着巨大作用,为广大新时代农民学习先进科技带来动力。加快农业信息化发展、推进新农村建设,需要在各方努力下,完善农村基础设施建设,建立健全信息体系和相关法律法规,培养农村信息化的专业人才。只有这样才能让信息化带动新农村建设。     

发展农业循环经济要解决的几个关键问题

正发展农业循环经济在我国已成为必然和现实的选择。发展农业循环经济要在重视循环经济理念的基础上,注重有关政策、法规建设,通过财税支持、技术创新及提高农民素质等多方面途径进一步推进农业循环经济发展。1、现阶段农业循环经济发展中存在的问题1.1循环经济的观念缺乏。农民往往只顾眼前利益而忽视对农业生态环境的保护;一些地方领导对农村循环经济建设的重要性及紧迫性尚未引起足够重视,有的甚至还提出一些不利     

农村天高地广阔

在去年制定的“十一五”规划建议中，党中央明确提出建设社会主义新农村是中国现代化进程中的重大历史任务。只有发展好农村经济，建设好农民的家园．让农民过上宽裕的生活，才能保障全体人民共享经济社会发展成果。才能不断扩大内需和促进国民经济持续发展。才能构建真正的和谐社会。然而，当前农业和农村发展仍然处在艰难的爬坡阶段，农业基础设施脆弱、农村社会事业发展滞后、城乡居民收入差距扩大的矛盾依然突出．解决好“三农”问题仍然是工业化、城镇化进程中重大而艰巨的历史任务。     

莆田农村庭院生态循环农业发展模式及效益分析

农村庭院生态循环农业发展模式是依据循环经济原理发展"优质、低耗、高效、高产、生态、安全"的农村庭院农业,使农民家庭实现节约资源、保护环境的生产和消费模式.莆田人多地少,农民仅靠责任田并用传统耕作方式想步人小康生活受到一定的制约,必须发展农村庭院生态循环农业,帮助农民增产增收,为莆田全面建设社会主义新农村奠定良好的基础.莆田发展农村庭院生态循环农业应遵循因地制宜、整体优化、良性循环、生态经济的原则,并在原有基础上采取加强领导,科学管理;因地制宜,做好规划;广泛宣传教育.提高认识水平;加强队伍管理,建立技术服务网络等措施完善发展模式.以期取得更高的经济、社会和生态效益.     

生物农药与设施农业病害的防治

设施农业是我国传统农业向现代农业转变的主要形式之一,同时为我国无公害农业的产业化经营提代了设施保障。植物病害频繁发生是威胁设施农业发展的主要限制因子,目前由于采用化学防治措施所带来的病害抗药性、农药残留和环境污染等问题已无法满足设施农业生产优质农副产品的要求,所以生物农药的研发与应用,为解决这一难题提供了技术保证,生物农药产业化、生物农药的质量监测与风险评估、加大对生物农药开发扶持力度,是我国设施农业病害生物防治中亟等解决的问题。     

虚拟农业体系结构的研究

本文通过对国内外虚拟农业的研究分析，在总结前人研究的基础上，认为虚拟农业是应用虚拟现实技术和可视化技术，在计算机和Internet的支持下，对农业生产、科研、教学、加工、销售等各个环节在计算机上的模拟和再现，以实现农业生产的高效益和可持续发展为目的的技术系统，从而指出虚拟农业既是一个系统平台，也是一种研究方法和手段，并应建立起完整的技术理论体系。提出了虚拟农业的技术支撑体系和体系结构。阐述了虚拟农业的应用前景和重要意义。     

如何让气象在为“三农”服务中发挥更大的优势

基于农业生产对气象条件的敏感性和依赖性,每年因气象灾害造成的粮食减产波动高达10%～20%。因此,着力关注气象与“三农”的关系,简要分析了当前气象服务中存在的问题,论述了如何让气象在为“三农”服务中发挥更大的优势,这是气象部门亟待解决的首要问题。以此为出发点,粗略探讨了气象与“三农”的关系。     

湖南农业云物联网建设对策分析

针对湖南作为国家农村信息化试点省份的需求导向,以湖南农业物联网项目实施过程中多次下基层调研分析为基础,同时借鉴江苏省宜兴市农业物联网的实施成果,针对湖南省农业物联网的实际情况,结合湖南农业云平台的应用,提出湖南农业物联网建设必须优先突破当前比较成熟的生猪、水产、大棚栽培等物联网建设,以高校技术支持、企业运作为主轴,以资源整合为重点,先示范后推广,加快湖南省农业物联网研究成果转化的具体对策。     

Opportunities for control engineering in arable precision agriculture

In this paper, we present an overview of several challenges in arable farming that are well suited for research by the control engineering society. We discuss the global needs that these challenges are related to as well as the relation of these challenges to future applications of arable farming. For each of these opportunities we provide several concrete and detailed research questions. Particular attention is paid to the management of resources and sensors in farms. The objective of writing this paper is to further entice control engineers into the domains of agronomy and agricultural technology.     

