Selecting patterns and features for between- and within- crop-row weed mapping using UAV-imagery

This paper approaches the problem of weed mapping for precision agriculture, using imagery provided by Unmanned Aerial Vehicles (UAVs) from sunflower and maize crops. Precision agriculture referred to weed control is mainly based on the design of early post-emergence site-specific control treatments according to weed coverage, where one of the most important challenges is the spectral similarity of crop and weed pixels in early growth stages. Our work tackles this problem in the context of object-based image analysis (OBIA) by means of supervised machine learning methods combined with pattern and feature selection techniques, devising a strategy for alleviating the user intervention in the system while not compromising the accuracy. This work firstly proposes a method for choosing a set of training patterns via clustering techniques so as to consider a representative set of the whole field data spectrum for the classification method. Furthermore, a feature selection method is used to obtain the best discriminating features from a set of several statistics and measures of different nature. Results from this research show that the proposed method for pattern selection is suitable and leads to the construction of robust sets of data. The exploitation of different statistical, spatial and texture metrics represents a new avenue with huge potential for between and within crop-row weed mapping via UAV-imagery and shows good synergy when complemented with OBIA. Finally, there are some measures (specially those linked to vegetation indexes) that are of great influence for weed mapping in both sunflower and maize crops.     

The application of remote sensing techniques to sugarcane (Saccharum spp. hybrid) production: a review of the literature

Remote sensing techniques provide timely, up‐to‐date and relatively accurate information for the management of sugarcane crop. This article reviews the literature on the application of remote sensing to sugarcane agriculture and highlights the challenges and opportunities pertinent to the success of this application. The aim of the review was to provide accurate and fundamental information relating the spectral properties of sugarcane to its agronomic, health and nutritional status characteristics that would be of importance to cane farmers and farm managers. The applications of the remote sensing techniques in sugarcane agriculture have been undertaken with particular emphasis on sugarcane classification and areal extent mapping, thermal age group identification, varietal discrimination, yield prediction and crop health and nutritional status monitoring. It can be concluded that by selecting appropriate spatial and spectral resolution as well as suitable processing techniques for extracting sugarcane spectral information, remotely sensed data should find use in sugarcane agriculture in all areas of application with satisfactory results.     

基于WSN的精准农业远程环境监测系统设计

准确和实时的获取农作物生长的环境信息是实施和推进精准农业的基本前提。在对现有农业环境监测系统和无线传感器网络充分研究的基础上,构建了基于无线传感器网络的精准农业监测系统的设计方案,介绍了系统的总体结构及软硬件设计,提出了三层监测网络结构,详述了无线传感器网络的构建及数据传输过程,结合GPRS网络实现了信息的远程实时交互...     

A vision-based method for weeds identification through the Bayesian decision theory

One of the objectives of precision agriculture is to minimize the volume of herbicides that are applied to the fields through the use of site-specific weed management systems. This paper outlines an automatic computer vision-based approach for the detection and differential spraying of weeds in corn crops. The method is designed for post-emergence herbicide applications where weeds and corn plants display similar spectral signatures and the weeds appear irregularly distributed within the crop's field. The proposed strategy involves two processes: image segmentation and decision making. Image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based measuring relationships between crop and weeds. The decision making determines the cells to be sprayed based on the computation of a posterior probability under a Bayesian framework. The a priori probability in this framework is computed taking into account the dynamic of the physical system (tractor) where the method is embedded. The main contributions of this paper are: (1) the combination of the image segmentation and decision making processes and (2) the decision making itself which exploits a previous knowledge which is mapped as the a priori probability. The performance of the method is illustrated by comparative analysis against some existing strategies.     

