Development of a virtual reality GIS using stereo vision

This paper presents an integrated virtual reality geographic information system (VRGIS) based on computational stereo. In this system, three-dimensional (3D) and 360° panoramic scenes are reconstructed from multiple stereo image pairs acquired with a mobile stereo machine vision system. The spatial coordinates of panoramic scenes are registered to electronic maps to obtain a real scale VRGIS that is capable of efficient retrieval of spatial information and allows for navigation in virtual reality. A software program was implemented for image processing, coordinate transformation, 3D surface model building, data fusion, digital mapping, and database management for the VRGIS. In setting up 3D models for the virtual reality environment, we focused on comparisons of pattern matching algorithms for creating disparity images from stereo pairs, and adopted the graph cut method and the non-parametric local transform method in our system. Following the computation of disparity images, they are transformed into cylindrical coordinates and stitched into a 3D and 360° panoramic scene. In order to allow for virtual reality navigation, the panoramic scene is further cast into 3D triangular surface model for fast texture mapping using memoryless simplification algorithm. The effects of reducing the number of edges in a 3D surface model on computation time and discrepancy from 3D surface model with no edge reduction were also analyzed. Experimental results show that the discrepancy was about 13% when 80% edge reduction was applied to create the approximate 3D surface model. This indicates a significant computational and memory reduction while the computer graphic display of the scene model was still visually satisfactory. Finally, multiple 3D scenes were integrated into a site on the electronic map permitting the user to navigate through location to location in a virtual reality environment. The developed system may serve as a versatile platform in establishing field information for many applications such as precision agriculture, vehicle automation, geographic visualization, archaeology site modelling, etc.     

遥操作农田信息采集机器人控制系统研究

提出了一种基于遥操作机器人的农田信息采集方法,设计了新型农田信息采集机器人的控制系统并制造了移动机器人样机.主控计算机与车载网络摄像机构成无线局域网,实现视觉图像传输.在遥操作模式下,操作人员通过计算机导航软件控制机器人的动作,实现遥控导航和信号采集,可为精确农业提供原始数据.试验结果表明,机器人工作可靠性高、稳定性好.     

移动GIS在现代农业中的应用研究进展

摘要：移动GIS在农业上的应用是实现和发展现代农业的必然过程及手段,本文介绍了移动GIS及其在现代农业中的农田信息采集与查询、农业气象监测、田间精准施肥查询和病虫害监控与预报方面上的应用,分析了移动GIS的长处与不足,为其在今后更好的服务现代农业提供借鉴和指导。     

Mapping radiation interception in row-structured orchards using 3D simulation and high-resolution airborne imagery acquired from a UAV

This study was conducted to model the fraction of intercepted photosynthetically active radiation (fIPAR) in heterogeneous row-structured orchards, and to develop methodologies for accurate mapping of the instantaneous fIPAR at field scale using remote sensing imagery. The generation of high-resolution maps delineating the spatial variation of the radiation interception is critical for precision agriculture purposes such as adjusting management actions and harvesting in homogeneous within-field areas. Scaling-up and model inversion methods were investigated to estimate fIPAR using the 3D radiative transfer model, Forest Light Interaction Model (FLIGHT). The model was tested against airborne and field measurements of canopy reflectance and fIPAR acquired on two commercial peach and citrus orchards, where study plots showing a gradient in the canopy structure were selected. High-resolution airborne multi-spectral imagery was acquired at 10?nm bandwidth and 150?mm spatial resolution using a miniaturized multi-spectral camera on board an unmanned aerial vehicle (UAV). In addition, simulations of the land surface bidirectional reflectance were conducted to understand the relationships between canopy architecture and fIPAR. Input parameters used for the canopy model, such as the leaf and soil optical properties, canopy architecture, and sun geometry were studied in order to assess the effect of these inputs on canopy reflectance, vegetation indices and fIPAR. The 3D canopy model approach used to simulate the discontinuous row-tree canopies yielded spectral RMSE values below 0.03 (visible region) and below 0.05 (near-infrared) when compared against airborne canopy reflectance imagery acquired over the sites under study. The FLIGHT model assessment conducted for fIPAR estimation against field measurements yielded RMSE values below 0.08. The simulations conducted suggested the usefulness of these modeling methods in heterogeneous row-structured orchards, and the high sensitivity of the normalized difference vegetation index and fIPAR to background, row orientation, percentage cover and sun geometry. Mapping fIPAR from high-resolution airborne imagery through scaling-up and model inversion methods conducted with the 3D model yielded RMSE error values below 0.09 for the scaling-up approach, and below 0.10 for the model inversion conducted with a look-up table. The generation of intercepted radiation maps in row-structured tree orchards is demonstrated to be feasible using a miniaturized multi-spectral camera on board UAV platforms for precision agriculture purposes.     

