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6 recherche sur le mot-clé
'APPRENTISSAGE PROFOND' 
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Article
Optimizing agricultural productivity and promoting sustainability necessitates accurate predictions of crop yields to ensure food security. Various agricultural and climatic variables are included in the analysis, encompassing crop type, year, s[...]
Article
D. Paudel ; A. de Wit ; H. Boogaard ; D. Marcos ; S. Osinga ; I.N. Athanasiadis |Machine learning models for crop yield forecasting often rely on expert-designed features or predictors. The effectiveness and interpretability of these handcrafted features depends on the expertise of the people designing them. Neural networks [...]
Article
The objective of this study was to provide a comprehensive overview of the recent advancements in the use of deep learning (DL) in the agricultural sector. The author conducted a review of studies published between 2016 and 2022 to highlight the[...]
Article
Machine learning (ML) and its branch, deep learning (DL), is rapidly evolving and gaining popularity as it outperforms other, more traditional methods in different areas of agriculture. However, ML and DL techniques must be correctly applied to [...]
Article
G. Cecili ; P. De Fioravante ; L. Congedo ; M. Marchetti ; M. Munafò |In recent years, deep learning (DL) algorithms have been widely integrated for remote sensing image classification, but fewer studies have applied it for land consumption (LC). LC is the main factor in land transformation dynamics and it is the [...]
E-Book
Deep Learning with R introduces the world of deep learning using the powerful Keras library and its R language interface. Initially written for Python as Deep Learning with Python by Keras creator and Google AI researcher François Chollet and ad[...]
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