Geo-parcel-based crop classification in very-high-resolution images via hierarchical perception

ABSTRACT

The basic application of remote sensing is classifying surface objects in images. Traditional pixel-based or object-based classification methods are poorly suited to very high-resolution (VHR) images captured by remote sensors with high spatial resolutions. In the field of computer vision, deep learning has recently achieved great advances in natural image processing. Inspired by this, we propose a methodology guided by hierarchical perception to classify crops in VHR images based on geo-parcels. Geo-parcel-based crop classification is used in agriculture and in refined farmland management. The proposed methodology can be divided into three steps: zoning, location and quality. In the first step, the image is divided into blocks based on the road network. In the second step, geographical entities are extracted from every block defined in the zoning step. In the last step, the geographical entity types are identified based on the texture information. These steps provide mutual constraints. In each step, the information is extracted by neural networks that have been adapted to the VHR images. The experimental results indicate that our methodology performs well, with a precision greater than 90%. Furthermore, our methodology combines deep learning techniques and theory regarding image perception by humans, providing a valuable method for processing remote sensing information.     

基于ArcGIS与Illustrator的长城专题影像图制作

长城专题影像图作为长城测量项目重要的成果之一,是以航空遥感影像为基础,负载大量现势性强的基础空间数据和长城专题信息数据,从而可以满足长城修复、保护等各项工作。通过分析当前专题图制作的两种主要技术,提出基于地理信息系统软件（ArcGIS）和专业绘图软件（Illustrator）联合实现专题图制作的方法,以设计和制作长城专题影像图为例,详细介绍了该模式下专题影像图的整体设计、数据组织及处理、图廓整饰等。通过应用说明：这种混合制图方式不仅可提高绘图精度、丰富地图内容表示等方面具有明显优势,而且能提高绘图效率、降低工作成本,在专题地图制作方面具有广阔的应用前景。     

空间信息处理技术与农田地理信息系统的研究

为了满足实施精细农业定位作物管理中数据管理与分析的需要,在对空间信息处理技术研究的基础上设计开发了一个农田地理信息系统。系统除了具有农田信息管理与数据处理的功能以外,还提供了处理GPS数据与RS图像的功能,方便了3S(GIS、GPS、RS)技术的集成应用。文章介绍了系统中GPS数据的制图与平滑处理、地图坐标转换、RS图像的几何校正与坐标配准、空间插值分析以及农田信息统计和查询等主要的功能及其实现方法。在北京精准农业示范区的应用表明系统可以作为一种用于精细农业研究的数据分析与信息管理的工具。     

基于波谱库的特色农作物智能专家分类系统设计

农作物的长势监测和产量估算一直是遥感技术应用的重要方面,而一个好的农作物分类算法对于农作物产量和长势进行监测十分关键。目前对于一些特色农作物而言,这方面的研究比较缺乏。因此拳研究设计了符合特色农作物的长势监测和产量测算功能模块,将数据挖掘和知识发现应用到专家分类算法中,自行开发了适合农作物数据发现和挖掘的归纳学习算法,充分利用了波谱库中大量的波谱数据、相关属性和空间数据,形成了基于波谱库的特色农作物智能专家分类系统。     

Harris Hawks Optimizer with Graph Convolutional Network Based Weed Detection in Precision Agriculture

Precision agriculture includes the optimum and adequate use of resources depending on several variables that govern crop yield. Precision agriculture offers a novel solution utilizing a systematic technique for current agricultural problems like balancing production and environmental concerns. Weed control has become one of the significant problems in the agricultural sector. In traditional weed control, the entire field is treated uniformly by spraying the soil, a single herbicide dose, weed, and crops in the same way. For more precise farming, robots could accomplish targeted weed treatment if they could specifically find the location of the dispensable plant and identify the weed type. This may lessen by large margin utilization of agrochemicals on agricultural fields and favour sustainable agriculture. This study presents a Harris Hawks Optimizer with Graph Convolutional Network based Weed Detection (HHOGCN-WD) technique for Precision Agriculture. The HHOGCN-WD technique mainly focuses on identifying and classifying weeds for precision agriculture. For image pre-processing, the HHOGCN-WD model utilizes a bilateral normal filter (BNF) for noise removal. In addition, coupled convolutional neural network (CCNet) model is utilized to derive a set of feature vectors. To detect and classify weed, the GCN model is utilized with the HHO algorithm as a hyperparameter optimizer to improve the detection performance. The experimental results of the HHOGCN-WD technique are investigated under the benchmark dataset. The results indicate the promising performance of the presented HHOGCN-WD model over other recent approaches, with increased accuracy of 99.13%.     

