Manual geo-rectification to improve the spatial accuracy of ortho-mosaics based on images from consumer-grade unmanned aerial vehicles (UAVs)

Precision Agriculture - The global spatial accuracy of ortho-mosaics based on images from consumer-grade unmanned aerial vehicles (UAVs) is relatively low. The use of ground control points (GCPs)...     

Assessing the accuracy of mosaics from unmanned aerial vehicle (UAV) imagery for precision agriculture purposes in wheat

High spatial resolution images taken by unmanned aerial vehicles (UAVs) have been shown to have the potential for monitoring agronomic and environmental variables. However, it is necessary to capture a large number of overlapped images that must be mosaicked together to produce a single and accurate ortho-image (also called an ortho-mosaicked image) representing the entire area of work. Thus, ground control points (GCPs) must be acquired to ensure the accuracy of the mosaicking process. UAV ortho-mosaics are becoming an important tool for early site-specific weed management (ESSWM), as the discrimination of small plants (crop and weeds) at early growth stages is subject to serious limitations using other types of remote platforms with coarse spatial resolutions, such as satellite or conventional aerial platforms. Small changes in flight altitude are crucial for low-altitude image acquisition because these variations can cause important differences in the spatial resolution of the ortho-images. Furthermore, a decrease of flying altitude reduces the area covered by each single overlapped image, which implies an increase of both the sequence of images and the complexity of the image mosaicking procedure to obtain an ortho-image covering the whole study area. This study was carried out in two wheat fields naturally infested by broad-leaved and grass weeds at a very early phenological stage. The geometric accuracy differences and crop line alignment among ortho-mosaics created from UAV image series were investigated while taking into account three different flight altitudes (30, 60 and 100 m) and a number of GCPs (from 11 to 45). The results did not show relevant differences in geo-referencing accuracy on the interval of altitudes studied. Similarly, the increase of the number of GCPs did not imply a relevant increase of geo-referencing accuracy. Therefore, the most important parameter to consider when choosing the flying altitude is the ortho-image spatial resolution required rather than the geo-referencing accuracy. Regarding the crop mis-alignment, the results showed that the overall errors were less than twice the spatial resolution, which did not break the crop line continuity at the studied spatial resolutions (pixels from 7.4 to 24.7 mm for 30, 60 and 100 m flying altitudes respectively) on the studied crop (early wheat). The results lead to the conclusion that a UAV flying at a range of 30 to 100 m altitude and using a moderate number of GCPs is able to generate ultra-high spatial resolution ortho-imagesortho-images with the geo-referencing accuracy required to map small weeds in wheat at a very early phenological stage. This is an ambitious agronomic objective that is being studied in a wide research program whose global aim is to create broad-leaved and grass weed maps in wheat crops for an effective ESSWM.     

基于无人机三维点云的玉米植株自动计数研究

植株计数是农民、育种专家等在整个作物生长季评估作物生长状况和管理实践的最常用方法之一,可用来进行合理的田间规划以及管理。针对高密度种植试验区高通量获取玉米自动株数方法匮乏的问题,本研究利用无人机遥感平台,获取田间314个不同基因型的玉米高密度育种小区的数码影像和激光雷达(light detection and ranging, LiDAR)点云数据,发展了一种结合玉米三维空间信息的固定窗口局部最大值算法,实现了高密度玉米育种小区成株数的自动检测,并对比了基于此两种不同数据源的检测精度。该方法以冠层高度模型(canopy height model, CHM)中包含的株高信息为基础,以玉米种植株距为固定窗口进行单株玉米种子点检测,并将检测到的种子点与目视解译的玉米位置进行空间匹配来进行精度的评估。结果表明,基于无人机数码影像构建3种空间分辨率CHM的综合检测精度分别为81.30%、83.11%和78.93%;基于无人机LiDAR的综合精度分别为82.33%、88.66%和81.46%;基于两种数据源构建的CHM,均在空间分辨率为0.05 m时,获得最佳的检测精度。此外,当空间分辨率相同时,...     

