A low-cost approach for soil moisture prediction using multi-sensor data and machine learning algorithm

The Science of The Total Environment, Journal Year: 2022, Volume and Issue: 833, P. 155066 - 155066

Published: April 7, 2022

Language: Английский

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Artificial intelligence-based decision support systems in smart agriculture: Bibliometric analysis for operational insights and future directions

Frontiers in Sustainable Food Systems, Journal Year: 2023, Volume and Issue: 6

Published: Jan. 9, 2023

As the world population is expected to touch 9.73 billion by 2050, according Food and Agriculture Organization (FAO), demand for agricultural needs increasing proportionately. Smart replacing conventional farming systems, employing advanced technologies such as Internet of Things (IoT), Artificial Intelligence (AI), Machine Learning (ML) ensure higher productivity precise agriculture management overcome food demand. In recent years, there has been an increased interest in researchers within Agriculture. Previous literature reviews have also conducted similar bibliometric analyses; however, a lack research Operations Research (OR) insights into This paper conducts Bibliometric Analysis past work OR knowledge which done over last two decades 4.0, understand trends gaps. Biblioshiny, data mining tool, was used conducting analysis on total number 1,305 articles collected from Scopus database between years 2000–2022. Researchers decision makers will be able visualize how newer theories are being applied they can contribute toward some gaps highlighted this review paper. While governments policymakers benefit through understanding Unmanned Aerial Vehicles (UAV) robotic units farms optimize resource allocation. Nations that arid climate conditions would informed satellite imagery mapping assist them detecting irrigation lands their scarce resources.

Language: Английский

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Assessing Machine Learning-Based Prediction under Different Agricultural Practices for Digital Mapping of Soil Organic Carbon and Available Phosphorus

Agriculture, Journal Year: 2022, Volume and Issue: 12(7), P. 1062 - 1062

Published: July 20, 2022

Predicting soil chemical properties such as organic carbon (SOC) and available phosphorus (Ava-P) content is critical in areas where different land uses exist. The distribution of SOC Ava-P influenced by both natural anthropogenic factors. This study aimed at (1) predicting a piedmont plain Northeast Iran using the Random Forests (RF) Cubist mathematical models hybrid (Regression Kriging), (2) comparing models’ results, (3) identifying key variables that influence spatial dynamics under agricultural practices. machine learning were trained with 201 composite surface samples 24 ancillary data, including climate (C), organism (O), topography- relief (R), parent material (P) features (S) according to SCORPAN digital mapping framework, which can predictively represent formation factors spatially. Clay, one most well-known relationship SOC, was important predictor followed open-access multispectral satellite images-based vegetation indices. had similar set effective variables. Hybrid approaches did not improve model accuracy significantly, but they reduce map uncertainty. In validation set, calculated RF algorithm normalized root mean square (NRMSE) 96.8, while an NRMSE 94.2. These values change when technique for Ava-P; however, changed just 1% SOC. management supply activities be guided maps. Produced maps scientist plays active role used identify concentrations are high need protected, uncertainty sampling required further monitoring.

Language: Английский

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Application of deep learning models to detect coastlines and shorelines

Journal of Environmental Management, Journal Year: 2022, Volume and Issue: 320, P. 115732 - 115732

Published: Aug. 2, 2022

Language: Английский

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Advances in Earth observation and machine learning for quantifying blue carbon

Earth-Science Reviews, Journal Year: 2023, Volume and Issue: 243, P. 104501 - 104501

Published: July 13, 2023

Blue carbon ecosystems (mangroves, seagrasses and saltmarshes) are highly productive coastal habitats, considered some of the most carbon-dense on earth. They an important nature-based solution for both climate change mitigation adaptation. Quantifying blue stocks assessing their dynamics at large scales through remote sensing remains challenging due to difficulties cloud coverage, spectral, spatial temporal limitations multispectral sensors speckle noise synthetic aperture radar (SAR). Recent advances in airborne space-borne SAR imagery Light Detection Ranging (LiDAR) data, sensor platforms such as unmanned aerial vehicles (UAVs), combined with novel machine learning techniques have offered different users a wide-range spatial, multi-temporal information quantifying from space. However, number challenges posed by various traits atmospheric correction, water penetration, column transparency issues environments, multi-dimensionality size LiDAR limitation training samples, backscattering mechanisms acquisition process. As result, existing methodologies face major accurately estimating using these datasets. In this context, emerging innovative artificial intelligence often required robustness reliability estimates, particularly those open-source software signal processing regression tasks. This review provides overview Earth Observation state-of-the-art deep that currently being used quantify above-ground carbon, below-ground soil mangroves, saltmarshes ecosystems. Some key future directions potential use data fusion advanced learning, metaheuristic optimisation also highlighted. summary, quantification approaches holds great contributing global efforts towards mitigating protecting

