UAV systems for parameter identification in agriculture

George, Eobin Alex; Tiwari, Gaurav; Yadav, R. N.; Peters, Edward S.; Sadana, Srishti

doi:10.1109/ghtc-sas.2013.6629929

Cited by 25 publications

(16 citation statements)

References 1 publication

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“…Due to their light weight, these sensors can be easily mounted on unmanned aerial vehicles (UAV). UAV equipped with consumer-grade cameras have been widely used in forestry [2,3], precision agriculture, and crop monitoring [4][5][6][7]. George et al monitored crop parameters using an were to (1) extract the crop height from imagery taken during the growing season and after harvest; and (2) evaluate the orthomosics and crop height derived from aerial imagery for crop type mapping.…”

Section: Introductionmentioning

confidence: 99%

Evaluation of Orthomosics and Digital Surface Models Derived from Aerial Imagery for Crop Type Mapping

Yang

Song

et al. 2017

Remote Sensing

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Orthomosics and digital surface models (DSM) derived from aerial imagery, acquired by consumer-grade cameras, have the potential for crop type mapping. In this study, a novel method was proposed for extracting the crop height from DSM and for evaluating the orthomosics and crop height for the identification of crop types (mainly corn, cotton, and sorghum). The crop height was extracted by subtracting the DSM derived during the crop growing season from that derived after the crops were harvested. Then, the crops were identified from four-band aerial imagery (blue, green, red, and near-infrared) and the crop height, using an object-based classification method and a maximum likelihood method. The results showed that the extracted crop height had a very high linear correlation with the field measured crop height, with an R-squared value of 0.98. For the object-based method, crops could be identified from the four-band airborne imagery and crop height, with an overall accuracy of 97.50% and a kappa coefficient of 0.95, which were 2.52% and 0.04 higher than those without crop height, respectively. When considering the maximum likelihood, crops could be mapped from the four-band airborne imagery and crop height with an overall accuracy of 78.52% and a kappa coefficient of 0.67, which were 2.63% and 0.04 higher than those without crop height, respectively.

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Section: Introductionmentioning

confidence: 99%

Evaluation of Orthomosics and Digital Surface Models Derived from Aerial Imagery for Crop Type Mapping

Yang

Song

et al. 2017

Remote Sensing

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“…In the context of the article at hand we would like to emphasis 4 main application areas where UAVs are already used for various data-collection / sensing centered missions. These areas, which are briefly introduced below and for which the algorithm proposed in this article was mainly developed, are: Section 1.2.1: Precision agriculture [8,10,16,[22][23][24] Section 1.2.2: Disaster response and relief [9][10][11]13,16,21,[25][26][27] Section 1.2.3: Smart city applications and civil protection [9,11,16,21,28] Section 1.2.4: Land administration and wildlife protection [10,13,16,[29][30][31] 1.2.1. Precision agriculture…”

Section: Relevant Application Areasmentioning

confidence: 99%

“…Agriculture has of course been around for millennia, but the traditional approach to data collection is through sampling, which is inherently unreliable, imprecise and suffering from a large time delay between collecting the data and evaluating it. Especially in the context of pest monitoring [22] and extreme conditions [16], it would be of added benefit to be able to deploy a swarm of devices that can cover an even larger area while at the same time being able to dispatch one of its members to investigate specific locations without the swarm losing sight of the entire field to do so. UAVs can be deployed [23] during or after disasters to contribute to disaster management operations by e.g., performing logistic tasks such the delivery of hardware and resources to incident sites [9], recovering hazardous material [16], assisting with traffic monitoring and management [10,16] or monitoring structures and inspecting terrain [13] before human operatives are permitted access.…”

Section: Relevant Application Areasmentioning

confidence: 99%

“…This is especially the case in the context of environmental or weather monitoring missions [10]. The provided information has to be timely, as changing conditions may affect the data collection or the measured quantity and, if, for example, abnormal readings a reported, of high resolution and accuracy so as to identify critical issues as early and precise as possible [22]. Agriculture has of course been around for millennia but the traditional approach to data collection is through sampling, which is inherently unreliable, imprecise and suffering from a large time delay between collecting the data and evaluating it.…”

