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\n  \n 2023\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n GHG Global Emission Prediction of Synthetic N Fertilizers Using Expectile Regression Techniques.\n \n \n \n \n\n\n \n Benghzial, K.; Raki, H.; Bamansour, S.; Elhamdi, M.; Aalaila, Y.; and Peluffo-Ordóñez, D.\n\n\n \n\n\n\n Atmosphere, 14(2). 2023.\n \n\n\n\n
\n\n\n\n \n \n \"GHGWebsite\n  \n \n\n \n \n doi\n  \n \n\n \n link\n  \n \n\n bibtex\n \n\n \n  \n \n abstract \n \n\n \n\n \n \n \n \n \n \n \n\n  \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n\n\n\n
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@article{\n title = {GHG Global Emission Prediction of Synthetic N Fertilizers Using Expectile Regression Techniques},\n type = {article},\n year = {2023},\n keywords = {air quality,bio-meteorology,expectile regression,greenhouse gas emissions,nitrogen-based fertilizers,nitrous oxide,supervised machine learning},\n volume = {14},\n websites = {https://www.mdpi.com/2073-4433/14/2/283*},\n id = {3662561c-eac2-38ab-9065-d5b6848f2493},\n created = {2023-03-14T21:54:18.604Z},\n accessed = {2023-05-23},\n file_attached = {false},\n profile_id = {be6263d9-e5f6-3436-8031-6661eb6f0f4d},\n last_modified = {2023-05-23T15:37:37.939Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n private_publication = {true},\n abstract = {Agriculture accounts for a large percentage of nitrous oxide ((Formula presented.)) emissions, mainly due to the misapplication of nitrogen-based fertilizers, leading to an increase in the greenhouse gas (GHG) footprint. These emissions are of a direct nature, released straight into the atmosphere through nitrification and denitrification, or of an indirect nature, mainly through nitrate leaching, runoff, and (Formula presented.) volatilization processes. (Formula presented.) emissions are largely ascribed to the agricultural sector, which represents a threat to sustainability and food production, subsequent to the radical contribution to climate change. In this connection, it is crucial to unveil the relationship between synthetic N fertilizer global use and (Formula presented.) emissions. To this end, we worked on a dataset drawn from a recent study, which estimates direct and indirect (Formula presented.) emissions according to each country, by the Intergovernmental Panel on Climate Change (IPCC) guidelines. Machine learning tools are considered great explainable techniques when dealing with air quality problems. Hence, our work focuses on expectile regression (ER) based-approaches to predict (Formula presented.) emissions based on N fertilizer use. In contrast to classical linear regression (LR), this method allows for heteroscedasticity and omits a parametric specification of the underlying distribution. ER provides a complete picture of the target variable’s distribution, especially when the tails are of interest, or in dealing with heavy-tailed distributions. In this work, we applied expectile regression and the kernel expectile regression estimator (KERE) to predict direct and indirect (Formula presented.) emissions. The results outline both the flexibility and competitiveness of ER-based techniques in regard to the state-of-the-art regression approaches.},\n bibtype = {article},\n author = {Benghzial, K. and Raki, H. and Bamansour, S. and Elhamdi, M. and Aalaila, Y. and Peluffo-Ordóñez, D.H.},\n doi = {10.3390/atmos14020283},\n journal = {Atmosphere},\n number = {2}\n}
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\n Agriculture accounts for a large percentage of nitrous oxide ((Formula presented.)) emissions, mainly due to the misapplication of nitrogen-based fertilizers, leading to an increase in the greenhouse gas (GHG) footprint. These emissions are of a direct nature, released straight into the atmosphere through nitrification and denitrification, or of an indirect nature, mainly through nitrate leaching, runoff, and (Formula presented.) volatilization processes. (Formula presented.) emissions are largely ascribed to the agricultural sector, which represents a threat to sustainability and food production, subsequent to the radical contribution to climate change. In this connection, it is crucial to unveil the relationship between synthetic N fertilizer global use and (Formula presented.) emissions. To this end, we worked on a dataset drawn from a recent study, which estimates direct and indirect (Formula presented.) emissions according to each country, by the Intergovernmental Panel on Climate Change (IPCC) guidelines. Machine learning tools