基于NB-IOT的大田管理精准农业系统的设计与实现

针对传统规模化大田种植缺乏科学监测手段,农作物数据采集系统存在智能化程度不高、管理效率低下,难以实现对大田作物精准灌溉的问题,以及当前成熟商用的物联网通信技术存在低功耗与广覆盖难以两全的问题,开发出一个基于NB-IOT的大田管理精准农业系统,给出了系统的总体设计方案,详细阐释了系统的硬件与软件设计方法.该系统利用无线传...     

农田农情参数遥感监测进展及应用展望

农情参数是指反映作物生长过程及其产出的状态指标,关键农情参数主要包括作物长势、单产、物候和旱情等,可用于指导农田的生产管理。遥感是关键农情参数获取的有效手段,然而目前农情参数的遥感监测大多停留在大尺度、宏观监测的层面上,由于缺乏高时空分辨率、高准确度、低成本的农田信息获取技术,业务化的农田尺度农情参数获取受到了诸多因素的制约与限制。导致难以为农田生产管理提供及时的信息支持,这已经影响到精准耕作的发展与应用。文章在总结目前长势、单产、物候和旱情等几个主要农情参数遥感监测研究进展的基础上,分析了这些技术在农田尺度应用的瓶颈,并从新数据源和农情参数监测新模型两个角度出发,对农田尺度农情参数的获取进行了展望。     

Development of glacier mapping in Indian Himalaya: a review of approaches

The paper reviews the status of glacier mapping with special reference to the Indian Himalaya. The review provides information on various satellite remote sensing data interpretation methods used with special emphasis laid on recent semi-automated algorithms used for glacier and debris-cover mapping, along with their limitations and challenges. Further, the pragmatic solutions on offer are discussed, and the emerging areas of glacier mapping research are highlighted. The review also touches – contribution of Survey of India (SOI) and Geological Survey of India (GSI) in the glacier mapping. Finally, it discusses the wider range of spatial and spectral domains in which remote sensing data helps to inventories glaciers. The review also identifies gaps in using the latest techniques like Unmanned Aerial Vehicles (UAVs) and machine learning algorithms to improvise on the ongoing efforts. At last, the review provides an exhaustive list of references on glacier mapping from the Indian Himalaya as benefit to readers.     

The application of time-series MODIS NDVI profiles for the acquisition of crop information across Afghanistan

We investigated and developed a prototype crop information system integrating 250 m Moderate Resolution Imaging Spectroradiometer (MODIS) normalized difference vegetation index (NDVI) data with other available remotely sensed imagery, field data, and knowledge as part of a wider project monitoring opium and cereal crops. NDVI profiles exhibited large geographical variations in timing, height, shape, and number of peaks, with characteristics determined by underlying crop mixes, growth cycles, and agricultural practices. MODIS pixels were typically bigger than the field sizes, but profiles were indicators of crop phenology as the growth stages of the main first-cycle crops (opium poppy and cereals) were in phase. Profiles were used to investigate crop rotations, areas of newly exploited agriculture, localized variation in land management, and environmental factors such as water availability and disease. Near-real-time tracking of the current years’ profile provided forecasts of crop growth stages, early warning of drought, and mapping of affected areas. Derived data products and bulletins provided timely crop information to the UK Government and other international stakeholders to assist the development of counter-narcotic policy, plan activity, and measure progress. Results show the potential for transferring these techniques to other agricultural systems.     

