无线传感器网络在农田节水灌溉系统中的应用

针对农田节水灌溉的需要,提出了把无线传感器网络应用于农田节水灌溉系统的思路和农田节水灌溉系统设计方案,该灌溉系统由农田监测区域的无线监测网络和远程监控中心组成,可对农田需水信息变量进行实时监测。介绍了系统的总体架构,设计开发了无线传感器网络节点、基站以及软件流程。该系统采用了无线传输的方式,解决了有线通信方式所存在的难以升级、难以扩展等问题,具有低功耗、低成本、扩展灵活等优点,在农业节水灌溉方面具有广阔的应用前景。     

无线传感器网络改进质心算法的节点自定位

节点自定位是无线传感器网络的关键技术之一。在对距离无关的定位机制质心算法研究的基础上,采用极大似然估计法,估算信标节点与未知节点间的距离,以距离作约束降低误差值,从而提高定位的精度。通过MATLAB仿真。结果表明算法能够有效提高定位精度。     

粒子滤波在电子航标节点跟踪中的应用

提出一种高斯粒子滤波求解基于RSSI测距定位的无线传感器网络电子航标节点定位的非线性方程组算法.该算法在每次递推产生新的粒子时充分考虑了当前时刻的量测,使得该算法能很好地利用最新的量测信息.仿真结果表明该算法的节点估计均方误差在3 m以内.可见,它在解决非线性机动目标跟踪问题时有比较好的跟踪性能和滤波结果.     

The prediction of seedy grape drying rate using a neural network method

This paper presents an application which uses Feedforward Neural Networks (FNNs) to model the nonlinear behaviour of the drying of seedy grapes. First, a novel type of dryer for experimentally and mathematically evaluating the thin-layer drying kinetics of seedy grapes is developed. In the developed drying system, an expanded-surface solar air collector, a solar air collector with Phase-Change Material (PCM) and drying room with swirl element have been particularly included. Secondly, the drying rate is estimated as an exponential-type equation using non-linear regression analysis. Thirdly, the drying rate of seedy grapes is estimated using an FNN. Finally, the performance of the FNN model is compared with those of nonlinear and linear regression models by means of the root mean square errors, the mean absolute errors, and the correlation coefficient statistics. The results indicate that the FNN is more accurate and performed more consistently than alternative approaches employed in estimating drying rate.     

A novel methodology for the monitoring of the agricultural production process based on wireless sensor networks

Precision agriculture is a field which provides one of the most suitable scenarios for the deployment of wireless sensor networks (WSNs). The particular characteristics of agricultural environments - which may vary significantly with location - make WSNs a key technology able to provide accurate knowledge to farmers. This knowledge represents a valuable resource because it enables real-time decision making with regard to issues such as establishing water saving policies while providing adequate irrigation and choosing the right time to harvest the fruit based on its maturity.This article proposes a methodology consisting of a set of well-defined phases that cover the complete life cycle of WSN applications for agricultural monitoring. We have studied different existing real-world scenarios where WSNs are being applied. Based on this study we have discovered that there exist significant commonalities but no methodology that specifies the best practices that should be used in the general, crop-independent case. The lack of a general methodology negatively impacts the amount of effort, development time, and cost of developing applications.     

A morphological assessment system for ‘show quality’ bovine livestock based on image analysis

Morphological assessment is one important parameter considered in conservation and improvement programs for bovine livestock. This assessment process consists of scoring an animal based on its morphology and is normally carried out by highly qualified staff. These animals are all of agreed ‘show quality’ and hence they are morphologically very similar.This paper presents a system designed to provide an assessment based on a lateral image of the cow. The system consists of two main parts: a feature extraction stage, to reduce the information on the cow in the image to a set of parameters, and a neural network stage to provide a score based on that set of parameters. For the image analysis section, a model of the animal is constructed by means of the point distribution model (PDM) technique. Later, that model is used in the searching process within each image, which is implemented using genetic algorithms (GAs). As a result of this stage, the vector of weights that describe the deviation of the given shape from the mean is obtained. This vector is used in the second stage, where a multilayer perceptron is trained to provide the desired assessment, using the scores given by experts for selected cows.The system has been tested with 138 images corresponding to 44 individuals of a special rustic breed, with very promising results, given that the information contained in only one view of the cow can not be considered complete.     

探讨基于云计算的设施农业云系统设计

讨论了云计算以及设施农业的发展和特点,从理论上提出了建立设施农业云系统(FACS)来解决农业研究问题,并从总体架构、关键技术等方面进行了详细的论述。以农业研究协同化为例,描述了FACS在解决该类问题时的过程。     

基于无线传感器网络和虚拟仪器的农残检测系统的建立

果蔬中的农药残留问题一直备受关注.为了实现对果蔬中农药残留的快速检测,基于酶抑制法设计了一种农药残留快速检测系统.采用光频传感器采集农药残留量信息,通过基于nRF24L01的无线传感器网络将其发送到上位机,最后在上位机利用虚拟仪器语言LabVIEW开发一套集数据采集、存储、回放和分析于一体的虚拟检测系统.利用该系统对甲...     

基于全卷积网络的哺乳母猪图像分割

猪舍场景下,光照变化、母猪体表颜色不均及与环境颜色对比度不大、母猪与仔猪的粘连等,均给目标分割带来很大的困难。该文提出了基于全卷积网络(FCN,fully convolutional networks)的哺乳母猪图像分割算法。以VGG16为基础网络,采用融合深层抽象特征与浅层细节特征并将融合的特征图上采样8倍的跳跃式结构,设计哺乳母猪分割的FCN。利用Caffe深度学习框架,以7栏伴有不同日龄仔猪的3811幅哺乳母猪训练样本进行母猪分割FCN训练,在另外21栏的523幅哺乳母猪测试集上的分割结果表明:该算法可有效避免光线变化、母猪颜色不均、小猪遮挡与粘连等影响,实现完整的哺乳母猪区域分割;分割的平均准确率达到99.28%,平均区域重合度达到95.16%,平均速度达到0.22 s/幅。与深度卷积网络的SDS(simultaneous detection and segmentation)及传统的基于图论的图像分割、基于水平集的图像分割方法做了对比试验,该文分割方法平均区域重合度分别比这3种方法高出9.99、31.96和26.44个百分点,有较好的泛化性和鲁棒性,实现了猪舍场景下哺乳母猪准确、快速分割,可为猪只图像分割提供了技术参考。     

分布式多源农林物联网感知数据共享平台研发

由于农业和林业传感器种类繁多、数据传输协议多样,在各个物联网数据应用系统之间形成了信息孤单现象,难以实现物联网传感器数据的交互共享。因此,该文以农林领域常见传感器数据为研究对象,针对不同类型传感器节点及采集数据差异性等特点,采用符号表示法表达传感器节点数据,设计了通用数据交互格式;针对其海量性特点,采用分布式面向服务的结构方法及成熟的开发技术,设计了分布式多源农林物联网感知数据共享平台。该平台由数据中心子系统、数据适配子系统、数据存储子系统、数据发布子系统及数据传输总线5部分组成,分别实现了传感器节点注册、多源差异数据的接入适配、数据的分布式存储、数据标准化发布及数据传输等功能,为农林领域不同物联网设备与数据应用系统之间架起了数据桥梁,实现了农业和林业物联网感知数据的统一管理。目前,平台分别接入了顺义、新疆、杨凌、通州等17个合作单位的550个传感器节点数据,接入数据量每天超过10 000条,运行状态良好。