TY - JOUR
T1 - Rice Yield Forecasting Using Hybrid Quantum Deep Learning Model
AU - Setiadi, De Rosal Ignatius Moses
AU - Susanto, Ajib
AU - Nugroho, Kristiawan
AU - Muslikh, Ahmad Rofiqul
AU - Ojugo, Arnold Adimabua
AU - Gan, Hong Seng
N1 - Publisher Copyright:
© 2024 by the authors.
PY - 2024/8
Y1 - 2024/8
N2 - 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.
AB - 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.
KW - hybrid quantum deep learning
KW - quantum feature processing
KW - quantum machine learning
KW - rice production forecasting
KW - XGBoost regressor
UR - http://www.scopus.com/inward/record.url?scp=85202622829&partnerID=8YFLogxK
U2 - 10.3390/computers13080191
DO - 10.3390/computers13080191
M3 - Article
AN - SCOPUS:85202622829
SN - 2073-431X
VL - 13
JO - Computers
JF - Computers
IS - 8
M1 - 191
ER -