利用无人机高分辨率影像进行树木高度提取

无人机遥感技术在树木参数获取中具有重要作用。为探讨利用无人机高分辨率影像提取树高的可行性,本文选择邱集煤矿矿区森林公园为研究区,采用Pix4D软件对无人机采集的高分辨率影像进行处理,生成研究区正射影像和三维点云;利用最大类间方差法将三维点云分割为树木点云及树下地面点云两部分,由此提取树木顶端高度和地面平均高度,并将地面平均高度视为树木根部的高度,得到树木高度。研究表明:最大类间方差法能够准确分割树木点云和地面点云;利用无人机高分辨率影像进行树高提取是可行的,树木高度测量绝对误差小于80cm、相对误差绝对值最大为16.2%、标准误差为36.3cm;同时,树冠的形状会对树高测量造成影响,阔卵形树冠的法国梧桐和圆锥形树冠的圆柏高度标准误差分别为29.2和50.9cm,两者树高测量值与真实值决定系数分别为0.9920和0.8894,阔卵形树冠的法国梧桐测高精度明显高于圆锥形树冠的圆柏测高精度。      

植保无人机喷施锌锰型水稻降Cd叶面阻控剂的飞行参数研究

应用植保无人机喷施叶面阻控剂有利于提高喷施均匀度、减少劳动力成本、提升作业效率。为规范植保无人机喷施叶面阻控剂田间作业,确保作业技术效果,本研究以大疆T16植保无人机为作业机,研究了喷施"美鑫隆"锌锰型叶面阻控剂以降低水稻Cd积累的最佳飞行高度、飞行速度和喷雾剂量等参数。结果表明,喷施锌锰型叶面阻控剂最佳降镉(Cd)效果的植保无人机作业参数分别为:喷施叶面阻控剂质量分数为12%,喷头总流量为3.6 L·min~(-1),飞行高度3 m,飞行速度5 m·s~(-1)。第二年在最佳参数下喷施叶面阻控剂,黄华占和湘晚籼13号的糙米Cd含量分别比对照下降了0.24 mg·kg~(-1)和0.12 mg·kg~(-1),降Cd率分别达到52.7%和62.1%(P0.01)。     

我国农用无人机产业链分析

针对我国现阶段农用无人机产业混乱问题,按照产业链研发、生产、市场、服务等上下游拓展关系,对国内农用无人机产业链上各环节的现状进行研究。结果表明:1)我国农用无人机的研发存在与实际生产脱离的现象,无人机核心科学技术不能满足生产需求;2)现阶段国内虽有大量企业从事农用无人机生产,但无人机生产质量得不到保证,与国外相比存在很大差距;3)农用无人机市场规模巨大,与传统农用机械相比具有较多的优势和机会;4)农用无人机售后服务需要改进的地方较多,存在企业服务理念不明确现象。应警惕国内农用无人机产业混乱问题,建议在研发、生产、市场、服务各节点进行调整,并增强各环节间联系,以促进农用无人机在我国农业植保中的普及,进一步覆盖农业作物全程机械化生产、自动化生产的缺失。     

Optimization of the factors affecting droplet deposition in rice fields by rotary unmanned aerial vehicles (UAVs)

Precision Agriculture - Unmanned aerial vehicles (UAVs) are widely used in modern agricultural management for aviation spraying. Many factors affect the droplet deposition during quadrotor UAV...     

Imaging from manned ultra-light and unmanned aerial vehicles for estimating properties of spring wheat

This study investigates an imaging system based on a Rikola hyperspectral (HSI) and Nikon D800E (CIR) cameras installed on a manned ultralight aircraft Bekas Ch-32 for applications involving precision agriculture. The efficiency of this technical solution is compared with that of using Canon PowerShot SX260HS camera images acquired from helicopter-type unmanned aerial vehicle (UAV) to accomplish similar tasks. The criteria for comparison were the suitability of acquired images for modelling chlorophyll concentration in spring wheat and for estimating the normalized difference red edge (NDRE) index, which is conventionally obtained using OptRx proximal sensors. Hyperspectral image values used as explanatory variables in ordinary least squares regression explain 68 and 61% of the variance in chlorophyll concentration and NDRE, respectively and outperform other images. The advantage of hyperspectral imagery became negligible when applying geographically weighted regression to improve global regression models. The use of ultralight aircraft as a sensor platform for precision agriculture aimed aerial photography projects is suggested as currently the most cost-effective solution in Lithuania.     

