Smart agriculture has emerged as a transformative approach to enhance crop productivity, resource efficiency, and environmental sustainability using advanced sensing and computational technologies. Wireless Sensor Networks (WSNs) enable real-time monitoring of agricultural parameters such as soil moisture, temperature, humidity, and crop health. However, traditional machine learning models struggle to efficiently process high-dimensional, noisy, and heterogeneous agricultural sensor data. This study proposes a Wireless Sensor-Driven Smart Agriculture framework using Scalable Quantum Convolutional Models (SQCM). The proposed model integrates wireless sensor data acquisition with quantum-inspired convolutional neural networks to enhance pattern recognition, predictive accuracy, and scalability. The quantum convolutional layers enable high-dimensional feature representation, while classical layers ensure efficient decision-making for agricultural optimization. The model is evaluated using agricultural sensor datasets, and performance is measured using accuracy, precision, recall, and RMSE for environmental prediction tasks. Experimental results demonstrate that the proposed quantum-enhanced architecture outperforms traditional machine learning and deep learning models in crop condition prediction and resource optimization. The framework is suitable for precision agriculture, automated irrigation systems, and smart farming applications.
Smart agriculture has emerged as a transformative approach to enhance crop productivity, resource efficiency, and environmental sustainability using advanced sensing and computational technologies. Wireless Sensor Networks (WSNs) enable real-time monitoring of agricultural parameters such as soil moisture, temperature, humidity, and crop health. However, traditional machine learning models struggle to efficiently process high-dimensional, noisy, and heterogeneous agricultural sensor data. This study proposes a Wireless Sensor-Driven Smart Agriculture framework using Scalable Quantum Convolutional Models (SQCM). The proposed model integrates wireless sensor data acquisition with quantum-inspired convolutional neural networks to enhance pattern recognition, predictive accuracy, and scalability. The quantum convolutional layers enable high-dimensional feature representation, while classical layers ensure efficient decision-making for agricultural optimization. The model is evaluated using agricultural sensor datasets, and performance is measured using accuracy, precision, recall, and RMSE for environmental prediction tasks. Experimental results demonstrate that the proposed quantum-enhanced architecture outperforms traditional machine learning and deep learning models in crop condition prediction and resource optimization. The framework is suitable for precision agriculture, automated irrigation systems, and smart farming applications.
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.
Abstract: The increasing global demand for food, coupled with the challenges of climate change, resource scarcity, and supply chain inefficiencies, necessitates transformative solutions in agriculture and food security. Traditional AI-driven agricultural systems, while effective, often struggle with processing vast, complex, and interdependent variables such as weather patterns, soil quality, pest outbreaks, and logistics optimization. Quantum AI, which merges the computational power of quantum computing and artificial intelligence, offers unprecedented capabilities in predictive analytics, real-time decision-making, and optimization for sustainable farming. This chapter explores Quantum AI’s applications in precision agriculture, climate-resilient farming, pest and disease prediction, and smart food supply chains, demonstrating how quantum-enhanced models optimize irrigation, enhance crop yield forecasts, and improve food distribution efficiency. Additionally, it highlights the challenges, ethical considerations, and future potential of Quantum AI in ensuring global food security while promoting environmental sustainability. By integrating Quantum AI with IoT, blockchain, and remote sensing technologies, this chapter outlines a strategic roadmap for transforming agriculture into a highly efficient, adaptive, and climate-resilient system, paving the way for a food-secure and ecologically sustainable future. Keywords: Quantum AI, food security, sustainable agriculture, precision farming, climate-resilient farming, quantum computing in agriculture, smart food supply chains, predictive analytics, crop yield optimization, pest and disease prediction, quantum-enhanced logistics, irrigation management, AI-driven agriculture, blockchain for food traceability, sustainable resource management, quantum optimization algorithms
Changes in Weather decrease in soil fertility, limited water resources and growing populations all are making agriculture harder. Even though precision agriculture uses traditional machine learning techniques, the majority of these approaches are unable to effectively handle the extremely complex and multidimensional data coming from crop genetics, weather systems, soil sensors, and satellite surveillance. In this regard, the current research presents a framework for intelligent agriculture that is based on quantum machine learning and quantum optimization. The system can represent and analyze the agricultural data far more efficiently than traditional computer methods by taking advantage of quantum phenomena like superposition and entanglement. Improved crop yield prediction and intelligent resource management via quantum-inspired optimization, particularly QAOA, are the two main objectives of the work. The model achieves accuracy values for yield estimates between 70 and 90%, according to simulation results, and it allows for real-time modification of fertilizer and irrigation. Lastly, it provides farmers with an easy-to-use dashboard to visualize insights. The current study provides a conceptual and technical foundation for quantum computing applications in agriculture in the near future, for more resource-efficient, data-driven, and sustainable farming, even though current quantum hardware is still in the development stage.