Ground truth evaluation of computer vision based 3D reconstruction of synthesized and real plant images

There is an increasing interest in using 3D computer vision in precision agriculture. This calls for better quantitative evaluation and understanding of computer vision methods. This paper proposes a test framework using ray traced crop scenes that allows in-depth analysis of algorithm performance and finds the optimal hardware and light source setup before investing in expensive equipment and field experiments. It was expected to be a valuable tool to structure the otherwise incomprehensibly large information space and to see relationships between parameter configurations and crop features. Images of real plants with similar structural categories were annotated manually for comparison in order to validate the performance results on the synthesised images. The results showed substantial correlation between synthesized and real plants, but only when all error sources were accounted for in the simulation. However, there were exceptions where there were structural differences between the virtual plant and the real plant that were unaccounted for by its category. The test framework was evaluated to be a valuable tool to uncover information from complex data structures.     

Mobile low-cost 3D camera maize crop height measurements under field conditions

To tackle global challenges such as food supply and renewable energy provision, the improvement of efficiency and productivity in agriculture is of high importance. Site-specific information about crop height plays an important role in reaching these goals. Crop height can be derived with a variety of approaches including the analysis of three-dimensional (3D) geodata. Crop height values derived from 3D geodata of maize (1.88 and 2.35 m average height) captured with a low-cost 3D camera were examined. Data were collected with a unique measurement setup including the mobile mounting of the 3D camera, and data acquisition under field conditions including wind and sunlight. Furthermore, the data were located in a global co-ordinate system with a straightforward approach, which can strongly reduce computational efforts and which can subsequently support near real-time data processing in the field. Based upon a comparison between crop height values derived from 3D geodata captured with the low-cost approach, and high-end terrestrial laser scanning reference data, minimum RMS and standard deviation values of 0.13 m (6.91% of average crop height), and maximum R² values of 0.79 were achieved. It can be concluded that the crop height measurements derived from data captured with the introduced setup can provide valuable input for tasks such as biomass estimation. Overall, the setup is considered to be a valuable extension for agricultural machines which will provide complementary crop height measurements for various agricultural applications.     

A flexible unmanned aerial vehicle for precision agriculture

An unmanned aerial vehicle (??VIPtero??) was assembled and tested with the aim of developing a flexible and powerful tool for site-specific vineyard management. The system comprised a six-rotor aerial platform capable of flying autonomously to a predetermined point in space, and of a pitch and roll compensated multi-spectral camera for vegetation canopy reflectance recording. Before the flight campaign, the camera accuracy was evaluated against high resolution ground-based measurements, made with a field spectrometer. Then, ??VIPtero?? performed the flight in an experimental vineyard in Central Italy, acquiring 63 multi-spectral images during 10?min of flight completed almost autonomously. Images were analysed and classified vigour maps were produced based on normalized difference vegetation index. The resulting vigour maps showed clearly crop heterogeneity conditions, in good agreement with ground-based observations. The system provided very promising results that encourage its development as a tool for precision agriculture application in small crops.     

Site-specific management units in a commercial maize plot delineated using very high resolution remote sensing and soil properties mapping

The joint use of satellite imagery and digital soil maps derived from soil sampling is investigated in the present paper with the goal of proposing site-specific management units (SSMU) within a commercial field plot. Very high resolution Quickbird imagery has been used to derive leaf area index (LAI) maps in maize canopies in two different years. Soil properties maps were obtained from the interpolation of ion concentrations (Na, Mg, Ca, K and P) and texture determined in soil samples and also from automatic readings of electromagnetic induction (EMI) readings taken with a mobile sensor.Links between the image-derived LAI and soil properties were established, making it possible to differentiate units within fields subject to abiotic stress associated with soil sodicity, a small water-holding capacity or flooding constraints. In accordance with the previous findings, the delineation of SSMUs is proposed, describing those field areas susceptible of variable-rate management for agricultural inputs such as water or fertilizing, or soil limitation correctors such as gypsum application in the case of sodicity problems. This demonstrates the suitability of spatial information technologies such as remote sensing and digital soil mapping in the context of precision agriculture.     