Landscape metrics and change analysis of a national wildlife refuge at different spatial resolutions

For the past three decades, ecologists and biogeographers have increasingly incorporated remote sensing and geographical information systems (GISs) to inventory and analyse spatially organized data. Although there are many studies exploring the effects of fine resolution on remote sensing and GIS mapping, there is still a gap on how to identify the most appropriate spatial resolutions for studying landscapes and their structures and dynamics. This study investigated the effects of landscape changes over a 64-year study period at different spatial resolutions using four resampling schemes. The study was conducted on a national wildlife refuge of five land-use and land-cover (LULC) categories using aerial photos recorded in three distant years (1938, 1971, and 2001). This refuge has undergone major landscape changes in the last 64 years. Among the five LULC categories studied, the one that lost the most surface is agriculture; the most gain was made in forest and water. In terms of net change and swapping, agriculture and forest were the most dynamic categories in the National Wildlife Refuge. Our findings showed considerable spatial variability in landscape dynamics at different scales. We specifically observed that hard-classified maps with spatial resolutions of 30 m or finer provided better analysis of landscape dynamics, whereas with soft-classified maps it could go up to 90 m or finer. This implies that there is a range of optimum resolution that could allow for the use of medium-resolution data, such as Landsat, for reliable land change analysis. These findings offer further insight on landscape change analysis at different spatial resolutions and advance our understanding and knowledge on the effect of scale on landscape ecology.     

Mapping plant functional types from MODIS data using multisource evidential reasoning

Reliable information about the geographic distribution and abundance of major plant functional types (PFTs) around the world is increasingly needed for global change research. Using remote sensing techniques to map PFTs is a relatively recent field of research. This paper presents a method to map PFTs from the Moderate Resolution Imaging Spectroradiometer (MODIS) data using a multisource evidential reasoning (ER) algorithm. The method first utilizes a suite of improved and standard MODIS products to generate evidence measures for each PFT class. The multiple lines of evidence computed from input data are then combined using Dempster's Rule of combination. Finally, a decision rule based on maximum support is used to make classification decisions. The proposed method was tested over the states of Illinois, Indiana, Iowa, and North Dakota, USA where crops dominate. The Cropland Data Layer (CDL) data provided by the United States Department of Agriculture were employed to validate our new PFT maps and the current MODIS PFT product. Our preliminary results suggest that multisource data fusion is a promising approach to improve the mapping of PFTs. For several major PFT classes such as crop, trees, and grass and shrub, the PFT maps generated with the ER method provide greater spatial details compared to the MODIS PFT. The overall accuracies increased for all the four states, with the biggest improvement occurring in Iowa from 51% (MODIS) to 64% (ER). The overall kappa statistic also increased for all the four states, with the biggest improvement occurring in Iowa from 0.03 (MODIS) to 0.38 (ER). The paper concludes with a discussion of several methodological issues pertaining to the further improvement of the ER approach.     

A computer vision approach for weeds identification through Support Vector Machines

This paper outlines an automatic computer vision system for the identification of avena sterilis which is a special weed seed growing in cereal crops. The final goal is to reduce the quantity of herbicide to be sprayed as an important and necessary step for precision agriculture. So, only areas where the presence of weeds is important should be sprayed. The main problems for the identification of this kind of weed are its similar spectral signature with respect the crops and also its irregular distribution in the field. It has been designed a new strategy involving two processes: image segmentation and decision making. The image segmentation combines basic suitable image processing techniques in order to extract cells from the image as the low level units. Each cell is described by two area-based attributes measuring the relations among the crops and weeds. The decision making is based on the Support Vector Machines and determines if a cell must be sprayed. The main findings of this paper are reflected in the combination of the segmentation and the Support Vector Machines decision processes. Another important contribution of this approach is the minimum requirements of the system in terms of memory and computation power if compared with other previous works. The performance of the method is illustrated by comparative analysis against some existing strategies.     