This study investigates the effectiveness of a hybrid quantum-classical framework for plant disease detection, with a focus on improving classification accuracy compared to conventional approaches. Image-based datasets of crop diseases were processed and encoded into variational quantum circuits (VQCs), which were then integrated with classical machine learning classifiers. The proposed model achieved an overall classification accuracy of 95.2%, outperforming a baseline convolutional neural network that recorded 91.4% accuracy on the same dataset. Performance evaluation was carried out using multiple metrics including precision, recall, and F1-score, with the hybrid approach showing consistent gains across all categories. Furthermore, the model demonstrated reduced overfitting and improved robustness when tested under noisy and high-dimensional input conditions. These results highlight the potential of quantum-assisted machine learning to significantly enhance crop disease detection, offering a scalable and accurate pathway for sustainable agriculture and precision farming.
The need for accuracy and scalability is imperative when it comes to monitoring soil health and detecting plant diseases using precision agriculture. This paper suggests a Quantum AI-based hybrid analytics approach that combines classical machine learning algorithms, Variational Quantum Circuits, and statistics modeling in order to provide improved agricultural decision making capabilities. For this purpose, we used the multi-modal datasets that include Soil Health Data set (Kaggle dataset, n=12,500) that contains soil pH, moisture, Nitrogen (N), Phosphorus (P), Potassium (K) information and PlantVillage dataset (54,306 leaf images from 38 classes). This paper proposes a hybrid framework of QSVM and QCNN to predict soil health status and detect crop diseases in a dataset. This research work has been experimentally evaluated with Qiskit simulator (8 qubits) and classical ML algorithms like Random Forest and CNN. Based on experimental results, it is clear that the proposed hybrid framework yields soil health prediction accuracy of 94.7%, which is better than classical ML approaches (88.3%). Further, the accuracy achieved by the QCNN framework for predicting crop disease is 97.2%, with precision, recall, and F1-score being 0.96, 0.97, and 0.965, respectively. It is observed that the computation time is reduced by around 21.4% in feature selection tasks. From the above discussion, it can be concluded that this paper explores the capability of Quantum Artificial Intelligence (AI) to provide timely assistance for precision farming applications.
The innovations of quantum computing, sophisticated tools, and artificial intelligence (AI) are changing the future of sustainable farming to present solutions to resource management, precise care, and environment monitoring of crops and animals. This section talks about how the application of quantum computing transforms agricultural data analysis, optimization, and simulation processes. It highlights that the integration of the IoT sensors, quantum-enhanced AI system models, and heterogeneous modeling methods makes it manageable and automates processes in the farms. The second portion of the chapter discusses the application of AI in crop prediction, identification of diseases and pests, examination of livestock DNA, and irrigation systems management. Despite some remaining hardware and integration problems, quantum–classical and multi-disciplinary science cooperation/collaboration will most likely lead to greater scalability, efficiency, and sustainability in agriculture. Adopting the most recent technologies in the agricultural production industry can increase production, reduce damage to the environment, and guarantee the availability of food as the global demands evolve.