The challenge of reproducing remote sensing data from satellites and unmanned aerial vehicles (UAVs) in the context of management zones and precision agriculture

Precision Agriculture - Mapping the within-field variability of crop status is of great importance in precision agriculture, which seeks to balance agronomic inputs with spatial crop demands....     

Applying colour-based feature extraction and transfer learning to develop a high throughput inference system for potato (Solanum tuberosum L.) stems with images from unmanned aerial vehicles after canopy consolidation

Precision Agriculture - Potato (Solanum tuberosum) stem density variation in the field can be used to inform harvest timing to improve tuber size distribution. Current methods for quantifying stem...     

基于无人机多光谱影像的荒漠草原典型物种识别

【目的】准确获取草原植物物种空间分布信息是草原生态系统生物多样性监测、群落重构与生态功能维持的重要基础。及时准确获取植物物种空间分布可以为草原植物物种信息提取提供有效技术手段。【方法】文章以无人机多光谱影像为基础,分别在像元尺度和对象尺度上开展了荒漠草原典型物种的信息提取方法研究。像元尺度上先定义样本计算样本可分离性,在选择不同分类器进行分类。而对象尺度上首先进行遥感影像尺度分割研究,选出最佳分割尺度。在此基础上,提取最优特征变量,并采用阈值分类法提取植被信息。【结果】高分辨率无人机多光谱数据能够为荒漠草原物种信息提取提供有效数据基础。面向对象影像分析技术的表现最好,总体精度85.16%,Kappa系数0.71,其中短花针茅的制图精度和用户精度分别为97.6%和86%;其次是支持向量机机器学习算法,其总体精度80.40%,Kappa系数0.70,短花针茅的制图精度和用户精度分别为90.08%和76.46%;而传统最大似然分类法的识别精度较低,总体精度为74.68%,Kappa系数0.64,短花针茅的制图精度和用户精度分别为72.40和81.96。【结论】无人机多光谱数据对于集中连片分布...     

草坪宁51号和草坪宁71号混合防除草坪杂草试验

本试验研究了草坪宁51号和草坪宁71对狗牙根草坪上杂草的防除效果,结果表明,在草坪杂草1～4叶期,用草坪宁51号和草坪宁71混合进行防除,对杂草的防除效果比单独使用时的防除效果好.其混合用量(按两种药剂的有效成分的重量11的比例混合)为4.5 ml/10m2时,对杂草的防除效果较好.混合使用化学除草剂,可以节省用药量,降低除草剂在草坪上的残留量,并且节省了人力和物力.     

玉米田化学除草试验研究与分析

在田间试验的基础上 ,通过不同时间的定点调查 ,分析 3种化学除草剂对玉米田的除草效果 ,筛选出对玉米田阔叶和禾本科两大类杂草均有较理想防效的除草剂。同时 ,分析了施药后药效持续时间对不同杂草除草效果的影响。     

Introduction of herbicide resistant gene （bar） into maize （Zea mays L.） via pollen tube pathway

The pollen tube pathway method of transformation has been reported to be successful in most crops,but less successfu in maize.DNA can be transferred by cutting the stigma following pollination and applying the DNA solution in a suitable period DNA presumably reaches the ovary by flowing down the pollen tube and then integrates into the just fertilized but undivided zygotic cells.To provide the molecular evidence for this procedure,the plasmids pGBIRC carrying a CaMV35S promoter-PPT acetyle transferase(bar)gene-nos terminator gene fusion construct were used.Total 3 276 seeds were produced from the ears treated with DNA.It was found that 35 seedlings were GUS assay positive,but less intense than that of the positive controls,of which 17 were PCR amplification positive.But,only 13 of the seeds from the plants treated with DNA containing the bar gene were found to be resistant compared with the negative control.Less than 1.07% of progeny seedlings tested expressed a herbicide positive reaction and polymerase chain reaction(PCR)with seedling DNA did detect the bar gene.Morphological variation was observed in six plants.We succeed in obtain PPT-resistant maize inbred lines via pollen tube pathway.     