基于Hadoop农业大数据管理平台的设计

信息技术的高速发展使得每天的数据量以TB级速度暴增,如何有效利用和管理这些爆炸式增长的大数据呢？是当前亟待处理的问题.大数据已经渗透到包括农业领域在内的各个领域,随着农业信息化建设以及物联网技术在农业生产中的应用,产生了海量的农业大数据待存储、管理和处理.本文以成都农业科技职业学院彭州葛仙山农业示范基地的农业信息化建设为背景,根据农业物联网和信息化建设要求,构建高性能基于Hadoop农业大数据管理的平台,实现农业大数据的安全可靠存储、智能管理与应用,最终达到对农业生产的智能预警、智能决策和智能分析的目的,并为农户提供专业的指导.为我国进入精细化种植、精准化控制、可视化管理、智能化决策的智慧农业时代奠定基础.     

A comparative study of fruit detection and counting methods for yield mapping in apple orchards

We present a modular end‐to‐end system for yield estimation in apple orchards. Our goal is to identify fruit detection and counting methods with the best performance for this task. We propose a novel semantic segmentation‐based approach for fruit detection and counting and perform extensive comparative analysis against other state‐of‐the‐art techniques. This is the first work comparing multiple fruit detection and counting methods head‐to‐head on the same data sets. Fruit detection results indicate that the semisupervised method, based on Gaussian Mixture Models, outperforms the deep learning‐based methods in the majority of the data sets. For fruit counting though, the deep learning‐based approach performs better for all of the data sets. Combining these two methods, we achieve yield estimation accuracies ranging from 95.56% to 97.83%.     

Semantic mapping for orchard environments by merging two‐sides reconstructions of tree rows

Measuring semantic traits for phenotyping is an essential but labor‐intensive activity in horticulture. Researchers often rely on manual measurements which may not be accurate for tasks, such as measuring tree volume. To improve the accuracy of such measurements and to automate the process, we consider the problem of building coherent three‐dimensional (3D) reconstructions of orchard rows. Even though 3D reconstructions of side views can be obtained using standard mapping techniques, merging the two side‐views is difficult due to the lack of overlap between the two partial reconstructions. Our first main contribution in this paper is a novel method that utilizes global features and semantic information to obtain an initial solution aligning the two sides. Our mapping approach then refines the 3D model of the entire tree row by integrating semantic information common to both sides, and extracted using our novel robust detection and fitting algorithms. Next, we present a vision system to measure semantic traits from the optimized 3D model that is built from the RGB or RGB‐D data captured by only a camera. Specifically, we show how canopy volume, trunk diameter, tree height, and fruit count (FC) can be automatically obtained in real orchard environments. The experiment results from multiple data sets quantitatively demonstrate the high accuracy and robustness of our method.     

Coverage trajectory planning for a bush trimming robot arm

A novel motion planning algorithm for robotic bush trimming is presented. The algorithm is based on an optimal route search over a graph. Differently from other works in robotic surface coverage, it entails both accuracy in the surface sweeping task and smoothness in the motion of the robot arm. The proposed method requires the selection of a custom objective function in the joint space for optimal node traversal scheduling, as well as a kinematically constrained time interpolation. The algorithm was tested in simulation using a model of the Jaco arm and three target bush shapes. Analysis of the simulated motions showed how, differently from classical coverage techniques, the proposed algorithm is able to ensure high tool positioning accuracy while avoiding excessive arm motion jerkiness. It was reported that forbidding manipulation posture changes during the cutting phase of the motion is a key element for task accuracy, leading to a decrease of the tool positioning error up to 90%. Furthermore, the algorithm was validated in a real‐world trimming scenario with boxwood bushes. A target of 20 mm accuracy was proposed for a trimming result to be considered successful. Results showed that on average 82% of the bush surface was affected by trimming, and 51% of the trimmed surface was cut within the desired level of accuracy. Despite the fact that the trimming accuracy turned out to be lower than the stated requirements, it was found out this was mainly a consequence of the inaccurate, early stage vision system employed to compute the target trimming surface. By contrast, the trimming motion planning algorithm generated trajectories that smoothly followed their input target and allowed effective branch cutting.     

A field‐tested robotic harvesting system for iceberg lettuce

Agriculture provides an unique opportunity for the development of robotic systems; robots must be developed which can operate in harsh conditions and in highly uncertain and unknown environments. One particular challenge is performing manipulation for autonomous robotic harvesting. This paper describes recent and current work to automate the harvesting of iceberg lettuce. Unlike many other produce, iceberg is challenging to harvest as the crop is easily damaged by handling and is very hard to detect visually. A platform called Vegebot has been developed to enable the iterative development and field testing of the solution, which comprises of a vision system, custom end effector and software. To address the harvesting challenges posed by iceberg lettuce a bespoke vision and learning system has been developed which uses two integrated convolutional neural networks to achieve classification and localization. A custom end effector has been developed to allow damage free harvesting. To allow this end effector to achieve repeatable and consistent harvesting, a control method using force feedback allows detection of the ground. The system has been tested in the field, with experimental evidence gained which demonstrates the success of the vision system to localize and classify the lettuce, and the full integrated system to harvest lettuce. This study demonstrates how existing state‐of‐the art vision approaches can be applied to agricultural robotics, and mechanical systems can be developed which leverage the environmental constraints imposed in such environments.