The Recent Applications of Remote sensing in Agriculture-A Review

Published: March 2, 2025

Remote sensing is becoming a crucial technology in current agricultural practices, with several uses and benefits for farmers, researchers policymakers. Crop monitoring management are the principal applications of remote agriculture. allows rapid precise diagnosis crop health, growth yield estimation by evaluating data received from satellites or airborne platforms. This assists farmers optimising irrigation, fertilization, pest disease control measures, resulting better resource allocation, enhanced productivity lower environmental consequences. The identification mapping diseases pests key application. may detect minute differences plant physiology, such as chlorophyll content changes, which signal presence infestations. Initial focused treatments precision pesticide application, avoidance loss reduction. Precision agriculture relies heavily on sensing. Farmers produce field maps that delineate soil qualities, nutrient levels, moisture integrating satellite photography, GPS navigation systems computer algorithms. enables site-specific management, allowing to deploy resources precisely where they required, inputs, lowering costs minimising makes land-use planning easier. It can assist identifying potential sites, assessing land degradation tracking changes cover use trends over time. Policymakers this make informed decisions about sustainable practices conservation activities. helps water management. feasible monitor availability, assess irrigation demands identify locations vulnerable drought stress studying data. information more efficient distribution, reducing waste improving water-use efficiency has numerous agriculture, revolutionizing old farming practices. Keywords: Artificial intelligence, sensing, satellites, spectral reflectance, sustainability

Language: Английский

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The Recent Applications of Remote sensing in Agriculture-A Review

Published: March 2, 2025

Remote sensing is becoming a crucial technology in current agricultural practices, with several uses and benefits for farmers, researchers, policymakers. Crop monitoring management are the principal applications of remote agriculture. allows rapid precise diagnosis crop health, growth yield estimation by evaluating data received from satellites or airborne platforms. This assists farmers optimising irrigation, fertilization, pest disease control measures, resulting better resource allocation, enhanced productivity lower environmental consequences. The identification mapping diseases pests key application. may detect minute differences plant physiology, such as chlorophyll content changes, which signal presence infestations. Initial focused treatments precision pesticide application, avoidance loss reduction. Precision agriculture relies heavily on sensing. Farmers produce field maps that delineate soil qualities, nutrient levels, moisture integrating satellite photography, GPS navigation systems computer algorithms. enables site-specific management, allowing to deploy resources precisely where they required, inputs, lowering costs minimising makes land-use planning easier. It can assist identifying potential sites, assessing land degradation tracking changes cover use trends over time. Policymakers this make informed decisions about sustainable practices conservation activities. helps water management. feasible monitor availability, assess irrigation demands identify locations vulnerable drought stress studying data. information more efficient distribution, reducing waste improving water-use efficiency has numerous agriculture, revolutionizing old farming practices. Keywords: Artificial intelligence, sensing, Satellites, Spectral reflectance, Sustainability

Language: Английский

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EFFECTIVENESS OF MACHINE LEARNING METHODS IN DETERMINING EARTHQUAKE PROBABLE AREAS: EXAMPLE OF KAZAKHSTAN

Scientific Journal of Astana IT University, Journal Year: 2025, Volume and Issue: unknown

Published: March 30, 2025

This study investigates the effectiveness of machine learning methods in identifying earthquake-prone areas Kazakhstan and its neighboring regions. By leveraging a comprehensive dataset encompassing significant earthquake data from 1900 to 2023, various algorithms were employed, including RandomForest, GradientBoosting, Logistic Regression, Support Vector Classification (SVC), K-Nearest Neighbors (KNeighbors), Decision Tree, XGBoost, LightGBM, AdaBoost, MLPClassifier. The primary objective was analyze compare performance these models predicting magnitudes frequencies. results reveal that certain significantly outperformed others terms accuracy, underscoring potential techniques enhance prediction capabilities. Notably, XGBoost RandomForest demonstrated highest predictive suggesting their suitability for application seismic risk assessment. These findings offer valuable insights governmental agencies engaged disaster management prevention planning, highlighting practical implications integrating advanced analytical strategies. In addition model analysis, visual heatmap generated illustrate geographical distribution occurrences across studied representation effectively identifies high-risk areas, serving as crucial tool local authorities researchers making informed decisions regarding safety measures emergency preparedness. research contributes expanding body knowledge on utilizing learning, emphasizing necessity continuous improvement by incorporating additional environmental geological factors. extend beyond academic discourse, holding enhancing public regions vulnerable activity. As such, this advocates integration methodologies frameworks mitigate risks preparedness

Language: Английский

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An EWS-LSTM-Based Deep Learning Early Warning System for Industrial Machine Fault Prediction

Applied Sciences, Journal Year: 2025, Volume and Issue: 15(7), P. 4013 - 4013

Published: April 5, 2025

Early warning systems (EWSs) are crucial for optimising predictive maintenance strategies, especially in the industrial sector, where machine failures often cause significant downtime and economic losses. This research details creation evaluation of an EWS that incorporates deep learning methods, particularly using Long Short-Term Memory (LSTM) networks enhanced with attention layers to predict critical faults. The proposed system is designed process time-series data collected from printing machine’s embosser component, identifying error patterns could lead operational disruptions. dataset was preprocessed through feature selection, normalisation, transformation. A multi-model classification strategy adopted, each LSTM-based model trained detect a specific class frequent errors. Experimental results show can failure events up 10 time units advance, best-performing achieving AUROC 0.93 recall above 90%. Results indicate approach successfully predicts events, demonstrating potential EWSs powered by enhancing strategies. By integrating artificial intelligence real-time monitoring, this study highlights how intelligent improve efficiency, reduce unplanned downtime, optimise operations.

Language: Английский

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Innovative Approaches for Earthquake Estimation Using AI and ML Techniques

Advances in computational intelligence and robotics book series, Journal Year: 2025, Volume and Issue: unknown, P. 203 - 226

Published: Feb. 28, 2025

Predictive techniques are increasingly crucial in earthquakes for early warning and risk mitigation due to the increasing frequency impact of these natural disasters. Artificial Intelligence (AI) Machine Learning (ML) transforming earthquake estimation as they help analyze vast amounts data, identify patterns, improve accuracy prediction. This chapter deals with new AI ML approaches estimating seismic activity that include deep learning models, neural networks, support vector machines. These techniques, contrast, make more precise predictions terms magnitude, location, intensity using real-time data from sensors, geological surveys, historical records earthquakes. AI-driven models provide faster computation adaptability insights may not be possible other methods. Data scarcity model validation uncertainty challenges also discussed along future directions AI-enhanced forecasting.

Language: Английский

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