Unmanned Aerial Vehicles in Agriculture: A Review of Perspective of Platform, Control, and Applications

Kim, Jeongeun; Ju, Chanyoung

doi:10.1109/access.2019.2932119

Cited by 385 publications

(267 citation statements)

References 91 publications

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“…Unmanned Aerial Vehicles (UAVs) are an example of an autonomous system that has seen diverse utilization in areas of agriculture, disaster relief, remote sensing, surveillance, etc. [1], [2], [3], [4], [5]. UAVs are often deployed in uncontrolled environments, and are hence required to adapt to dynamic conditions in real-time with minimal sacrifice to functionality and performance.…”

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confidence: 99%

Real-Time System Identification Using Deep Learning for Linear Processes With Application to Unmanned Aerial Vehicles

et al. 2020

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System identification is a key discipline within the field of automation that deals with inferring mathematical models of dynamic systems based on input-output measurements. Conventional identification methods require extensive data generation and are thus not suitable for real-time applications. In this paper, a novel real-time approach for the parametric identification of linear systems using Deep Learning (DL) and the Modified Relay Feedback Test (MRFT) is proposed. The proposed approach requires only a single steady-state cycle of MRFT, and guarantees stability and performance in the identification and control phases. The MRFT output is passed to a trained DL model that identifies the underlying process parameters in milliseconds. A novel modification to the Softmax function is derived to better conform the DL model for the process identification task. Quadrotor Unmanned Aerial Vehicle (UAV) attitude and altitude dynamics were used in simulation and experimentation to verify the presented approach. Results show the effectiveness and real-time capabilities of the proposed approach, which outperforms the conventional Prediction Error Method in terms of accuracy, robustness to biases, computational efficiency and data requirements.

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confidence: 99%

Real-Time System Identification Using Deep Learning for Linear Processes With Application to Unmanned Aerial Vehicles

et al. 2020

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“…Various studies are performed and put forward by various academicians and authors concerning the evolution of agriculture relating to the incubation of the concepts of the Internet of Things. Jeongeun Kim et al [64], reviewed different articles related to the induction of unmanned aerial vehicles in agriculture. The authors highlighted the recent trends, controls, perspectives, and platforms of UAV for agriculture.…”

Section: Related Workmentioning

confidence: 99%

Recent Developments of the Internet of Things in Agriculture: A Survey

2020

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A rise in the population has immensely increased the pressure on the agriculture sector. With the advent of technology, this decade is witnessing a shift from conventional approaches to the most advanced ones. The Internet of Things (IoT) has transformed both the quality and quantity of the agriculture sector. Hybridization of species along with the real-time monitoring of the farms paved a way for resource optimization. Scientists, research institutions, academicians, and most nations across the globe are moving towards the practice and execution of collaborative projects to explore the horizon of this field for serving mankind. The tech industry is racing to provide more optimal solutions. Inclusion of IoT, along with cloud computing, big data analytics, and wireless sensor networks can provide sufficient scope to predict, process, and analyze the situations and improve the activities in the real-time scenario. The concept of heterogeneity and interoperability of the devices by providing flexible, scalable, and durable methods, models are also opening new domains in this field. Therefore, this paper contributes towards the recent IoT technologies in the agriculture sector, along with the development of hardware and software systems. The public and private sector projects and startup's started all over the globe to provide smart and sustainable solutions in precision agriculture are also discussed. The current scenario, applications, research potential, limitations, and future aspects are briefly discussed. Based on the concepts of IoT a precision farming framework is also proposed in this article.

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“…The integration of spectral and geophysical UAS-based information offers a refined scale between airborne and ground surveys. Numerous studies and reviews have investigated the potential of UASs for various applications, e.g., in the fields of agriculture and forestry, structural geology, and sedimentology [1][2][3][4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Integrated Geological and Geophysical Mapping of a Carbonatite-Hosting Outcrop in Siilinjärvi, Finland, Using Unmanned Aerial Systems

et al. 2020

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Mapping geological outcrops is a crucial part of mineral exploration, mine planning and ore extraction. With the advent of unmanned aerial systems (UASs) for rapid spatial and spectral mapping, opportunities arise in fields where traditional ground-based approaches are established and trusted, but fail to cover sufficient area or compromise personal safety. Multi-sensor UAS are a technology that change geoscientific research, but they are still not routinely used for geological mapping in exploration and mining due to lack of trust in their added value and missing expertise and guidance in the selection and combination of drones and sensors. To address these limitations and highlight the potential of using UAS in exploration settings, we present an UAS multi-sensor mapping approach based on the integration of drone-borne photography, multi- and hyperspectral imaging and magnetics. Data are processed with conventional methods as well as innovative machine learning algorithms and validated by geological field mapping, yielding a comprehensive and geologically interpretable product. As a case study, we chose the northern extension of the Siilinjärvi apatite mine in Finland, in a brownfield exploration setting with plenty of ground truth data available and a survey area that is partly covered by vegetation. We conducted rapid UAS surveys from which we created a multi-layered data set to investigate properties of the ore-bearing carbonatite-glimmerite body. Our resulting geologic map discriminates between the principal lithologic units and distinguishes ore-bearing from waste rocks. Structural orientations and lithological units are deduced based on high-resolution, hyperspectral image-enhanced point clouds. UAS-based magnetic data allow an insight into their subsurface geometry through modeling based on magnetic interpretation. We validate our results via ground survey including rock specimen sampling, geochemical and mineralogical analysis and spectroscopic point measurements. We are convinced that the presented non-invasive, data-driven mapping approach can complement traditional workflows in mineral exploration as a flexible tool. Mapping products based on UAS data increase efficiency and maximize safety of the resource extraction process, and reduce expenses and incidental wastes.

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Unmanned Aerial Vehicles in Agriculture: A Review of Perspective of Platform, Control, and Applications

Cited by 385 publications

References 91 publications

Real-Time System Identification Using Deep Learning for Linear Processes With Application to Unmanned Aerial Vehicles

Real-Time System Identification Using Deep Learning for Linear Processes With Application to Unmanned Aerial Vehicles

Recent Developments of the Internet of Things in Agriculture: A Survey

Integrated Geological and Geophysical Mapping of a Carbonatite-Hosting Outcrop in Siilinjärvi, Finland, Using Unmanned Aerial Systems

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