Rice Yield Forecasting Using Hybrid Quantum Deep Learning Model

De Rosal Ignatius Moses Setiadi*, Ajib Susanto, Kristiawan Nugroho, Ahmad Rofiqul Muslikh, Arnold Adimabua Ojugo, Hong Seng Gan

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

7 Citations (Scopus)

Abstract

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 of quantum computing combined with the robust pattern recognition prowess of deep learning algorithms such as Extreme Gradient Boosting (XGBoost) and Bidirectional Long Short-Term Memory (Bi-LSTM). Bi-LSTM networks are used for temporal feature extraction and quantum circuits for quantum feature processing. Quantum circuits leverage quantum superposition and entanglement to enhance data representation by capturing intricate feature interactions. These enriched quantum features are combined with the temporal features extracted by Bi-LSTM and fed into an XGBoost regressor. By synthesizing quantum feature processing and classical machine learning techniques, our model aims to improve prediction accuracy significantly. Based on measurements of mean square error (MSE), the coefficient of determination ((Formula presented.)), and mean average error (MAE), the results are 1.191621 × 10−5, 0.999929482, and 0.001392724, respectively. This value is so close to perfect that it helps make essential decisions in global agricultural planning and management.

Original languageEnglish
Article number191
JournalComputers
Volume13
Issue number8
DOIs
Publication statusPublished - Aug 2024

Keywords

  • hybrid quantum deep learning
  • quantum feature processing
  • quantum machine learning
  • rice production forecasting
  • XGBoost regressor

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