View in EDS

Integrating machine learning for precision agriculture waste estimation and sustainability enhancement.

Saved in:
Bibliographic Details
Title: Integrating machine learning for precision agriculture waste estimation and sustainability enhancement.
Authors: Lakhouit, Abderrahim1 (AUTHOR) a.lakhouit@ut.edu.sa, AL Rashed, Wael S.1 (AUTHOR), Abbas, Sumaya Y.H.2 (AUTHOR), Shaban, Mahmoud3 (AUTHOR)
Source: Computers & Electronics in Agriculture. Mar2025, Vol. 230, pN.PAG-N.PAG. 1p.
Subjects: Agricultural wastes, Waste management, Crop residues, Machine learning, Environmental management
Abstract: • Machine learning techniques projected agricultural waste output by evaluating farming and waste practices. • Machine learning predictions helped policymakers craft tailored environmental protection strategies. • Data gathering in these countries improved waste estimation, highlighting technology's role in environmental management. • This research offers a blueprint for sustainable waste handling to protect regional ecosystems and resources. [Display omitted] Agricultural waste management and its environmental impacts are a growing concern globally. Between 2010 and 2020, Bahrain, Kuwait, and the United Arab Emirates (UAE) experienced substantial agricultural growth, leading to an increase in waste generation from crop residue, manure, and by-products from processing facilities. The objective of this study is to utilize machine-learning (ML) algorithms to accurately estimate agricultural waste volumes in these three Middle Eastern countries. By considering factors such as crop type, cultivated area, livestock population, and waste management practices, the researchers aim to predict waste generation and provide valuable insights for waste management. Data on agricultural waste production for the period 2010–2020 were collected and then fed into ML algorithms to estimate waste volumes. The algorithms were trained to analyze the data and make precise predictions according to specified factors. Based on the data, the algorithms accurately estimated waste volumes, demonstrating their effectiveness in predicting waste generation. These findings offer guidance to policymakers and waste management authorities for efficiently allocating resources and developing effective mitigation strategies in the Middle East and elsewhere. [ABSTRACT FROM AUTHOR]
Copyright of Computers & Electronics in Agriculture is the property of Elsevier B.V. and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
Description
Abstract:• Machine learning techniques projected agricultural waste output by evaluating farming and waste practices. • Machine learning predictions helped policymakers craft tailored environmental protection strategies. • Data gathering in these countries improved waste estimation, highlighting technology's role in environmental management. • This research offers a blueprint for sustainable waste handling to protect regional ecosystems and resources. [Display omitted] Agricultural waste management and its environmental impacts are a growing concern globally. Between 2010 and 2020, Bahrain, Kuwait, and the United Arab Emirates (UAE) experienced substantial agricultural growth, leading to an increase in waste generation from crop residue, manure, and by-products from processing facilities. The objective of this study is to utilize machine-learning (ML) algorithms to accurately estimate agricultural waste volumes in these three Middle Eastern countries. By considering factors such as crop type, cultivated area, livestock population, and waste management practices, the researchers aim to predict waste generation and provide valuable insights for waste management. Data on agricultural waste production for the period 2010–2020 were collected and then fed into ML algorithms to estimate waste volumes. The algorithms were trained to analyze the data and make precise predictions according to specified factors. Based on the data, the algorithms accurately estimated waste volumes, demonstrating their effectiveness in predicting waste generation. These findings offer guidance to policymakers and waste management authorities for efficiently allocating resources and developing effective mitigation strategies in the Middle East and elsewhere. [ABSTRACT FROM AUTHOR]
ISSN:01681699
DOI:10.1016/j.compag.2025.109933