Using artificial intelligence and data fusion for environmental monitoring: A review and future perspectives

Information Fusion, Journal Year: 2022, Volume and Issue: 86-87, P. 44 - 75

Published: June 25, 2022

Language: Английский

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Salt Stress in Plants and Mitigation Approaches

Plants, Journal Year: 2022, Volume and Issue: 11(6), P. 717 - 717

Published: March 8, 2022

Salinization of soils and freshwater resources by natural processes and/or human activities has become an increasing issue that affects environmental services socioeconomic relations. In addition, salinization jeopardizes agroecosystems, inducing salt stress in most cultivated plants (nutrient deficiency, pH oxidative stress, biomass reduction), directly the quality quantity food production. Depending on type salt/stress (alkaline or pH-neutral), specific approaches solutions should be applied to ameliorate situation on-site. Various agro-hydrotechnical (soil water conservation, reduced tillage, mulching, rainwater harvesting, irrigation drainage, control seawater intrusion), biological (agroforestry, multi-cropping, cultivation salt-resistant species, bacterial inoculation, promotion mycorrhiza, grafting with rootstocks), chemical (application organic mineral amendments, phytohormones), bio-ecological (breeding, desalination, application nano-based products, seed biopriming), institutional (salinity monitoring, integrated national regional strategies) are very effective against salinity/salt numerous other constraints. Advances computer science (artificial intelligence, machine learning) provide rapid predictions from field global scale, under scenarios, including climate change. Thus, these results represent a comprehensive outcome tool for multidisciplinary approach protect salinization, minimizing damages caused stress.

Language: Английский

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Artificial intelligence and digital twins in sustainable agriculture and forestry: a survey

TURKISH JOURNAL OF AGRICULTURE AND FORESTRY, Journal Year: 2022, Volume and Issue: 46(5), P. 642 - 661

Published: Jan. 1, 2022

Affected by global economic pressure and epidemics, sustainable agriculture has received widespread attention from farmers agricultural engineers. Throughout history, technology closely followed the pace of scientific technological development footsteps mechanization, automation, intelligence to progress continuously. At this stage, artificial (AI) is dominating field advancing agriculture. However, large amount data required AI high cost have ensued, while rapid virtualization made people gradually begin consider application digital twins (DT) in This paper examines twin smart recent years discusses analyzes challenges they face future directions development. We find that great potential for success agriculture, which significance solutions achieve low precision meet growing demand high-yield production around world.

Language: Английский

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Challenges and Opportunities in Remote Sensing for Soil Salinization Mapping and Monitoring: A Review

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(10), P. 2540 - 2540

Published: May 12, 2023

Meeting current needs without compromising future generations’ ability to meet theirs is the only path toward achieving environmental sustainability. As most valuable natural resource, soil faces global, regional, and local challenges, from quality degradation mass losses brought on by salinization. These issues affect agricultural productivity ecological balance, undermining sustainability food security. Therefore, timely monitoring accurate mapping of salinization processes are crucial, especially in semi-arid arid regions where climate variability impacts have already reached alarming levels. Salt-affected has enormous potential thanks recent progress remote sensing. This paper comprehensively reviews sensing assess The review demonstrates that large-scale salinity estimation based tools remains a significant challenge, primarily due data resolution acquisition costs. Fundamental trade-offs constrain practical applications between resolution, spatial temporal coverage, costs, high accuracy expectations. article provides an overview research work related using By synthesizing highlighting areas further investigation needed, this helps steer efforts, insight for decision-making resource management, promotes interdisciplinary collaboration.

Language: Английский

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Advancement of Remote Sensing for Soil Measurements and Applications: A Comprehensive Review

Sustainability, Journal Year: 2023, Volume and Issue: 15(21), P. 15444 - 15444

Published: Oct. 30, 2023

Remote sensing (RS) techniques offer advantages over other methods for measuring soil properties, including large-scale coverage, a non-destructive nature, temporal monitoring, multispectral capabilities, and rapid data acquisition. This review highlights the different detection methods, types, parts, applications of RS in measurements, as well disadvantages measurements properties. The choice depends on specific requirements task because it is important to consider limitations each method, context objective determine most suitable technique. paper follows well-structured arrangement after investigating existing literature ensure well-organized, coherent covers all essential aspects related studying advancement using While several remote are available, this suggests spectral reflectance, which entails satellite tools based its global high spatial resolution, long-term monitoring non-invasiveness, cost effectiveness. Conclusively, has improved property various but more research needed calibration, sensor fusion, artificial intelligence, validation, machine learning enhance accuracy applicability.

Language: Английский

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Integrating Active and Passive Remote Sensing Data for Mapping Soil Salinity Using Machine Learning and Feature Selection Approaches in Arid Regions

Remote Sensing, Journal Year: 2023, Volume and Issue: 15(7), P. 1751 - 1751

Published: March 24, 2023

The prevention of soil salinization and managing agricultural irrigation depend greatly on accurately estimating salinity. Although the long-standing laboratory method measuring salinity composition is accurate for determining parameters, its use frequently constrained by high expense difficulty long-term in situ measurement. Soil northern Nile Delta Egypt severely affects agriculture sustainability food security Egypt. Understanding spatial distribution a critical factor development management drylands. This research aims to improve prediction using combined data collection consisting Sentinel-1 C radar Sentinel-2 optical acquired simultaneously via integrated sensor variables. modelling approach focuses feature selection strategies regression learning. Feature approaches that include filter, wrapper, embedded methods were used with 47 selected variables depending genetic algorithm scrutinize whether regions spectrum from indices SAR texture choose optimum combinations sub-setting resulting each train learners’ random forest (RF), linear (LR), backpropagation neural network (BPNN), support vector (SVR). Combining BPNN RF learner better predicted (RME 0.000246; = 18). Integrating different remote sensing machine learning provides an opportunity develop robust predict evaluated performances various models, overcame limitations conventional techniques, optimized variable input combinations. can assist farmers soil-salinization-affected areas planting procedures enhancing their lands.

