Mapping skips in sugarcane fields using object-based analysis of unmanned aerial vehicle (UAV) images

Souza, Carlos Eduardo Alves de; Lamparelli, Rubens Augusto Camargo; Rocha, Jansle Vieira; Magalhães, Paulo Sérgio Graziano

doi:10.1016/j.compag.2017.10.006

Cited by 54 publications

(28 citation statements)

References 42 publications

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“…Then, OBIA combines the spectral, topological, and contextual information of these objects to address complicated classification issues. Successful examples of OBIA applications include agricultural [24][25][26][27][28], grassland [29,30], and forest scenarios [31][32][33]. Therefore, the combination of UAV-based DSM and OBIA enables to tackle the significant challenge of automating image analysis [19], which represents a relevant advance in agronomy science.…”

Section: Introductionmentioning

confidence: 99%

3-D Characterization of Vineyards Using a Novel UAV Imagery-Based OBIA Procedure for Precision Viticulture Applications

et al. 2018

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Precision viticulture has arisen in recent years as a new approach in grape production. It is based on assessing field spatial variability and implementing site-specific management strategies, which can require georeferenced information of the three dimensional (3D) grapevine canopy structure as one of the input data. The 3D structure of vineyard fields can be generated applying photogrammetric techniques to aerial images collected with Unmanned Aerial Vehicles (UAVs), although processing the large amount of crop data embedded in 3D models is currently a bottleneck of this technology. To solve this limitation, a novel and robust object-based image analysis (OBIA) procedure based on Digital Surface Model (DSM) was developed for 3D grapevine characterization. The significance of this work relies on the developed OBIA algorithm which is fully automatic and self-adaptive to different crop-field conditions, classifying grapevines, and row gap (missing vine plants), and computing vine dimensions without any user intervention. The results obtained in three testing fields on two different dates showed high accuracy in the classification of grapevine area and row gaps, as well as minor errors in the estimates of grapevine height. In addition, this algorithm computed the position, projected area, and volume of every grapevine in the field, which increases the potential of this UAV-and OBIA-based technology as a tool for site-specific crop management applications.

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Section: Introductionmentioning

confidence: 99%

3-D Characterization of Vineyards Using a Novel UAV Imagery-Based OBIA Procedure for Precision Viticulture Applications

et al. 2018

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“…In Brazil, UAVs were deployed over sugarcane fields. Wachholz de Souza et al [8] described an object-based image analysis (OBIA) procedure for UAV images, designed to map and extract information about skips in sugarcane planting rows. German researchers examined the prospects of monitoring biophysical parameters and nitrogen content in wheat crops using images are entered into the database must be absolutely reliable [34].…”

Section: Introductionmentioning

confidence: 99%

Use of Unmanned Aerial Vehicles (UAVs) for Updating Farmland Cadastral Data in Areas Subject to Landslides

Puniach

Bieda

Ćwiąkała

et al. 2018

IJGI

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Abstract:The purpose of this study was to verify the applicability of unmanned aerial vehicles (UAVs) to update cadastral records in areas affected by landslides. Its authors intended to compare the accuracy of coordinates determined using different UAV data processing methods for points which form the framework of a cadastral database, and to find out whether products obtained as a result of such UAV data processing are sufficient to define the extent of changes in the cadastral objects. To achieve this, an experiment was designed to take place at the site of a landslide. The entire photogrammetry mission was planned to cover an area of more than 70 ha. Given the steep grade of the site, the UAV was flown over each line at a different, individually preset altitude, such as to ensure consistent mean shooting distance (height above ground level), and thus, appropriate ground sample distance (GSD; pixel size). The results were analyzed in four variants, differing from each other in terms of the number of control points used and the method of their measurement. This allowed identification of the factors that affect surveying accuracy and the indication of the cadastral data updatable based on an UAV photogrammetric survey.

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“…For the areas with weak-connection, we list four typical regions that are construction sites in urban, disaster regions in urban, blind coverage spots in the city, and the transportation road. In these areas, some recent studies use UAVs to offer an extended network coverage and perform some specified applications such Areas with weak-connection Urban construction sites Construction project management [119]- [121] Indoor construction monitoring [122], [123] Disaster regions Disaster surveillance [80], [124], [125] Emergency networks construction [126]- [129] Urban coverage blind spots Enhanced coverage in urban area [29], [80], [130]- [133] Patrolling and surveillance [134]- [139] Transportation systems Intelligent transportation systems [140]- [143] Connection between ground vehicles [144]- [147] Areas without network deployment Farms Survey of UAV in agriculture [63], [148] Imagery analysis of crops [149]- [153] Deserts Disaster monitoring [154]- [156] Geomorphological analysis [61], [155], [157] Military detection [158] Forests Trees and plants monitoring [159]- [162] Forest growing volume prediction [163], [164] Oceans Coastal environment analysis [165]- [168] Ocean environment monitoring [169]- [171] Marine science and observation [18]...…”

Section: B Uav-enabled Ioementioning

confidence: 99%

Unmanned aerial vehicle for internet of everything: Opportunities and challenges

Liu

Dai

Wang

et al. 2020

Computer Communications

162

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The recent advances in information and communication technology (ICT) have further extended Internet of Things (IoT) from the sole "things" aspect to the omnipotent role of "intelligent connection of things". Meanwhile, the concept of internet of everything (IoE) is presented as such an omnipotent extension of IoT. However, the IoE realization meets critical challenges including the restricted network coverage and the limited resource of existing network technologies. Recently, Unmanned Aerial Vehicles (UAVs) have attracted significant attentions attributed to their high mobility, low cost, and flexible deployment. Thus, UAVs may potentially overcome the challenges of IoE. This article presents a comprehensive survey on opportunities and challenges of UAV-enabled IoE. We first present three critical expectations of IoE: 1) scalability requiring a scalable network architecture with ubiquitous coverage, 2) intelligence requiring a global computing plane enabling intelligent things, 3) diversity requiring provisions of diverse applications. Thereafter, we review the enabling technologies to achieve these expectations and discuss four intrinsic constraints of IoE (i.e., coverage constraint, battery constraint, computing constraint, and security issues). We then present an overview of UAVs. We next discuss the opportunities brought by UAV to IoE. Additionally, we introduce a UAV-enabled IoE (Ue-IoE) solution by exploiting UAVs's mobility, in which we show that Ue-IoE can greatly enhance the scalability, intelligence and diversity of IoE. Finally, we outline the future directions in Ue-IoE.

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Mapping skips in sugarcane fields using object-based analysis of unmanned aerial vehicle (UAV) images

Cited by 54 publications

References 42 publications

3-D Characterization of Vineyards Using a Novel UAV Imagery-Based OBIA Procedure for Precision Viticulture Applications

3-D Characterization of Vineyards Using a Novel UAV Imagery-Based OBIA Procedure for Precision Viticulture Applications

Use of Unmanned Aerial Vehicles (UAVs) for Updating Farmland Cadastral Data in Areas Subject to Landslides

Unmanned aerial vehicle for internet of everything: Opportunities and challenges

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