Individualized and accurate evaluation of nutrient intake is essential for good health. Disease prevention and increased food security This article combines image analysis with quantum algorithms for precise food insights, introducing an advanced quantum-enhanced AI system. It is designed to predict the nutritional content of foods consumed. The system starts with taking photos of food using a Convolutional Neural Network (CNN) is processed by it has a classification accuracy of 91.87%. User-specific information such as age, weight, height, and BMI are also used to calculate individual nutritional needs. It enables tailored dietary recommendations. Quantum Support Vector Machines (QSVM), Quantum Neural Network (QNN), and Quantum Reinforcement Learning (QRL). The system's Leveraging (QRL) has high prediction accuracies of 90%, 92%, and 93%, ensuring efficient nutritional analysis in different foods. Integrating quantum computer models will greatly improve predictive performance and scalability. This has led to advances in bioengineering applications related to personalized nutrition. The proposed approach has the potential to be widely applied in health care. By helping with personal nutrition planning and supporting nutritional decision-making at a granular level.
Individualized and accurate evaluation of nutrient intake is essential for good health. Disease prevention and increased food security This article combines image analysis with quantum algorithms for precise food insights, introducing an advanced quantum-enhanced AI system. It is designed to predict the nutritional content of foods consumed. The system starts with taking photos of food using a Convolutional Neural Network (CNN) is processed by it has a classification accuracy of 91.87%. User-specific information such as age, weight, height, and BMI are also used to calculate individual nutritional needs. It enables tailored dietary recommendations. Quantum Support Vector Machines (QSVM), Quantum Neural Network (QNN), and Quantum Reinforcement Learning (QRL). The system's Leveraging (QRL) has high prediction accuracies of 90%, 92%, and 93%, ensuring efficient nutritional analysis in different foods. Integrating quantum computer models will greatly improve predictive performance and scalability. This has led to advances in bioengineering applications related to personalized nutrition. The proposed approach has the potential to be widely applied in health care. By helping with personal nutrition planning and supporting nutritional decision-making at a granular level.
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.