Rice Yield Forecasting Using Hybrid Quantum Deep Learning Model

In recent advancements in agricultural technology, quantum mechanics and deep learning integration have shown promising potential to revolutionize rice yield forecasting methods. This research introduces a novel Hybrid Quantum Deep Learning model that leverages the intricate processing capabilities...

詳細記述

書誌詳細

主要な著者:	De Rosal Ignatius Moses Setiadi, Ajib Susanto, Kristiawan Nugroho, Ahmad Rofiqul Muslikh, Arnold Adimabua Ojugo, Hong-Seng Gan
フォーマット:	Artigo
言語:	Inglês
出版事項:	MDPI AG 2024-08-01
シリーズ:	Computers
主題:	hybrid quantum deep learning rice production forecasting quantum feature processing quantum machine learning XGBoost regressor
オンライン･アクセス:	https://www.mdpi.com/2073-431X/13/8/191
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