Machine Learning Approach for Predicting Environmental Impact: A Neuro-Fuzzy Model for Life Cycle Impact Assessment of Strawberry Production
Artificial Intelligence (AI) is transforming traditional methods reliant on human knowledge by introducing machine learning techniques, which offer effective solutions to complex challenges. An example of such a case is the evaluation of the environmental impacts of products throughout their life cy...
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| Format: | Article |
| Language: | English |
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Sciendo
2025-01-01
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| Series: | Environmental and Climate Technologies |
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| Online Access: | https://doi.org/10.2478/rtuect-2025-0017 |
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| author | Feofilovs Maksims Zaeemi Majid Cappelli Andrea Romagnoli Francesco |
| author_facet | Feofilovs Maksims Zaeemi Majid Cappelli Andrea Romagnoli Francesco |
| author_sort | Feofilovs Maksims |
| collection | DOAJ |
| description | Artificial Intelligence (AI) is transforming traditional methods reliant on human knowledge by introducing machine learning techniques, which offer effective solutions to complex challenges. An example of such a case is the evaluation of the environmental impacts of products throughout their life cycle. This study bridges the gap in life cycle assessment (LCA) by leveraging AI to predict environmental impacts in agriculture, specifically by using LCA data from one cultivation system to model another. We employed Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to predict CO2 equivalent emissions for open-field strawberry production, utilizing greenhouse strawberry data. The novelty lies in combining machine learning with LCA to address data scarcity and improve predictive accuracy in agricultural impact assessments. The model was trained with data generated in MATLAB and validated against emissions computed using the Ecoinvent 3.10 database and SimaPro software. Among the three fuzzy inference system (FIS) generation approaches - Fuzzy C-Means (FCM), Subtractive Clustering (SC), and Grid Partitioning (GP) FCM exhibited the highest the accuracy. This methodology showcases AI’s potential to transform LCA, enabling more efficient, data-driven sustainability assessments. |
| format | Article |
| id | doaj-art-40d6fec57a12486f951490fb0a177602 |
| institution | Kabale University |
| issn | 2255-8837 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Environmental and Climate Technologies |
| spelling | doaj-art-40d6fec57a12486f951490fb0a1776022025-08-25T06:12:10ZengSciendoEnvironmental and Climate Technologies2255-88372025-01-0129124325810.2478/rtuect-2025-0017Machine Learning Approach for Predicting Environmental Impact: A Neuro-Fuzzy Model for Life Cycle Impact Assessment of Strawberry ProductionFeofilovs Maksims0Zaeemi Majid1Cappelli Andrea2Romagnoli Francesco3Institute of Energy Systems and Environment, Riga Technical University, Azenes iela 12/1, Riga, LV-1048, Latvia2Bioeconomy in Transition Research Group, IDEA, Unitelma Sapienza - University of Rome, Viale Regina Elena, 295, 00161Rome, Italy3Department of Chemical Engineering Materials Environment (DICMA), Faculty of Civil and Industrial Engineering, Sapienza University of Rome, Via Eudossiana 18, Rome, 00184, ItalyInstitute of Energy Systems and Environment, Riga Technical University, Azenes iela 12/1, Riga, LV-1048, LatviaArtificial Intelligence (AI) is transforming traditional methods reliant on human knowledge by introducing machine learning techniques, which offer effective solutions to complex challenges. An example of such a case is the evaluation of the environmental impacts of products throughout their life cycle. This study bridges the gap in life cycle assessment (LCA) by leveraging AI to predict environmental impacts in agriculture, specifically by using LCA data from one cultivation system to model another. We employed Adaptive Neuro-Fuzzy Inference Systems (ANFIS) to predict CO2 equivalent emissions for open-field strawberry production, utilizing greenhouse strawberry data. The novelty lies in combining machine learning with LCA to address data scarcity and improve predictive accuracy in agricultural impact assessments. The model was trained with data generated in MATLAB and validated against emissions computed using the Ecoinvent 3.10 database and SimaPro software. Among the three fuzzy inference system (FIS) generation approaches - Fuzzy C-Means (FCM), Subtractive Clustering (SC), and Grid Partitioning (GP) FCM exhibited the highest the accuracy. This methodology showcases AI’s potential to transform LCA, enabling more efficient, data-driven sustainability assessments.https://doi.org/10.2478/rtuect-2025-0017anfisartificial intelligencecarbon footprintglobal warming potentialsustainability |
| spellingShingle | Feofilovs Maksims Zaeemi Majid Cappelli Andrea Romagnoli Francesco Machine Learning Approach for Predicting Environmental Impact: A Neuro-Fuzzy Model for Life Cycle Impact Assessment of Strawberry Production Environmental and Climate Technologies anfis artificial intelligence carbon footprint global warming potential sustainability |
| title | Machine Learning Approach for Predicting Environmental Impact: A Neuro-Fuzzy Model for Life Cycle Impact Assessment of Strawberry Production |
| title_full | Machine Learning Approach for Predicting Environmental Impact: A Neuro-Fuzzy Model for Life Cycle Impact Assessment of Strawberry Production |
| title_fullStr | Machine Learning Approach for Predicting Environmental Impact: A Neuro-Fuzzy Model for Life Cycle Impact Assessment of Strawberry Production |
| title_full_unstemmed | Machine Learning Approach for Predicting Environmental Impact: A Neuro-Fuzzy Model for Life Cycle Impact Assessment of Strawberry Production |
| title_short | Machine Learning Approach for Predicting Environmental Impact: A Neuro-Fuzzy Model for Life Cycle Impact Assessment of Strawberry Production |
| title_sort | machine learning approach for predicting environmental impact a neuro fuzzy model for life cycle impact assessment of strawberry production |
| topic | anfis artificial intelligence carbon footprint global warming potential sustainability |
| url | https://doi.org/10.2478/rtuect-2025-0017 |
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