Spatial Prediction of Soil Continuous and Categorical Properties Using Deep Learning Approaches for Tamil Nadu, India DOI Creative Commons

Thamizh Vendan Tarun Kshatriya,

R. Kumaraperumal, S. Pazhanivelan

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(11), P. 2707 - 2707

Published: Nov. 17, 2024

Large-scale mapping of soil resources can be crucial and indispensable for several the managerial applications policy implications. With machine learning models being most utilized modeling technique digital (DSM), implementation model-based deep methods spatial predictions is still under scrutiny. In this study, continuous (pH OC) categorical variables (order suborder) were predicted using learning–multi layer perceptron (DL-MLP) one-dimensional convolutional neural networks (1D-CNN) entire state Tamil Nadu, India. For training models, 27,098 profile observations (0–30 cm) extracted from generated database, considering series as distinctive stratum. A total 43 SCORPAN-based environmental covariates considered, which 37 retained after recursive feature elimination (RFE) process. The validation test results obtained each attributes both algorithms comparable with DL-MLP algorithm depicting attributes’ intricate organization details, compared to 1D-CNN model. Irrespective datasets, R2 RMSE values pH attribute ranged 0.15 0.30 0.97 1.15, respectively. Similarly, OC 0.20 0.39 0.38 0.42, Further, overall accuracy (OA) order suborder classification 39% 67% 35% 64%, explicit quantification covariate importance derived permutation implied that tried incorporate respect genesis study. Such approaches integrating soil–environmental relationships limited parameterization computing costs serve a baseline emphasizing opportunities in increasing transferability generalizability model while accounting associated dependencies.

Language: Английский

Stat-of-charge estimation for lithium-ion batteries based on recurrent neural network: Current status and perspectives DOI

Yucheng Zhang,

Xiao Tan, Zhenjun Wang

et al.

Journal of Energy Storage, Journal Year: 2025, Volume and Issue: 112, P. 115575 - 115575

Published: Jan. 30, 2025

Language: Английский

Citations

2
Towards Explainable AI: Interpreting Soil Organic Carbon Prediction Models Using a Learning‐Based Explanation Method DOI Creative Commons
Nafiseh Kakhani, Ruhollah Taghizadeh–Mehrjardi, Davoud Omarzadeh

et al.

European Journal of Soil Science, Journal Year: 2025, Volume and Issue: 76(2)

Published: Feb. 24, 2025

ABSTRACT An understanding of the key factors and processes influencing variability soil organic carbon (SOC) is essential for development effective policies aimed at enhancing storage in soils to mitigate climate change. In recent years, complex computational approaches from field machine learning (ML) have been developed modelling mapping SOC various ecosystems over large areas. However, order understand that account ML models serve as a basis new scientific discoveries, predictions made by these data‐driven must be accurately explained interpreted. this research, we introduce novel explanation approach applicable any model investigate significance environmental features explain across Germany. The methodology employed study involves training multiple using content measurements LUCAS dataset incorporating derived Google Earth Engine (GEE) explanatory variables. Thereafter, an applied elucidate what learned about relationship between supervised manner. our approach, post hoc trained estimate contribution specific inputs outputs models. results indicate different classes rely on interpretable but distinct variability. Decision tree‐based models, such random forest (RF) gradient boosting, highlight importance topographic features. Conversely, chemical information, particularly pH, crucial performance neural networks linear regression Therefore, interpreting studies requires carefully structured guided expert knowledge deep being analysed.

Language: Английский

Citations

1
SSL-SoilNet: A Hybrid Transformer-based Framework with Self-Supervised Learning for Large-scale Soil Organic Carbon Prediction DOI
Nafiseh Kakhani, Moien Rangzan, Ali Jamali

et al.

IEEE Transactions on Geoscience and Remote Sensing, Journal Year: 2024, Volume and Issue: 62, P. 1 - 15

Published: Jan. 1, 2024

Language: Английский

Citations

4
A bibliometric analysis of research on remote sensing-based monitoring of soil organic matter conducted between 2003 and 2023 DOI Creative Commons

X. Chen,

Fei Yuan, Syed Tahir Ata-Ul-Karim

et al.

Artificial Intelligence in Agriculture, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 1, 2025

Language: Английский

Citations

0
Neural Networks for Analyzing Soil Organic Carbon Storage DOI

Ayush Tripathi,

Prashant Upadhyay, Pawan Kumar Goel

et al.

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 455 - 480

Published: April 11, 2025

Soil organic carbon (SOC) is an essential element of the global cycle, serving a central role in climate change mitigation, soil fertility, and ecosystem sustainability. Conventional SOC estimation techniques are time-consuming, labor-intensive, geographically confined, thus confining their efficiency for large-scale monitoring. This chapter discusses how artificial neural networks, such as CNNs, RNNs, deep learning models, improve forecasting accuracy scalability. With integration remote sensing, geospatial data, environmental factors, AI-based models facilitate effective processing mapping distribution. Deep machine methodologies enhance predictive power, automate analysis, mitigate uncertainties estimation. Critical methodologies, issues, emerging trends exploiting networks storage discussed, prioritizing sequestration monitoring optimization, sustainable land management, resilience planning.

Language: Английский

Citations

0
Soil organic carbon estimation using spaceborne hyperspectral composites on a large scale DOI
Xiangyu Zhao, Zhitong Xiong,

Paul Karlshöfer

et al.

International Journal of Applied Earth Observation and Geoinformation, Journal Year: 2025, Volume and Issue: 140, P. 104504 - 104504

Published: May 14, 2025

Language: Английский

Citations

0
Prediction and spatial–temporal changes of soil organic matter in the Huanghuaihai Plain by combining legacy and recent data DOI Creative Commons
Fangfang Zhang, Ya Liu, Shiwen Wu

et al.

Geoderma, Journal Year: 2024, Volume and Issue: 450, P. 117031 - 117031

Published: Sept. 17, 2024

Language: Английский

Citations

1
Spatial Prediction of Soil Continuous and Categorical Properties Using Deep Learning Approaches for Tamil Nadu, India DOI Creative Commons

Thamizh Vendan Tarun Kshatriya,

R. Kumaraperumal, S. Pazhanivelan

et al.

Agronomy, Journal Year: 2024, Volume and Issue: 14(11), P. 2707 - 2707

Published: Nov. 17, 2024

Large-scale mapping of soil resources can be crucial and indispensable for several the managerial applications policy implications. With machine learning models being most utilized modeling technique digital (DSM), implementation model-based deep methods spatial predictions is still under scrutiny. In this study, continuous (pH OC) categorical variables (order suborder) were predicted using learning–multi layer perceptron (DL-MLP) one-dimensional convolutional neural networks (1D-CNN) entire state Tamil Nadu, India. For training models, 27,098 profile observations (0–30 cm) extracted from generated database, considering series as distinctive stratum. A total 43 SCORPAN-based environmental covariates considered, which 37 retained after recursive feature elimination (RFE) process. The validation test results obtained each attributes both algorithms comparable with DL-MLP algorithm depicting attributes’ intricate organization details, compared to 1D-CNN model. Irrespective datasets, R2 RMSE values pH attribute ranged 0.15 0.30 0.97 1.15, respectively. Similarly, OC 0.20 0.39 0.38 0.42, Further, overall accuracy (OA) order suborder classification 39% 67% 35% 64%, explicit quantification covariate importance derived permutation implied that tried incorporate respect genesis study. Such approaches integrating soil–environmental relationships limited parameterization computing costs serve a baseline emphasizing opportunities in increasing transferability generalizability model while accounting associated dependencies.

Language: Английский

Citations

0