植保无人机飞防助剂的筛选及其性能评价

无人机植保飞防技术因其省工、省时和省水等优势在中国多种农作物上推广应用.然而,在飞防作业过程中,农药雾滴的飘移和蒸发会造成药效降低、环境污染和作物药害等问题.本研究以70％吡蚜酮可湿性粉剂为试验药剂,采用液滴接触角分析、蒸发测定和雾滴检测等方法,研究5种表面活性剂及其不同添加量对农药药液润湿性、防蒸发性及沉积性能影响,...     

A new method to estimate the spatial correlation between planned and actual patch spraying of herbicides

Precision Agriculture - On/off patch spraying based on weed maps is used in site-specific weed management. Two prerequisites for realising patch spraying are accurate weed detection and targeting...     

A double-layer model for improving the estimation of wheat canopy nitrogen content from unmanned aerial vehicle multispectral imagery

The accurate and rapid estimation of canopy nitrogen content (CNC) in crops is the key to optimizing in-season nitrogen fertilizer application in precision agriculture. However, the determination of CNC from field sampling data for leaf area index (LAI), canopy photosynthetic pigments (CPP; including chlorophyll a, chlorophyll b and carotenoids) and leaf nitrogen concentration (LNC) can be time-consuming and costly. Here we evaluated the use of high-precision unmanned aerial vehicle (UAV) multispectral imagery for estimating the LAI, CPP and CNC of winter wheat over the whole growth period. A total of 23 spectral features (SFs; five original spectrum bands, 17 vegetation indices and the gray scale of the RGB image) and eight texture features (TFs; contrast, entropy, variance, mean, homogeneity, dissimilarity, second moment, and correlation) were selected as inputs for the models. Six machine learning methods, i.e., multiple stepwise regression (MSR), support vector regression (SVR), gradient boosting decision tree (GBDT), Gaussian process regression (GPR), back propagation neural network (BPNN) and radial basis function neural network (RBFNN), were compared for the retrieval of winter wheat LAI, CPP and CNC values, and a double-layer model was proposed for estimating CNC based on LAI and CPP. The results showed that the inversion of winter wheat LAI, CPP and CNC by the combination of SFs+TFs greatly improved the estimation accuracy compared with that by using only the SFs. The RBFNN and BPNN models outperformed the other machine learning models in estimating winter wheat LAI, CPP and CNC. The proposed double-layer models (R²=0.67–0.89, RMSE=13.63–23.71 mg g^–1, MAE=10.75–17.59 mg g^–1) performed better than the direct inversion models (R²=0.61–0.80, RMSE=18.01–25.12 mg g^–1, MAE=12.96–18.88 mg g^–1) in estimating winter wheat CNC. The best winter wheat CNC accuracy was obtained by the double-layer RBFNN model with SFs+TFs as inputs (R²=0.89, RMSE=13.63 mg g^–1, MAE=10.75 mg g^–1). The results of this study can provide guidance for the accurate and rapid determination of winter wheat canopy nitrogen content in the field.     

基于无人机航摄技术在土地整治项目中的应用研究

针对传统土地整治项目施工监管、后期监测效率不高的问题,本文通过低空无人机航摄和PhotoScan软件后期处理,对比生成的正射影像图和Google earth卫星图的分辨率和位置,发现该影像图能精准、高效的表征项目区影像数据变化,从而得到一种可用于土地整治项目各阶段动态观测的简单、快捷方法。     

Assessing the effect of temperature stressors on maize plants from the change in antioxidative activity

The effect of low (3°C) and high (44°C) temperatures on stress indices in young maize plants is examined. Unfavorable temperatures cause inhibition of the growth of axial organs, intensification of lipid peroxidation, and reduction of antioxidant activity, from the degree of suppression of which the intensity of the stress effect of abiotic factors on plants can be judged.