This study investigates how precision agriculture and crop production predictions may be improved using Quantum Machine Learning (QML) models, namely the Variational Quantum Circuit (VQC). The VQC outperformed traditional linear regression and other quantum models such as Quantum Neural Networks (QNN) and Quantum Convolutional Neural Networks (QCNN) by using quantum computing's ability to analyze high-dimensional agricultural data. With the lowest Mean Squared Error (MSE: 28.00), Mean Absolute Error (MAE: 3.8), and greatest R-squared (R2: 0.97), the VQC successfully identified intricate relationships between input variables such as acreage, rainfall, and fertilizer usage. Classical models, on the other hand, had more prediction errors and showed serious limits. This work opens the door for further research and the use of quantum technologies in agricultural systems by demonstrating the revolutionary potential of QML, particularly VQC, in tackling issues in agriculture, including food security, resource sustainability, and climate resilience.
This study investigates how precision agriculture and crop production predictions may be improved using Quantum Machine Learning (QML) models, namely the Variational Quantum Circuit (VQC). The VQC outperformed traditional linear regression and other quantum models such as Quantum Neural Networks (QNN) and Quantum Convolutional Neural Networks (QCNN) by using quantum computing's ability to analyze high-dimensional agricultural data. With the lowest Mean Squared Error (MSE: 28.00), Mean Absolute Error (MAE: 3.8), and greatest R-squared (R2: 0.97), the VQC successfully identified intricate relationships between input variables such as acreage, rainfall, and fertilizer usage. Classical models, on the other hand, had more prediction errors and showed serious limits. This work opens the door for further research and the use of quantum technologies in agricultural systems by demonstrating the revolutionary potential of QML, particularly VQC, in tackling issues in agriculture, including food security, resource sustainability, and climate resilience.
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 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.
Agriculture is increasingly threatened by diverse global security challenges, including climate change, biodiversity loss, and health security risks, underscoring the need for sustainable practices to ensure food security for future generations. Advanced technologies such as quantum machine learning (QML) offer transformative potential in addressing these challenges by enhancing crop disease monitoring systems. This paper explores the implementation of QML in agriculture, with a particular focus on its application to crop disease detection and prevention. Leveraging image processing, sensory data analysis, and spectral imaging, QML algorithms demonstrate superior accuracy and efficiency in early disease recognition, surpassing traditional methods. A case study of Hungary's crop disease monitoring system illustrates the practical benefits of integrating QML with technologies such as remote sensing and IoT. By enabling real-time monitoring and predictive analytics, QML not only aids in mitigating crop losses but also contributes to sustainable agricultural practices and global food security. Despite current limitations in quantum hardware, advancements in QML present promising opportunities for revolutionizing agricultural systems and ensuring resilience against evolving threats.
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.
Precision agriculture is experiencing a technological transformation driven by artificial intelligence (AI), the Internet of Things (IoT), and big data analytics.Nonetheless, traditional computation models find it difficult to handle large-scale high-dimensional datasets in agriculture.By utilizing phenomena like superposition and entanglement, quantum computing represents a fundamental departure that can greatly accelerate and optimize such algorithms.This paper presents the Hybrid Quantum Optimization and Machine Learning Framework (HQOMLF), which comprises its derivatives, such as the Quantum-Enhanced Resource Allocation (QERA) and Quantum Convolutional Neural Networks (QCNN) for effective resource distribution and predictive analytics.The proposed method outperforms classical models with respect to computation speed, accuracy, and scalability.It demonstrates via experiments that processing speed can be improved up to 3.5× and resource optimization of SBT can be enhanced by 11.3% using the proposed method.Based on data analysis, there are several unsolved problems, like hardware limitations and complexity of the algorithm, but quantum computing in agronomics has well-picking potential.These results set the stage for future quantum-classical hybrid models towards sustaining, data-driven agricultural solutions.
Contemporary agriculture is having major difficulties in assuring nutrient delivery and caring for the environment. The paper proposes integrating quantum machine learning (QML) techniques with precision agriculture tools to bring improvements to nutrient management. Predicting the correct amount, timing and distribution of fertilizer works much better with our new quantum-based strategy than with established ones. Analyzing the interactions among soil, plants and the atmosphere with quantum variational circuits and quantum kernel methods allows us to predict how much fertilizer is needed with an accuracy of 94.7% and helps lower waste by 38.2%. With QAOA implementation, real-time adjustments can be done in variable-rate systems. This type of research aims to overcome important problems with current algorithms unable to work with high-dimensional datasets in agriculture. This work creates the base for new agriculture systems that help achieve high crop yields while keeping the environment safe through quick and effective methods of optimization.