A Technical Study on UAV Characteristics for Precision Agriculture Applications and Associated Practical Challenges

Delavarpour, Nadia; Koparan, Cengiz; Nowatzki, John; Bajwa, Sreekala G.; Sun, Xiaorui

doi:10.3390/rs13061204

Cited by 210 publications

(97 citation statements)

References 99 publications

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“…For instance, plant height, yield, color, and other phenotypes of horticultural crops can be measured by using unmanned aircraft systems (UASs), remote sensing, and image technology, which elevated the efficiency for large‐scale phenotyping (Manivasagam and Rozenstein, 2020; Morell et al., 2016). The development and application of AI is closely combined with the research and development of UASs and robots (Delavarpour et al., 2021; Zhang et al., 2021b). With the breakthrough of self‐driving technology, commercial agricultural tools (UASs and robots) based on AI are rapidly emerging on the market.…”

Section: Smart Horticulture Is the Trend Of The Futurementioning

confidence: 99%

Combining novel technologies with interdisciplinary basic research to enhance horticultural crops

et al. 2021

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SUMMARY Horticultural crops mainly include fruits, vegetables, ornamental trees and flowers, and tea trees (Melaleuca alternifolia). They produce a variety of nutrients for the daily human diet in addition to the nutrition provided by staple crops, and some of them additionally possess ornamental and medicinal features. As such, horticultural crops make unique and important contributions to both food security and a colorful lifestyle. Under the current climate change scenario, the growing population and limited arable land means that agriculture, and especially horticulture, has been facing unprecedented challenges to meet the diverse demands of human daily life. Breeding horticultural crops with high quality and adaptability and establishing an effective system that combines cultivation, post‐harvest handling, and sales becomes increasingly imperative for horticultural production. This review discusses characteristic and recent research highlights in horticultural crops, focusing on the breeding of quality traits and the mechanisms that underpin them. It additionally addresses challenges and potential solutions in horticultural production and post‐harvest practices. Finally, we provide a prospective as to how emerging technologies can be implemented alongside interdisciplinary basic research to enhance our understanding and exploitation of horticultural crops.

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Section: Smart Horticulture Is the Trend Of The Futurementioning

confidence: 99%

Combining novel technologies with interdisciplinary basic research to enhance horticultural crops

et al. 2021

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“…UAVs, commonly known as drones, are characterized by their mobility, adaptive altitude, advanced wireless communication, and sensing capabilities [ 6 ]. Such characteristics motivate several industries to employ UAVs in different operating scenarios, including but not limited to the manufacturing industry [ 7 ], agriculture [ 8 ], and public safety [ 9 ]. In fact, the global market of drones for industrial applications is projected to grow to around USD 43 billion by 2024, reaching the compound annual growth rate of 20% between 2018 and 2024 [ 10 ].…”

Section: State-of-the-art and Related Workmentioning

confidence: 99%

Prototype Design and Experimental Evaluation of Autonomous Collaborative Communication System for Emerging Maritime Use Cases

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Saafi

et al. 2021

Sensors

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Automated systems have been seamlessly integrated into several industries as part of their industrial automation processes. Employing automated systems, such as autonomous vehicles, allows industries to increase productivity, benefit from a wide range of technologies and capabilities, and improve workplace safety. So far, most of the existing systems consider utilizing one type of autonomous vehicle. In this work, we propose a collaboration of different types of unmanned vehicles in maritime offshore scenarios. Providing high capacity, extended coverage, and better quality of services, autonomous collaborative systems can enable emerging maritime use cases, such as remote monitoring and navigation assistance. Motivated by these potential benefits, we propose the deployment of an Unmanned Surface Vehicle (USV) and an Unmanned Aerial Vehicle (UAV) in an autonomous collaborative communication system. Specifically, we design high-speed, directional communication links between a terrestrial control station and the two unmanned vehicles. Using measurement and simulation results, we evaluate the performance of the designed links in different communication scenarios and we show the benefits of employing multiple autonomous vehicles in the proposed communication system.

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“…Most of these applications are used to monitor crops, and the investigation of other types of vegetation within agricultural systems is needed. Weed control is a critical component in large-scale farming and the unexplored potential of thermal imagery using UAVs could boost the capabilities of site-specific weed management technology [55]. Therefore, the UAV-assisted thermal imagery could be used as a practical solution to identify glyphosate-susceptible and glyphosate-resistant weed populations based on canopy temperature.…”

Section: Introductionmentioning

confidence: 99%

UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection

et al. 2021

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The foundation of contemporary weed management practices in many parts of the world is glyphosate. However, dependency on the effectiveness of herbicide practices has led to overuse through continuous growth of crops resistant to a single mode of action. In order to provide a cost-effective weed management strategy that does not promote glyphosate-resistant weed biotypes, differences between resistant and susceptible biotypes have to be identified accurately in the field conditions. Unmanned Aerial Vehicle (UAV)-assisted thermal and multispectral remote sensing has potential for detecting biophysical characteristics of weed biotypes during the growing season, which includes distinguishing glyphosate-susceptible and glyphosate-resistant weed populations based on canopy temperature and deep learning driven weed identification algorithms. The objective of this study was to identify herbicide resistance after glyphosate application in true field conditions by analyzing the UAV-acquired thermal and multispectral response of kochia, waterhemp, redroot pigweed, and common ragweed. The data were processed in ArcGIS for raster classification as well as spectral comparison of glyphosate-resistant and glyphosate-susceptible weeds. The classification accuracy between the sensors and classification methods of maximum likelihood, random trees, and Support Vector Machine (SVM) were compared. The random trees classifier performed the best at 4 days after application (DAA) for kochia with 62.9% accuracy. The maximum likelihood classifier provided the highest performing result out of all classification methods with an accuracy of 75.2%. A commendable classification was made at 8 DAA where the random trees classifier attained an accuracy of 87.2%. However, thermal reflectance measurements as a predictor for glyphosate resistance within weed populations in field condition was unreliable due to its susceptibility to environmental conditions. Normalized Difference Vegetation Index (NDVI) and a composite reflectance of 842 nm, 705 nm, and 740 nm wavelength managed to provide better classification results than thermal in most cases.

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A Technical Study on UAV Characteristics for Precision Agriculture Applications and Associated Practical Challenges

Cited by 210 publications

References 99 publications

Combining novel technologies with interdisciplinary basic research to enhance horticultural crops

Combining novel technologies with interdisciplinary basic research to enhance horticultural crops

Prototype Design and Experimental Evaluation of Autonomous Collaborative Communication System for Emerging Maritime Use Cases

UAV-Assisted Thermal Infrared and Multispectral Imaging of Weed Canopies for Glyphosate Resistance Detection

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