Enhancing crop recommendation systems with explainable artificial intelligence: a study on agricultural decision-making

Crop Recommendation Systems are invaluable tools for farmers, assisting them in making informed decisions about crop selection to optimize yields. These systems leverage a wealth of data, including soil characteristics, historical crop performance, and prevailing weather patterns, to provide persona...

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Veröffentlicht in:	Neural computing & applications Jg. 36; H. 11; S. 5695 - 5714
Hauptverfasser:	Shams, Mahmoud Y., Gamel, Samah A., Talaat, Fatma M.
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	London Springer London 01.04.2024 Springer Nature B.V
Schlagworte:	Algorithms Artificial Intelligence Computational Biology/Bioinformatics Computational Science and Engineering Computer Science Crop yield Data Mining and Knowledge Discovery Decision making Decision trees Explainable artificial intelligence Harnesses Image Processing and Computer Vision Machine learning Original Article Performance evaluation Probability and Statistics in Computer Science Recommender systems Agriculture Crop recommendation systems eXplainable artificial intelligence Decision support system Machine learning
ISSN:	0941-0643, 1433-3058
Online-Zugang:	Volltext
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