Recent Advances of Hyperspectral Imaging Technology and Applications in Agriculture
TLDR
This review is intended to assist agricultural researchers and practitioners to better understand the strengths and limitations of hyperspectral imaging to agricultural applications and promote the adoption of this valuable technology.Abstract:
Remote sensing is a useful tool for monitoring spatio-temporal variations of crop morphological and physiological status and supporting practices in precision farming. In comparison with multispectral imaging, hyperspectral imaging is a more advanced technique that is capable of acquiring a detailed spectral response of target features. Due to limited accessibility outside of the scientific community, hyperspectral images have not been widely used in precision agriculture. In recent years, different mini-sized and low-cost airborne hyperspectral sensors (e.g., Headwall Micro-Hyperspec, Cubert UHD 185-Firefly) have been developed, and advanced spaceborne hyperspectral sensors have also been or will be launched (e.g., PRISMA, DESIS, EnMAP, HyspIRI). Hyperspectral imaging is becoming more widely available to agricultural applications. Meanwhile, the acquisition, processing, and analysis of hyperspectral imagery still remain a challenging research topic (e.g., large data volume, high data dimensionality, and complex information analysis). It is hence beneficial to conduct a thorough and in-depth review of the hyperspectral imaging technology (e.g., different platforms and sensors), methods available for processing and analyzing hyperspectral information, and recent advances of hyperspectral imaging in agricultural applications. Publications over the past 30 years in hyperspectral imaging technology and applications in agriculture were thus reviewed. The imaging platforms and sensors, together with analytic methods used in the literature, were discussed. Performances of hyperspectral imaging for different applications (e.g., crop biophysical and biochemical properties’ mapping, soil characteristics, and crop classification) were also evaluated. This review is intended to assist agricultural researchers and practitioners to better understand the strengths and limitations of hyperspectral imaging to agricultural applications and promote the adoption of this valuable technology. Recommendations for future hyperspectral imaging research for precision agriculture are also presented.read more
Citations
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Journal ArticleDOI
Deep Learning-Based Change Detection in Remote Sensing Images: A Review
TL;DR: This review focuses on deep learning techniques,such as supervised, unsupervised, and semi-supervised for different change detection datasets, such as SAR, multispectral, hyperspectrals, VHR, and heterogeneous images, and their advantages and disadvantages will be highlighted.
Journal ArticleDOI
Forecasting Plant and Crop Disease: An Explorative Study on Current Algorithms
TL;DR: In this paper, the authors present an analysis and classification of research studies conducted over the past decade that forecast the onset of disease at a pre-symptomatic stage (i.e., symptoms not visible to the naked eye) or at an early stage.
Journal ArticleDOI
Global open data remote sensing satellite missions for land monitoring and conservation: A review
TL;DR: A review of the most important global open data remote sensing satellite missions, current state-of-the-art processing methods and applications in land monitoring and conservation studies, and possibilities of their application in land cover, land suitability, vegetation monitoring, and natural disaster management.
Journal ArticleDOI
Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0
TL;DR: A general overview of RS technology is drawn with a special focus on the principal platforms of this technology, i.e., satellites and remotely piloted aircrafts (RPAs), and the sensors used, in relation to the 5th industrial revolution.
Journal ArticleDOI
AgriFusion: An Architecture for IoT and Emerging Technologies Based on a Precision Agriculture Survey
TL;DR: In this article, a multidisciplinary architecture, AgriFusion, has been proposed for precision agriculture (PA), which is a management strategy that utilizes communication and information technology for farm management.
References
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Random Forests
TL;DR: Internal estimates monitor error, strength, and correlation and these are used to show the response to increasing the number of features used in the forest, and are also applicable to regression.
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