Science mapping approach to analyze the research evolution on precision agriculture: world,EU and Italian situation

The relevance of precision agriculture produced a growth of the related literature over the years. However, a structured analysis of the published material is still missing. Thus, this study attempts to analyze the global scientific output of precision agriculture researches published during the period 2000–2016. By using a science mapping approach, mainly based on the application of network analysis tools, it was possible to investigate pivotal aspects of this research field such as publication trends, research topics and their geographical distribution. Using the Scopus database 17,756 scientific publications were retrieved from the chosen period. The number of publications increased after 2006, highlighting the vibrancy of the field. By authoring 35% of the publications, U.S.A. and China were the most active knowledge producer countries. Moreover, the generation of time resolved maps allowed us to identify agriculture engineering, computer science and agriculture studies as three main research areas characterizing precision agriculture panorama. The paper discusses the distribution of these topics at global level, among European countries and in Italy. Overall, this analysis represents the first holistic view of precision agriculture research providing valuable information for farmers, policy makers and researchers.     

全球智慧农业领域研究态势分析

[目的/意义]通过对全球智慧农业领域的研究态势进行分析,以期为当下及今后中国智慧农业领域的科技创新与管理决策提供数据参考和情报支撑.[方法/过程]基于Web of Science数据库,利用文献计量和知识图谱等方法,针对全球范围内智慧农业的研究概况、研究群体的竞争力、研究重点与热点等进行深入分析及可视化展示.[结果/结...     

Seeing is Believing: The Role of Field Days and Tours in Precision Agriculture Education

The traditional role of field days and tours has been to introduce growers and agricultural professionals to new technologies and techniques so that the audience could see how these technologies or techniques could be practically used and applied. Based on this concept, the use of field days or tours to demonstrate the radically new technologies and site-specific management techniques behind precision farming is a perfect application of these tools. Indeed, a survey of precision farming field days held in a number of states found that field days were beneficial in showing growers and agricultural professionals global positioning systems, yield monitoring systems, techniques for grid soil sampling, software for geographic information systems, vehicle guidance systems, variable-rate application equipment, and a host of other technologies and processes. In particular, hands-on experiences, such as field demonstrations, guided sampling activities, and combine harvesting demonstrations are extremely well received and valuable. Indoor seminars featuring farmer panels, side-by-side software demonstrations, and demonstrations of geographic information systems have received high marks by participants. The survey found that a field day must be centered on a well-defined objective and a thorough understanding of the needs of the audience. Survey respondents unanimously agreed that precision farming field days and tours will be even more important as future advances in technology and management techniques are discovered. However, future precision agriculture field days or tours must be coupled with other issues or topics where precision agriculture technologies can be used to solve a practical problem and enhance management practices.     

Zone mapping application for precision-farming: a decision support tool for variable rate application

A web-based decision support tool, zone mapping application for precision farming (ZoneMAP, ), has been developed to automatically determine the optimal number of management zones and delineate them using satellite imagery and field data provided by users. Application rates, such as of fertilizer, can be prescribed for each zone and downloaded in a variety of formats to ensure compatibility with GPS-enabled farming equipment. ZoneMAP is linked to Digital Northern Great Plains, a web-based application which hosts an archive of satellite imagery, as well as high resolution imagery from airborne sensors. Management zones created by ZoneMAP mapped natural variation of the soil organic matter and other nutrients relatively well and were consistent with zone maps created by traditional means. The results demonstrated that ZoneMAP can serve as an effective and easy-to-use tool for those who practice precision agriculture.     

基于MapObjects的农田信息采集与处理系统的研究

为解决精细农业中田间信息的采集与分析处理的问题,建立了一个基于MapObjects的农田信息采集与处理系统。该系统在VB开发环境下利用MapObjects和DGPS实现了农田信息采集功能;利用MapObjects的图形处理功能,实现了对已有农田信息的可视化分析和管理的功能。该系统的使用,为精细农业的开展,提供了一种新的信息采集和处理方法。     

精准农业中农田信息传输方式的研究进展

基于农田信息传输在精准农业中的重要作用,评述了有线传输介质和无线传输介质信息传输的特点,以及不同的传输方式应用领域和研究开发前景;通过对不同的信息传输方式综合性能的比较分析,指出精准农业中信息传输存在的问题,并就今后的发展提出自己的见解。     

计算机与信息技术在农业上的应用探究

本文对计算机与信息技术在农业基层结构中发挥的作用进行论述,指出计算机、信息 技术在农业领域应用中存在的问题,并通过3S 技术、生态化信息框架技术、精准农业等,对计算 机信息技术与农业领域的融合路径进行研究。     

