ArticlePeerJ2019
Combining UAV-based hyperspectral imagery and machine learning algorithms for soil moisture content monitoring.
Article in PeerJ, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Estimation and spatial distribution of soil organic carbon content in farmland using unmanned aerial vehicle hyperspectral remote sensing technology.Scientific reports · 2026Article
- MSRA-Net: A Multi-Task Learning Model for Soil Texture Prediction with Dynamic Weighting and Prior Knowledge Soft Constraints.Sensors (Basel, Switzerland) · 2025Article
- Estimating soil profile salinity under vegetation cover based on UAV multi-source remote sensing.Scientific reports · 2025Article
- Application of Artificial Intelligence and Sensor Fusion for Soil Organic Matter Prediction.Sensors (Basel, Switzerland) · 2024Article
- Article
- Cotton seed cultivar identification based on the fusion of spectral and textural features.PloS one · 2024Article
- Estimation and mapping of soil texture content based on unmanned aerial vehicle hyperspectral imaging.Scientific reports · 2023Article
- Soil Moisture, Organic Carbon, and Nitrogen Content Prediction with Hyperspectral Data Using Regression Models.Sensors (Basel, Switzerland) · 2022Article
- Soybean cyst nematode detection and management: a review.Plant methods · 2022Review
- Possibilities of Using UAVs in Pre-Hospital Security for Medical Emergencies.International journal of environmental research and public health · 2022Review
- Soil Nutrient Estimation and Mapping in Farmland Based on UAV Imaging Spectrometry.Sensors (Basel, Switzerland) · 2021Article
- Machine Learning in Agriculture: A Comprehensive Updated Review.Sensors (Basel, Switzerland) · 2021Review
- Advances in Unmanned Aerial System Remote Sensing for Precision Viticulture.Sensors (Basel, Switzerland) · 2021Review
- Peanut Leaf Wilting Estimation From RGB Color Indices and Logistic Models.Frontiers in plant science · 2021Article
- IoT-IIRS: Internet of Things based intelligent-irrigation recommendation system using machine learning approach for efficient water usage.PeerJ. Computer science · 2021Article
- Active and Passive Electro-Optical Sensors for Health Assessment in Food Crops.Sensors (Basel, Switzerland) · 2020Review
- Modified Red Blue Vegetation Index for Chlorophyll Estimation and Yield Prediction of Maize from Visible Images Captured by UAV.Sensors (Basel, Switzerland) · 2020Article
- A Study of Nitrogen Deficiency Inversion in Rice Leaves Based on the Hyperspectral Reflectance Differential.Frontiers in plant science · 2020Article
- Estimation of soil salt content by combining UAV-borne multispectral sensor and machine learning algorithms.PeerJ · 2020Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Soil moisture content (SMC) is an important factor that affects agricultural development in arid regions. Compared with the space-borne remote sensing system, the unmanned aerial vehicle (UAV) has been widely used because of its stronger controllability and higher resolution. It also provides a more convenient method for monitoring SMC than normal measurement methods that includes field sampling and oven-drying techniques. However, research based on UAV hyperspectral data has not yet formed a standard procedure in arid regions. Therefore, a universal processing scheme is required. We hypothesized that combining pretreatments of UAV hyperspectral imagery under optimal indices and a set of field observations within a machine learning framework will yield a highly accurate estimate of SMC. Optimal 2D spectral indices act as indispensable variables and allow us to characterize a model's SMC performance and spatial distribution. For this purpose, we used hyperspectral imagery and a total of 70 topsoil samples (0-10 cm) from the farmland (2.5 × 10
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.