Language: Английский

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Satellite-based soil organic carbon mapping on European soils using available datasets and support sampling

Science of Remote Sensing, Journal Year: 2024, Volume and Issue: 9, P. 100118 - 100118

Published: Jan. 28, 2024

Soil organic carbon (SOC) plays a major role in the global cycle and is an important factor for soil health fertility. Accurate mapping of SOC other influencing parameters are crucial to guide optimization agricultural land management maintain restore health, increase fertility, thus quantify its potential sequestering CO2. Remote sensing machine learning techniques offer promising approaches predicting distribution. In this study, we used remote data algorithms map at regional large scale, which then combined with temporospatial spectral signature-based sampling integrate local ground measurements. A rigorous validation approach was performed where several independent unseen datasets high number samples were used, additionally involved densely sampled fields. We found that our could predict average percentage error less than 10 % R2 0.91 using support on croplands located mineral soils, demonstrating sensing, learning, specific measurements SOC. Our results suggest make small differences measurable inform sequestration efforts improve understanding impacts use field practices cycling.

Language: Английский

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A novel framework to assess apple leaf nitrogen content: Fusion of hyperspectral reflectance and phenology information through deep learning

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 219, P. 108816 - 108816

Published: March 15, 2024

Language: Английский

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Spatial and temporal evolution of soil organic matter and its response to dynamic factors in the Southern part of Black Soil Region of Northeast China

Soil and Tillage Research, Journal Year: 2025, Volume and Issue: 248, P. 106475 - 106475

Published: Feb. 3, 2025

Language: Английский

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High-Accuracy Mapping of Soil Organic Carbon by Mining Sentinel-1/2 Radar and Optical Time-Series Data with Super Ensemble Model

Remote Sensing, Journal Year: 2025, Volume and Issue: 17(4), P. 678 - 678

Published: Feb. 17, 2025

Accurate digital soil organic carbon mapping is of great significance for regulating the global cycle and addressing climate change. With advent remote sensing big data era, multi-source multi-temporal techniques have been extensively applied in Earth observation. However, how to fully mine time-series high-accuracy SOC remains a key challenge. To address this challenge, study introduced new idea mining data. We used 413 topsoil samples from southern Xinjiang, China, as an example. By (Sentinel-1/2) 2017 2023, we revealed temporal variation pattern correlation between Sentinel-1/2 SOC, thereby identifying optimal time window monitoring using integrating environmental covariates super ensemble model, achieved Southern China. The results showed following aspects: (1) windows were July–September July–August, respectively; (2) modeling accuracy sensor integrated with was superior single-source alone. In model based on data, cumulative contribution rate Sentinel-2 51.71% higher than that Sentinel-1 data; (3) stacking model’s predictive performance outperformed weight average simple models. Therefore, covariates, driven represents strategy mapping.

Language: Английский

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Estimation of Soil Organic Carbon Content in the Ebinur Lake Wetland, Xinjiang, China, Based on Multisource Remote Sensing Data and Ensemble Learning Algorithms

Sensors, Journal Year: 2022, Volume and Issue: 22(7), P. 2685 - 2685

Published: March 31, 2022

Soil organic carbon (SOC), as the largest pool on land surface, plays an important role in soil quality, ecological security and global cycle. Multisource remote sensing data-driven modeling strategies are not well understood for accurately mapping carbon. Here, we hypothesized that Sentinel-2 Multispectral Sensor Instrument (MSI) strategy produced superior outcomes compared to based Landsat 8 Operational Land Imager (OLI) data due finer spatial spectral resolutions of Sentinel-2A MSI data. To test this hypothesis, Ebinur Lake wetland Xinjiang was selected study area. In study, SOC estimation carried out using data, combining climatic variables, topographic factors, index variables Sentinel-1A construct a common variable model full respectively. We utilized ensemble learning algorithms assess prediction performance strategies, including random forest (RF), gradient boosted decision tree (GBDT) extreme boosting (XGBoost) algorithms. The results show that: (1) outperformed contents, under XGBoost algorithm achieved best R2 = 0.804, RMSE 1.771, RPIQ 2.687). (2) with addition red-edge band improved by 6% 3.2% over models, (3) wetland, areas higher content were mainly concentrated oasis, while mountainous lakeside had lower contents. Our provide program monitor sustainability terrestrial ecosystems through satellite perspective.

Language: Английский

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