基于物联网技术的设施农业在线监控系统

运用物联网技术、嵌入式技术和云服务器技术设计一套基于物联网技术的设施农业在线监控系统,该系统能实时监测农业环境参数信息和作物生长状况,实现对分散在各地的温室环境进行状态监测、设备远程智能控制及实时的在线数据、图像视频查询与信息服务。从而使温室的环境适宜作物生长,提高资源利用率。     

A conceptual framework to define the spatial resolution requirements for agricultural monitoring using remote sensing

Satellite remote sensing is an invaluable tool to monitor agricultural resources. However, spatial patterns in agricultural landscapes vary significantly across the Earth resulting in different imagery requirements depending on what part of the globe is observed. Furthermore, there is an increasing diversity of Earth observation instruments providing imagery with various configurations of spatial, temporal, spectral and angular resolutions. In terms of spatial resolution, the choice of imagery should be conditioned by knowing the appropriate spatial frequency at which the landscape must be sampled with the imaging instrument in order to provide the required information from the targeted fields. This paper presents a conceptual framework to define quantitatively such requirements for both crop area estimation and crop growth monitoring based on user-defined constraints. The methodological development is based on simulating how agricultural landscapes, and more specifically the fields covered by a crop of interest, are seen by instruments with increasingly coarser resolving power. The results are provided not only in terms of acceptable pixel size but also of pixel purity which is the degree of homogeneity with respect to the target crop. This trade-off between size and purity can be adjusted according to the end-user's requirements. The method is implemented over various agricultural landscapes with contrasting spatial patterns, demonstrating its operational applicability. This diagnostic approach can be used: (i) to guide users in choosing the most appropriate imagery for their application, (ii) to evaluate the adequacy of existing remote sensing systems for monitoring agriculture in different regions of the world and (iii) to provide guidelines for space agencies to design future instruments dedicated to agriculture monitoring.     

TM遥感与地块内冬小麦产量变异

卫星遥感可以为农作物的准确管理提供必要,及时并具有空间连续性的信息,但高成本一直是限制该项技术在农业上深入发展的主要障碍,利用价格相对较为低廉的TM卫星影像作为信息源来评价其对估测小区域内作物产量空间变异并为规划管理单元提供必要信息的可行性做了初步的研究,结果表明,利用TM图像所获得的植被指数能较好地反映小麦各生育时期的基本特点,两种植被指数（NDVI及RVI）都表现出一定程序的空间,而且都以小麦抽穗后期的变异程度为最大,而且,小麦生长发育的三个重要时期（分蘖期,抽穗期及拔节期）的两种植被指数之间具有极显著相关关系,两个试验地块小麦11月8日的归一化植被指数都与产量表现出了良好的相关关系,另外,两种植被指数在表现作物千粒重和亩穗数等产量指标信息方面,也有一定的效果。     

Computer Vision Onboard UAVs for Civilian Tasks

Computer vision is much more than a technique to sense and recover environmental information from an UAV. It should play a main role regarding UAVs’ functionality because of the big amount of information that can be extracted, its possible uses and applications, and its natural connection to human driven tasks, taking into account that vision is our main interface to world understanding. Our current research’s focus lays on the development of techniques that allow UAVs to maneuver in spaces using visual information as their main input source. This task involves the creation of techniques that allow an UAV to maneuver towards features of interest whenever a GPS signal is not reliable or sufficient, e.g. when signal dropouts occur (which usually happens in urban areas, when flying through terrestrial urban canyons or when operating on remote planetary bodies), or when tracking or inspecting visual targets—including moving ones—without knowing their exact UMT coordinates. This paper also investigates visual servoing control techniques that use velocity and position of suitable image features to compute the references for flight control. This paper aims to give a global view of the main aspects related to the research field of computer vision for UAVs, clustered in four main active research lines: visual servoing and control, stereo-based visual navigation, image processing algorithms for detection and tracking, and visual SLAM. Finally, the results of applying these techniques in several applications are presented and discussed: this study will encompass power line inspection, mobile target tracking, stereo distance estimation, mapping and positioning.