Assessing Soil Organic Carbon in Semi-Arid Agricultural Soils Using UAVs and Machine Learning: A Pathway to Sustainable Water and Soil Resource Management DOI Open Access

Imad El-Jamaoui,

María José Delgado-Iniesta, María José Martínez‐Sánchez

и другие.

Sustainability, Год журнала: 2025, Номер 17(8), С. 3440 - 3440

Опубликована: Апрель 12, 2025

The global effort to combat climate change highlights the critical role of storing organic carbon in soil reduce greenhouse gas emissions. Traditional methods mapping (SOC) have been labour-intensive and costly, relying on extensive laboratory analyses. Recent advancements unmanned aerial vehicles (UAVs) offer a promising alternative for efficiently affordably SOC at field level. This study focused developing method accurately predict topsoil high resolution using spectral data from low-altitude UAV multispectral imagery, complemented by Nogalte farm Murcia, Spain, as part LIFE AMDRYC4 project. To attain this objective, Python version 3.10 was used implement several machine learning techniques, including partial least squares (PLS) regression, random forest (RF), support vector (SVM). Among these, algorithm demonstrated superior performance, achieving an R2 value 0.92, RMSE 0.22, MAE 0.19, MSE 0.05, EVE 0.71 estimating SOC. results RF model were then visualised spatially GIS compared with simple spatial interpolations findings suggest that sensor UAV-based modelling can provide valuable insights farmers, offering practical means monitor levels enhance precision agriculture systems. innovative approach reduces time cost associated traditional supports sustainable agricultural practices enabling more precise management resources.

Язык: Английский

A critical systematic review on spectral-based soil nutrient prediction using machine learning DOI
Shagun Jain, Divyashikha Sethia, K. C. Tiwari

и другие.

Environmental Monitoring and Assessment, Год журнала: 2024, Номер 196(8)

Опубликована: Июль 4, 2024

Язык: Английский

Процитировано

6
Digital mapping of soil organic carbon in a plain area based on time-series features DOI Creative Commons
Kun Yan, Decai Wang,

Yongkang Feng

и другие.

Ecological Indicators, Год журнала: 2025, Номер 171, С. 113215 - 113215

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

0
AI Algorithms in the Agrifood Industry: Application Potential in the Spanish Agrifood Context DOI Creative Commons
Javier Marcos Arévalo, Francisco Javier Flor Montalvo, Juan-Ignacio Latorre-Biel

и другие.

Applied Sciences, Год журнала: 2025, Номер 15(4), С. 2096 - 2096

Опубликована: Фев. 17, 2025

This research explores the prospective implementations of artificial intelligence (AI) algorithms within agrifood sector, focusing on Spanish context. AI methodologies, encompassing machine learning, deep and neural networks, are increasingly integrated into various sectors, including precision farming, crop yield forecasting, disease diagnosis, resource management. Utilizing a comprehensive bibliometric analysis scientific literature from 2020 to 2024, this outlines increasing incorporation in Spain identifies prevailing trends obstacles associated with it industry. The findings underscore extensive application remote sensing, water management, environmental sustainability. These areas particularly pertinent Spain’s diverse agricultural landscapes. Additionally, study conducts comparative between global outputs, highlighting its distinctive contributions unique challenges encountered sector. Despite considerable opportunities presented by these technologies, key limitations, need for enhanced digital infrastructure, improved data integration, increased accessibility smaller enterprises. paper also future pathways aimed at facilitating integration agriculture. It addresses cost-effective solutions, data-sharing frameworks, ethical societal implications inherent deployment.

Язык: Английский

Процитировано

0
Assessing Soil Organic Carbon in Semi-Arid Agricultural Soils Using UAVs and Machine Learning: A Pathway to Sustainable Water and Soil Resource Management DOI Open Access

Imad El-Jamaoui,

María José Delgado-Iniesta, María José Martínez‐Sánchez

и другие.

Sustainability, Год журнала: 2025, Номер 17(8), С. 3440 - 3440

Опубликована: Апрель 12, 2025

The global effort to combat climate change highlights the critical role of storing organic carbon in soil reduce greenhouse gas emissions. Traditional methods mapping (SOC) have been labour-intensive and costly, relying on extensive laboratory analyses. Recent advancements unmanned aerial vehicles (UAVs) offer a promising alternative for efficiently affordably SOC at field level. This study focused developing method accurately predict topsoil high resolution using spectral data from low-altitude UAV multispectral imagery, complemented by Nogalte farm Murcia, Spain, as part LIFE AMDRYC4 project. To attain this objective, Python version 3.10 was used implement several machine learning techniques, including partial least squares (PLS) regression, random forest (RF), support vector (SVM). Among these, algorithm demonstrated superior performance, achieving an R2 value 0.92, RMSE 0.22, MAE 0.19, MSE 0.05, EVE 0.71 estimating SOC. results RF model were then visualised spatially GIS compared with simple spatial interpolations findings suggest that sensor UAV-based modelling can provide valuable insights farmers, offering practical means monitor levels enhance precision agriculture systems. innovative approach reduces time cost associated traditional supports sustainable agricultural practices enabling more precise management resources.

Язык: Английский

Процитировано

0