UAV-based remote sensing in plant stress imagine using high-resolution thermal sensor for digital agriculture practices: a meta-review

Awais, Muhammad; Li, W.; Cheema, Muhammad Jehanzeb Masud; Zaman, Qamar; Shaheen, Asma; Aslam, Bilal; Zhu, Wenjing; Ajmal, Muhammad; Faheem, Muhammad; Hussain, Sajjad; Nadeem, Adeel Ahmad; Afzal, M. M.; Liu, C.

doi:10.1007/s13762-021-03801-5

Cited by 51 publications

(28 citation statements)

References 110 publications

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“…The field of remote sensing is no exception [79]. Intelligent interpretation of remote sensing is crucial to study in many areas, including environmental monitoring, land resources [80], crop monitoring [81] and yield estimation, forest carbon sink estimation [82], and national defense security [83]. Intelligent remote sensing interpretation is also an important requirement for national strategic development [8].…”

Section: A the Data Types Of Remote Sensingmentioning

confidence: 99%

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Jiao

Huang

Liu

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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Brain-inspired algorithms have become a new trend in next-generation artificial intelligence. Through research on brain science, the intelligence of remote sensing algorithms can be effectively improved. This paper summarizes and analyzes the essential properties of brain cognise learning and the recent advance of remote sensing interpretation. Firstly, this paper introduces the structural composition and the properties of the brain. Then, five represent brain-inspired algorithms are studied, including multiscale geometry analysis, compressed sensing, attention mechanism, reinforcement learning, and transfer learning. Next, this paper summarizes the data types of remote sensing, the development of typical applications of remote sensing interpretation and the implementations of remote sensing, including datasets, software, and hardware. Finally, the top ten open problems and the future direction of brain-inspired remote sensing interpretation are discussed. This work aims to comprehensively review the brain mechanisms and the development of remote sensing and to motivate future research on brain-inspired remote sensing interpretation.

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Section: A the Data Types Of Remote Sensingmentioning

confidence: 99%

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Jiao

Huang

Liu

et al. 2023

IEEE J. Sel. Top. Appl. Earth Observations Remote Sensing

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“…Besides the recently available and upcoming spaceborne imagine spectrometer data, such as the Environmental Mapping and Analysis Program (EnMAP) (Guanter et al, 2015), PRecursore IperSpettrale della Missione Applicativa (PRISMA) (Cogliati et al, 2021;Loizzo et al, 2019), Copernicus Hyperspectral Imaging Mission for the Environment (Rast et al, 2021) or the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECO-STRESS) data (Fisher et al, 2020), UAVs became a crucial platform with respect to sensor synergies. Several light multi-and hyperspectral VIS, NIR and SWIR, thermal and SIF sensors are available nowadays (overviews provided by Aasen et al (2018); Awais et al (2022);Herrmann and Berger (2021); Messina and Modica (2020); Neupane and Baysal-Gurel (2021); Yang et al (2017a)), which can be explored for model development or validation of physiological traits related to crop stress. Besides spectral synergies, the exploitation of multi-scale multi-sensor data allows to fill temporal gaps and improve spatial resolutions.…”

Section: Proposed Conceptional Framework: Integration Of Crop Growth ...mentioning

confidence: 99%

Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Berger

Machwitz

Kycko

et al. 2022

Remote Sensing of Environment

103

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“…Therefore, LST is a crucial parameter for the physical description of surface energy and water balance processes [3]. This important component has been demonstrated in numerous thermal infrared-based investigations and applications, such as vegetation monitoring [4], agriculture [5], hydrological modelling [6,7], monitoring of land use changes in wetlands, and evapotranspiration [8]. In agricultural applications, precision farming has become more dependent on TIR remote sensing technology to identify waterstressed crops, irrigation management, and plant diseases [9].…”

Section: Introductionmentioning

confidence: 99%

Comparative Evaluation of Land Surface Temperature Images from Unmanned Aerial Vehicle and Satellite Observation for Agricultural Areas Using In Situ Data

Awais

Hussain

et al. 2022

Agriculture

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Remotely-sensed data are a source of rich information and are valuable for precision agricultural tasks such as soil quality, plant disease analysis, crop stress assessment, and allowing for better management. It is necessary to validate the accuracy of land surface temperature (LST) that is acquired from an unmanned aerial vehicle (UAV) and satellite-based remote sensing and verify these data by a comparison with in situ LST. Comprehensive studies at the field scale are still needed to understand the suitability of UAV imagery and resolution, for which ground measurement is used as a reference. In this study, we examined the accuracy of surface temperature data that were obtained from a thermal infrared (TIR) sensor placed on a UAV. Accordingly, we evaluated the LST from the Landsat 8 satellite for the same specific periods. We used contact thermometers to measure LSTs in situ for comparison and evaluation. Between 18 August and 2 September 2020, UAV imagery and in situ measurements were carried out. The effectiveness of high-resolution UAVs imagery and of Landsat 8 imagery was evaluated by considering a regression and correlation coefficient analysis. The data from the satellite photography was compared to the UAV imagery using statistical metrics after it had been pre-processed. Ground control points (GCPs) were collected to create a rigorous geo-referenced dataset of UAV imagery that could be compared to the geo-referenced satellite and aerial imagery. The UAV TIR LST showed higher accuracy (R2 0.89, 0.90, root-mean-square error (RMSE) 1.07, 0.70 °C) than the Landsat LST accuracy (R2 0.70, 0.73, (RMSE) 0.78 °C). The relationship between LST and the available soil water content (SWC) was also observed. The results suggested that the UAV-SMC correlation was negative (−0.85) for the image of DOY 230, while this value remains approximately constant (−0.86) for the DOY 245. Our results showed that satellite imagery that was coherent and correlated with UAV images could be useful to assess the general conditions of the field while the UAV favors localized circumscribed areas that the lowest resolution of satellites missed. Accordingly, our results could help with urban area and environmental planning decisions that take into account the thermal environment.

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UAV-based remote sensing in plant stress imagine using high-resolution thermal sensor for digital agriculture practices: a meta-review

Cited by 51 publications

References 110 publications

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Brain-Inspired Remote Sensing Interpretation: A Comprehensive Survey

Multi-sensor spectral synergies for crop stress detection and monitoring in the optical domain: A review

Comparative Evaluation of Land Surface Temperature Images from Unmanned Aerial Vehicle and Satellite Observation for Agricultural Areas Using In Situ Data

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