Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring

Ge, Xiangyu; Wang, Jingzhe; Ding, Jianli; Cao, Xiaoyi; Zhang, Zipeng; Liu, Jie; Li, Xiaohang

doi:10.7287/peerj.preprints.27630v1

Cited by 5 publications

(3 citation statements)

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“…This research underscores the utility of CNN-based spectral analysis for evaluating cold damage in corn seedlings, facilitating the selection of cold stress-resistant crop varieties. In [225], the authors outline a methodology for precise estimation of soil moisture content (SMC) in arid regions using UAV hyperspectral data. It combines optimal spectral indices and ground observations in a machine learning framework to achieve highly accurate SMC predictions.…”

Section: B Agriculture and Food Quality And Safetymentioning

confidence: 99%

Hyperspectral Imaging Algorithms and Applications: A Review

Akewar,

Chandak

2023

Preprint

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<p>The paper covers topics ranging from hyperspectral imaging applications to innovative algorithms that have enhanced the analysis of hyperspectral data. We delve into the practical applications, including Agriculture, Food Quality and Safety, Earth Sciences, Exploration and Monitoring, Healthcare, Pharmaceuticals, Medical Imaging, Industrial Manufacturing, Management, Conservation, Safety and Security. Our goal is to provide a holistic understanding of how hyperspectral imaging is transforming these fields and driving new possibilities. We also discussed detailed algorithmic development in Hyperspectral Imaging including Supervised and Unsupervised algorithms. We reviewed algorithmic development from the early 1980’s to recent developments including Artificial Intelligence involving algorithms from Machine Learning to Deep Learning. We believe our paper will be of particular interest to your readers for those who wish to study algorithm development from simple image processing procedures to AI based approaches and how these algorithms are used in diverse applications to make intelligent decisions for increasing productivity and efficiency in Agriculture, Exploration and Management and identification of diseases and natural resources. Our paper is well organized into various sections as per algorithms and applications. We reviewed more than 250 papers with proper citations and authors' research work.</p>

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Section: B Agriculture and Food Quality And Safetymentioning

confidence: 99%

Hyperspectral Imaging Algorithms and Applications: A Review

Akewar,

Chandak

2023

Preprint

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“…Due to the high degree of complexity between remote sensor data and soil characteristics, most work in this area has been assisted by machine learning approaches. Some research has utilized only remote sensing data analyzed with machine learning (Ge et al., 2019) to obtain estimates such as soil moisture. Generally, studies have combined remote sensing data with data including terrain models, electrical conductivity maps (Wijewardane et al., 2019), crop heights, crop temperatures (Hu et al., 2019), soil temperatures (Aboutalebi, Allen, Torres‐Rua, McKee, & Coopmans, 2019), and water holding capacities (Hassan‐Esfahani, Torres‐Rua, Jensen, & McKee, 2015) aided by machine learning to produce UAV‐derived soil organic carbon, salinity, and moisture maps, respectively.…”

Section: Uav Applications In Agriculturementioning

confidence: 99%

Review on unmanned aerial vehicles, remote sensors, imagery processing, and their applications in agriculture

Olson

Anderson

2021

Agronomy Journal

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Unmanned aerial vehicles (UAV) and the sensors they can be equipped with are becoming more technologically advanced and common place within the agricultural industry. The output analyses from UAV captured data helps drive decisions for improving input efficiency in agricultural systems, which can result in maximum return on investment and reduced environmental impact. Advances in UAV technologies provides producers with options for assessment of crucial factors impacting crop yield and quality including crop water status and nutrient stress, competition from weeds, insects, and pathogens, and soil characteristics and precision field maps. Thus, integrating UAV technologies as a component of production agriculture enables producers to identify and locate problem areas in their fields in near real time and take corrective actions once these areas are verified. UAV technologies also offers researchers with a non-destructive, objective manner for obtaining phenotypic measurements such as height assessment, biomass estimation, canopy reflectance, and abiotic and biotic stress tolerance, which can greatly expedite field data collection for advancing germplasm with desired agronomic traits. This review covers more than a hundred articles that were obtained from the existing literature and illustrates common UAV types, common sensor types, imagery processing options, and their practical application and pitfalls in the modern agricultural industry.Abbreviations: CWSI, crop water stress index; LiDAR, light detecting and ranging; NDVI, normalized difference vegetation index; RENDVI, red edge normalized difference vegetation index; UAV, unmanned aerial vehicle.This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

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“…Compared with other spectral indices such as the normalized difference index and the ratio index, Ge et al. (2019) found that the two‐dimensional perpendicular index had the highest correlation with SMC. However, these spectral indices used only several bands and possibly neglected some other important spectral information that might decrease the prediction accuracy.…”

Section: Introductionmentioning

confidence: 99%

Predicting soil moisture content over partially vegetation covered surfaces from hyperspectral data with deep learning

Zhang

Liu

et al. 2021

Soil Science Soc of Amer J

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Previous studies for retrieving soil moisture content (SMC) from visible and nearinfrared hyperspectral data over vegetation-covered surfaces using spectral unmixing, non-negative matrix factorization, and albedo/vegetation coverage in trapezoid spaces have required mass spectral preprocessing and offered only limited improvements in prediction accuracy. Recently, deep learning has triggered some improvements in soil properties prediction from hyperspectral data because of its automatic feature extraction and high accuracy. In this study, hyperspectral data in a simulation experiment with different vegetation coverages, SMCs, and soil types were acquired. Deep learning models, one-dimensional convolutional neural network (1D-CNN), and long short-term memory network (LSTM) are proposed to predict SMC. The results showed that two deep learning models achieved excellent predictions (residual prediction deviation [RPD] > 2.5) using the unpreprocessed mixed spectra and partial least squares regression (PLSR) had a good prediction (RPD = 1.88). The 1D-CNN (R 2 p = .91) and LSTM (R 2 p = .90) significantly outperformed PLSR (R 2 p = .72), which demonstrated that deep learning could improve SMC prediction over partially vegetation-covered surfaces. However, when only using bare soil spectra, the prediction accuracy was commensurate, whether through the 1D-CNN, LSTM, or PLSR models; additionally, 1D-CNN and LSTM had better performance on all mixed spectra than bare soil spectra. These results indicated that deep learning had no advantage on smaller datasets. We also found that SMC prediction with deep learning was affected by vegetation coverage and soil type but was still very good. The 1D-CNN and LSTM are effective models for predicting SMC with large hyperspectral datasets acquired from complex soil surface conditions.

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Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring

Abstract: X. 2019. Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring. PeerJ 7:e6926 https://doi.

Cited by 5 publications

References 0 publications

Hyperspectral Imaging Algorithms and Applications: A Review

Hyperspectral Imaging Algorithms and Applications: A Review

Review on unmanned aerial vehicles, remote sensors, imagery processing, and their applications in agriculture

Predicting soil moisture content over partially vegetation covered surfaces from hyperspectral data with deep learning

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