Ensuring crop health, optimizing yields, and facilitating sustainable agricultural practices depend on early detection and exact classification of rice diseases, particularly in South and Southeast Asian nations like India and Malaysia where rice is a dietary mainstay and economic backbone. Using a hybrid deep learning framework combining Convolutional Neural Networks (CNNs) with Quantum Neural Networks (QNNs) on AI edge devices, this article presents a unique real-time rice illness diagnosis and field suggestion system. While a parameterized quantum circuit incorporating Hadamard, CNOT, and Ry gates improves feature representation and classification resilience, a transfer learning technique using a pre-trained ResNet152 architecture allows effective feature extraction from rice leaf pictures. Included to reduce decoherence and noise influences are quantum error correcting methods as Shor’s Code and surface code approaches. Ensuring exact illness identification, the trained hybrid model has an F1 score of
96% and a total classification accuracy of
96.8%. Integrated recommendation logic links identified illnesses to customized agronomic prescriptions, including field treatments, nutrition management, and focused pesticide use. Deployment on AI edge devices allows completely offline, real-time inference, hence giving farmers instant actionable information in the field. Aiming to provide farmers with smart, scalable, and sustainable disease control tools, this paper underlines the transforming power of merging hybrid quantum-classical learning, transfer learning, and mobile edge artificial intelligence for precision agriculture.
Medical and agricultural sector needs to make timely and accurate diagnosis of the disease in order to sustain crops and keep patients alive. Although conventional deep learning models can be also applicable to provide good performance, they are also typified with high-computational and scaling along with high-dimensionality agricultural and medical images. In a bid to overcome these challenges, the current paper proposes a Hybrid Quantum -Classical Artificial Intelligence (HQCAI) system that should be used in dual-domain image classification, namely, plant leaf disease classification and oesophageal cancer classification. The proposed architecture implements a conventional Convolutional Neural Network (CNN) to obtain spatial features of high quality and quantum-enhanced classifiers to optimize the decision boundaries relying on quantum superposition and parallelism with Variational Quantum Circuits (VQCs) and Quantum Support Vector Machines (QSVMs). It presents a common knowledge representation layer in order to help in the effective storage of characteristics, cross-domain learning and intelligent inference between heterogeneous data sets. Experiments are carried out with the help of the data collection. The proposed hybrid model gives a better classification rate of 98.42, with precision of 98.11, recall of 97.94 and F1-score of 98.02 compared to CNN and CNN-SVM models in the state of the art. Convergence and performance of the system is also better when noises are present. The results support the practical role of quantum-enhanced visual classification and describe the prospects of hybrid quantum classical intelligence of the agricultural diagnostics and medical diagnostics systems in the future.