Section: Relevant Application Areasmentioning

confidence: 99%

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Nature-Inspired Drone Swarming for Real-Time Aerial Data-Collection Under Dynamic Operational Constraints

Hildmann

Kovács²,

Saffre

et al. 2019

Drones

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Unmanned Aerial Vehicles (UAVs) with acceptable performance are becoming commercially available at an affordable cost. Due to this, the use of drones for real-time data collection is becoming common practice by individual practitioners in the areas of e.g., precision agriculture and civil defense such as fire fighting. At the same time, as UAVs become a house-hold item, a plethora of issues—which can no longer be ignored and considered niche problems—are coming of age. These range from legal and ethical questions to technical matters such as how to implement and operate a communication infrastructure to maintain control over deployed devices. With these issues being addressed, approaches that focus on enabling collectives of devices to operate semi-autonomously are also increasing in relevance. In this article we present a nature-inspired algorithm that enables a UAV-swarm to operate as a collective which provides real-time data such as video footage. The collective is able to autonomously adapt to changing resolution requirements for specific locations within the area under surveillance. Our distributed approach significantly reduces the requirements on the communication infrastructure and mitigates the computational cost otherwise incurred. In addition, if the UAVs themselves were to be equipped with even rudimentary data-analysis capabilities, the swarm could react in real-time to the data it generates and self-regulate which locations within its operational area it focuses on. The approach was tested in a swarm of 25 UAVs; we present out preliminary performance evaluation.

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“…These applications typically monitor the phenomenon of interest in real-time and relay the corresponding data to a central platform to allow an effective and timely response [1], [2]. Surveillance systems are being used for both military and civilian operations [3]- [6] and, therefore, it is imperative to design these systems for different deployment scenarios and conditions. More recently in early 2015, the Federal Aviation Administration (FAA) released its much anticipated regulations for the use of unmanned aircraft or Unmanned Aerial Vehicle (UAV) drones for commercial purposes in domestic airspace [7].…”

Section: Introductionmentioning

confidence: 99%

Cognitive Chaotic UWB-MIMO Detect-Avoid Radar for Autonomous UAV Navigation

Nijsure

Kaddoum

Mallat

et al. 2016

IEEE Trans. Intell. Transport. Syst.

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Abstract-A cognitive detect and avoid radar system based on chaotic UWB-MIMO waveform design to enable autonomous UAV navigation is presented. A Dirichlet-Process-Mixture-Model (DPMM) based Bayesian clustering approach to discriminate extended targets and a Change-Point (CP) detection algorithm are applied for the autonomous tracking and identification of potential collision threats. A DPMM based clustering mechanism does not rely upon any a priori target scene assumptions and facilitates online multivariate data clustering/classification for an arbitrary number of targets. Furthermore, this radar system utilizes a cognitive mechanism to select efficient c haotic waveforms to facilitate enhanced target detection and discrimination. We formulate the CP mechanism for the online tracking of target trajectories which present a collision threat to the UAV navigation and thus we supplement the conventional Kalman filter based tracking. Simulation results demonstrate a significant performance improvement for the DPMM-CP assisted detection as compared with direct generalized likelihood ratio based detection. Specifically, w e o bserve a 4 d B p erformance g ain i n target detection over conventional fixed U WB w aveforms a nd superior collision avoidance capability offered by the joint DPMM-CP mechanism.

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UAV systems for parameter identification in agriculture

Cited by 25 publications

References 1 publication

Evaluation of Orthomosics and Digital Surface Models Derived from Aerial Imagery for Crop Type Mapping

Evaluation of Orthomosics and Digital Surface Models Derived from Aerial Imagery for Crop Type Mapping

Nature-Inspired Drone Swarming for Real-Time Aerial Data-Collection Under Dynamic Operational Constraints

Cognitive Chaotic UWB-MIMO Detect-Avoid Radar for Autonomous UAV Navigation

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