Language: Английский

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Exploring the capability of Gaofen-5 hyperspectral data for assessing soil salinity risks

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2022, Volume and Issue: 112, P. 102969 - 102969

Published: Aug. 1, 2022

Soil salinization has hampered the achievement of sustainable development goals (SDGs) in many countries worldwide. Several have recently launched hyperspectral remote sensing satellites, opening new avenues for accurate soil-salinity monitoring. Among them, Gaofen-5 (GF-5) from China a high comprehensive performance, including spectral resolution 5 nm, 330 bands, and signal-to-noise ratio 700. However, potential GF-5 estimating soil salinity is not well understood. In this study, we proposed strategy that includes bootstrap methods, fractional order derivative (FOD) techniques decision-level fusion models to exploit diagnostic information reduce estimation uncertainty Ebinur Lake oasis northwestern China. The results showed data were suitable assessing salinity. FOD technique enhanced correlation between spectra, identified more improved accuracy estimation, reduced model uncertainty. low-order outperformed high-order FOD. spectra processed by 0.9 most correlated with (r = −0.76). driven 0.8 produced optimal estimated (R2 0.95, root mean square error (RMSE) 3.20 dS m−1 performance interquartile distance (RPIQ) 5.96). had less than based on original integer-order (first- second- derivatives) spectra. This study provides reference using framework low accuracy. great environmental problems facilitating further SDGs.

Language: Английский

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Computational assessment of groundwater salinity distribution within coastal multi-aquifers of Bangladesh

Scientific Reports, Journal Year: 2022, Volume and Issue: 12(1)

Published: July 1, 2022

The rising salinity trend in the country's coastal groundwater has reached an alarming rate due to unplanned use of agriculture and seawater seeping into underground sea-level rise caused by global warming. Therefore, assessing is crucial for status safe aquifers. In this research, a rigorous hybrid neurocomputing approach comprised Adaptive Neuro-Fuzzy Inference System (ANFIS) hybridized with new meta-heuristic optimization algorithm, namely Aquila (AO) Boruta-Random forest feature selection (FS) was developed estimating multi-aquifers regions Bangladesh. regard, 539 data samples, including ten water quality indices, were collected provide predictive model. Moreover, individual ANFIS, Slime Mould Algorithm (SMA), Ant Colony Optimization Continuous Domains (ACOR) coupled ANFIS (i.e., ANFIS-SMA ANFIS-ACOR) LASSO regression (Lasso-Reg) schemes examined compare primary Several goodness-of-fit such as correlation coefficient (R), root mean squared error (RMSE), Kling-Gupta efficiency (KGE) used validate robustness models. Here, Forest (B-RF), robust tree-based FS, adopted identify most significant candidate inputs effective input combinations reduce computational cost time modeling. outcomes four selected ascertained that ANFIS-OA regarding best accuracy terms (R = 0.9450, RMSE 1.1253 ppm, KGE 0.9146) outperformed 0.9406, 1.1534 0.8793), ANFIS-ACOR 0.9402, 1.1388 0.8653), Lasso-Reg 0.9358), 0.9306) Besides, first combination (C1) three inputs, Cl- (mg/l), Mg2+ Na+ yielded among all alternatives, implying role importance (B-RF) selection. Finally, spatial distribution assessment study area high predictability potential B-RF compared other paradigms. important novelty research using framework non-linear filtering technique neuro-computing approach, which can be considered reliable tool assess

Language: Английский

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Assessment of Soil Salinity Changes under the Climate Change in the Khorezm Region, Uzbekistan

International Journal of Environmental Research and Public Health, Journal Year: 2022, Volume and Issue: 19(14), P. 8794 - 8794

Published: July 20, 2022

Soil salinity negatively affects plant growth and leads to soil degradation. Saline lands result in low agricultural productivity, affecting the well-being of farmers economic situation region. The prediction salinization dynamics plays a crucial role sustainable development regions, preserving ecosystems, improving irrigation management practices. Accurate information through monitoring evaluating changes is essential for strategies agriculture productivity efficient management. As part an ex-ante analysis, we presented comprehensive statistical framework predicting using Homogeneity test linear regression model. was operationalized context Khorezm region Uzbekistan, which suffers from high levels salinity. trends were projected under impact climate change 2021 2050 2051 2100. results show that slightly saline soils would generally decrease (from 55.4% 52.4% by 2100 based on homogeneity test; 55.9% 54.5% according model), but moderately increase 31.2% 32.5% 32.4% model). Moreover, highly 13.4% 15.1% 12.9% 13.1% this study provide understanding depends help government better plan future

Language: Английский

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Current Status and Development Trend of Soil Salinity Monitoring Research in China

Sustainability, Journal Year: 2023, Volume and Issue: 15(7), P. 5874 - 5874

Published: March 28, 2023

Soil salinization is a resource and ecological problem that currently exists on large scale in all countries of the world. This seriously restricting development agricultural production, sustainable use land resources, stability environment. Salinized soils China are characterized by extensive area, complex saline species, prominent problems. Therefore, strengthening management utilization salinized soils, monitoring identifying accurate information, mastering degree regional important goals researchers have been trying to explore overcome. Based amount soil research, this paper reviews developmental history research China, discusses progress monitoring, summarizes main modeling methods for remote sensing soils. Additionally, also proposes analyzes limitations China’s salinity its future trend, taking into account real needs frontier hotspots country related research. great practical significance comprehensively grasp current situation further clarify sort out ideas enrich solve problems China.

Language: Английский

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