are considered great explainable techniques when dealing with air quality problems. Hence, our work focuses on expectile regression (ER) based-approaches to predict (Formula presented.) emissions based on N fertilizer use. In contrast to classical linear regression (LR), this method allows for heteroscedasticity and omits a parametric specification of the underlying distribution. ER provides a complete picture of the target variable’s distribution, especially when the tails are of interest, or in dealing with heavy-tailed distributions. In this work, we applied expectile regression and the kernel expectile regression estimator (KERE) to predict direct and indirect (Formula presented.) emissions. The results outline both the flexibility and competitiveness of ER-based techniques in regard to the state-of-the-art regression approaches.\n
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\n  \n 2022\n \n \n (1)\n \n \n
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\n \n\n \n \n \n \n \n \n Crop Classification Using Deep Learning: A Quick Comparative Study of Modern Approaches.\n \n \n \n \n\n\n \n Raki, H.; González-Vergara, J.; Aalaila, Y.; Elhamdi, M.; Bamansour, S.; Guachi-Guachi, L.; and Peluffo-Ordoñez, D.\n\n\n \n\n\n\n Volume 1643 CCIS 2022.\n \n\n\n\n
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@book{\n title = {Crop Classification Using Deep Learning: A Quick Comparative Study of Modern Approaches},\n type = {book},\n year = {2022},\n source = {Communications in Computer and Information Science},\n keywords = {Convolutional neural networks,Deep learning,Smart farming},\n volume = {1643 CCIS},\n websites = {https://link.springer.com/chapter/10.1007/978-3-031-19647-8_3},\n id = {a0a1ba48-10d3-3c1d-bd8a-b4f4351acead},\n created = {2023-03-14T21:54:18.595Z},\n accessed = {2023-05-23},\n file_attached = {false},\n profile_id = {be6263d9-e5f6-3436-8031-6661eb6f0f4d},\n last_modified = {2023-05-23T15:36:28.003Z},\n read = {false},\n starred = {false},\n authored = {true},\n confirmed = {false},\n hidden = {false},\n private_publication = {true},\n abstract = {Automatic crop classification using new technologies is recognized as one of the most important assets in today’s smart farming improvement. Investments in technology and innovation are key issues for shaping agricultural productivity as well as the inclusiveness and sustainability of the global agricultural transformation. Digital image processing (DIP) has been widely adopted in this field, by merging Unmanned Aerial Vehicle (UAV) based remote sensing and deep learning (DL) as a powerful tool for crop classification. Despite the wide range of alternatives, the proper selection of a DL approach is still an open and challenging issue. In this work, we carry out an exhaustive performance evaluation of three remarkable and lightweight DL approaches, namely: Visual Geometry Group (VGG), Residual Neural Network (ResNet) and Inception V3, tested on high resolution agriculture crop images dataset. Experimental results show that InceptionV3 outperforms VGG and ResNet in terms of precision (0,92), accuracy (0,97), recall (0,91), AUC (0,98), PCR (0,97), and F1 (0,91).},\n bibtype = {book},\n author = {Raki, H. and González-Vergara, J. and Aalaila, Y. and Elhamdi, M. and Bamansour, S. and Guachi-Guachi, L. and Peluffo-Ordoñez, D.H.},\n doi = {10.1007/978-3-031-19647-8_3}\n}
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\n Automatic crop classification using new technologies is recognized as one of the most important assets in today’s smart farming improvement. Investments in technology and innovation are key issues for shaping agricultural productivity as well as the inclusiveness and sustainability of the global agricultural transformation. Digital image processing (DIP) has been widely adopted in this field, by merging Unmanned Aerial Vehicle (UAV) based remote sensing and deep learning (DL) as a powerful tool for crop classification. Despite the wide range of alternatives, the proper selection of a DL approach is still an open and challenging issue. In this work, we carry out an exhaustive performance evaluation of three remarkable and lightweight DL approaches, namely: Visual Geometry Group (VGG), Residual Neural Network (ResNet) and Inception V3, tested on high resolution agriculture crop images dataset. Experimental results show that InceptionV3 outperforms VGG and ResNet in terms of precision (0,92), accuracy (0,97), recall (0,91), AUC (0,98), PCR (0,97), and F1 (0,91).\n
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