Spider Mite Detection and Canopy Component Mapping in Cotton Using Hyperspectral Imagery and Spectral Mixture Analysis

Spectral mixture analysis and hyperspectral remote sensing are analytical and hardware tools new to precision agriculture. They can allow detection and identification of various crop stresses and other plant and canopy characteristics through analysis of their spectral signatures. One stressor in cotton, the strawberry spider mite (Tetranychus turkestani U.N.), feeds on plants causing leaf puckering and reddish discoloration in early stages of infestation and leaf drop later. To determine the feasibility of detecting the damage caused by this pest at the field level, AVIRIS imagery was collected from USDA-ARS cotton research fields at Shafter, CA on 4 dates in 1999. Additionally, cotton plants and soil were imaged in situ in 10 nm increments from 450 to 1050 nm with a liquid-crystal tunable-filter camera system. Mite-damaged areas on leaves, healthy leaves, tilled shaded soil, and tilled sunlit soil were chosen as reference endmembers and used in a constrained linear spectral mixture analysis to unmix the AVIRIS data producing fractional abundance maps. The procedure successfully distinguished between adjacent mite-free and mite-infested cotton fields although shading due to sun angle differences between dates was a complicating factor. The resulting healthy plant, soil, mite-damaged, and shade fraction maps showed the distribution and relative abundance of these endmembers in the fields. These hardware and software technologies can identify the location, spatial extent, and severity of crop stresses for use in precision agriculture.     

丘陵山地精准农业技术体系的分析与展望

精准农业技术是科技含量最高、集成综合性最强的现代农业生产管理技术之一。其核心思想是实时采集、处理空间差异。以地理信息系统、遥感和全球卫星定位系统为主的地理信息技术是精准农业技术体系的核心技术。本文在分析了丘陵山地精准农业五大技术环节构建丘陵山地精准农业技术体系、农田信息获取、农田信息管理和分析、决策分析、决策的田间实施基础上，对丘陵山地精准农业技术体系进行了分析，并提出地理信息技术在丘陵山地精准农业方面研究的主要内容和方向。     

精准农业技术对生态环境的影响评价研究进展

精准农业是保证作物产量和农业环境质量双赢的重要途径。综述了水肥精准管理和病虫草害精准管理的生态环境效益,精准农业通过节省资源投入、减少资源损失、提高利用效率,降低农业生产对土壤和水体环境的污染。但目前的评价内容主要集中在资源投入、残留损失、利用效率以及水土质量方面;其次,由于田间变量信息的获取途径不一致、模型预测和田间应用效果的差异、土壤质地以及作物种类的不同等原因,导致精准农业的环境效益评价结果不确定性较大。未来研究应建立精准农业技术田间定位试验,进行长期的生态环境效益评价研究,为精准农业技术的推广和应用提供数据支持。     

广角镜头的摄像机非线性标定技术

在路面检测的计算机视觉系统中,采用了线结构光照明和摄像机采集外界三维信息。为了实现在大范围内的测量,摄像机采用了广角镜头摄像,同时也引入了比较大的图像畸变。为了精确标定摄像机,采用比较接近实际情况的几何模型,并采用非线性迭代解方程的方法对摄像机进行标定,求解摄像机的内外参数。实验精度可以达到0.1mm。该方法简洁实用,可以采用这种方法在实际当中对系统进行标定。     

The potential of automatic methods of classification to identify leaf diseases from multispectral images

Three methods of automatic classification of leaf diseases are described based on high-resolution multispectral stereo images. Leaf diseases are economically important as they can cause a loss of yield. Early and reliable detection of leaf diseases has important practical relevance, especially in the context of precision agriculture for localized treatment with fungicides. We took stereo images of single sugar beet leaves with two cameras (RGB and multispectral) in a laboratory under well controlled illumination conditions. The leaves were either healthy or infected with the leaf spot pathogen Cercospora beticola or the rust fungus Uromyces betae. To fuse information from the two sensors, we generated 3-D models of the leaves. We discuss the potential of two pixelwise methods of classification: k-nearest neighbour and an adaptive Bayes classification with minimum risk assuming a Gaussian mixture model. The medians of pixelwise classification rates achieved in our experiments are 91% for Cercospora beticola and 86% for Uromyces betae. In addition, we investigated the potential of contextual classification with the so called conditional random field method, which seemed to eliminate the typical errors of pixelwise classification.