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Quantum Fields of Vision UAV-Driven Rice Growth Stage Mapping With Quantum-Inspired Algorithms

Quantum Fields of Vision UAV-Driven Rice Growth Stage Mapping With Quantum-Inspired Algorithms
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Author(s): R. Deepa (SRM Institute of Science and Technology, India), S. Pushpalatha (Saveetha School of Engineering, India), B. Yasotha (SRM Institute of Science and Technology), R. Swathy (SRM Institute of Science and Technology, India), K. L. Shoba (SRM Institute of Science and Technology, India)and P. Thilakavathy (Vels Institute of Science, Technology, and Advanced Studies, India)
Copyright: 2026
Pages: 24
Source title: Advancing Environmental Research Through Applied GIS and Remote Sensing
Source Author(s)/Editor(s): Jamal Al Karkouri (Ibn Tofail University, Morocco), Adil Moumane (Ibn Tofail University, Morocco), Abdessamad Elmotawakkil (Ibn Tofail University, Morocco)and Mouhcine Batchi (Ibn Tofail University, Morocco)
DOI: 10.4018/979-8-3373-6608-1.ch011

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Abstract

Food safety and precision farming need accurate plot-scale rice yield predictions, so we developed a method combining UAV‐derived vegetation indices (VIs) with brightness, greenness and moisture data from tasseled cap transformation (TCT). Eight nitrogen gradients of rice were used during the booting and heading stages to obtain ground truth and six-band UAV imagery. We propose a hybrid quantum learning model that uses Bi-LSTM for extracting temporal features and quantum circuits for quantum feature processing. These enhanced features are combined with Bi-LSTM outputs into an XGBoost regressor. Our Quantum BiLSTM + XGBoost approach outperformed traditional models by 7-10%, achieving ~94% accuracy.

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