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The accelerated growth of Artificial Intelligence (AI) and Quantum Computing (QC) has unlocked transformative potential for solving complex challenges in climate-adaptive and energy-efficient agriculture. Traditional deep learning frameworks often struggle to manage the massive heterogeneity, temporal variance, and computational intensity inherent in multi-source agro-climatic data. To overcome these limitations, this work introduces AgriQDL (Quantum-Driven Deep Learning for Agriculture) — an advanced hybrid paradigm that combines the superior pattern recognition of deep neural networks with the parallel computational capabilities of quantum circuits. First Objective focuses on integrating Variational Quantum Circuits (VQCs) with deep architectures to accelerate learning across high-dimensional satellite, soil, weather, and IoT-sensor datasets. The second objective aims to employ quantum-inspired backpropagation and Quantum Generative Adversarial Networks (QGANs) for efficient feature learning and synthetic data generation in regions with sparse ground-truth samples. The final objective embeds the AgriQDL framework into a Federated Learning (FL) environment to ensure secure, decentralized model training that preserves community data privacy while supporting collaborative intelligence. The proposed AgriQDL architecture integrates quantum-enhanced convolutional and recurrent layers for spatial–temporal feature extraction, where VQCs perform high-speed state transformations and gradient evaluations. The QGAN module supplements limited datasets by generating high-fidelity synthetic samples, thereby mitigating regional data scarcity and improving model generalization. The FL-enabled deployment ensures that each agricultural community trains its local model while contributing encrypted gradients to a global quantum-assisted model aggregator, achieving scalability and confidentiality simultaneously. Comprehensive evaluations were conducted across multiple agro-climatic zones using diverse datasets encompassing satellite imagery, soil moisture indices, humidity, and crop-yield records. Comparative analysis against conventional deep learning baselines — including CNN, LSTM, and hybrid CNN-BiLSTM models — demonstrates that AgriQDL achieves an average accuracy improvement of 18%, reduces training time by up to 45%, and enhances resilience to climate variability and data imbalance. These outcomes highlight AgriQDL’s capability to perform rapid, resource-efficient agricultural analytics while maintaining strong privacy guarantees. The results establish AgriQDL as a robust foundation for next-generation quantum–AI-driven precision agriculture systems.