Rice is the most preferred grain worldwide, leading to the development of an automated method using convolutional neural networks (CNNs) for classifying rice types. This study evaluates the effectiveness of hybrid CNN models, including AlexNet, ResNet50, and EfficientNet‐b1, in distinguishing five major rice varieties grown in Turkey: Arborio, Basmati, Ipsala, Jasmine, and Karacadag. It is estimated that there are 75,000 photographs of grains, with 15,000 images corresponding to each type. The training is improved by the use of preprocessing and optimization approaches. The performance of the model was assessed based on sensitivity, specificity, precision, F 1 score, and confusion matrix analysis. The results show that EfficientNet‐b1 achieved an accuracy of 99.87%, which is higher than the accuracy achieved by AlexNet (96.00%) and ResNet50 (99.00%). This study shows that EfficientNet‐b1 is superior to other models that have emerged as state‐of‐the‐art automated classification models for rice varieties. This indicates that there is a balance between the computational efficiency and the accuracy of EfficientNet‐b1. These results exemplify the potential of CNN models for agriculture by reducing the restrictions associated with conventional classification approaches. These limitations include subjectivity and inconsistency regarding categorization.

This study presents a novel hybrid quantum‐classical framework for rice disease diagnosis, leveraging variational quantum circuits (VQCs) to address the limitations of traditional and deep learning models in precision agriculture. The proposed Quantum Variational Rice Disease Network (QVRDN) integrates quantum feature encoding, variational quantum processing, and adaptive optimization to achieve superior classification accuracy, efficiency, and robustness. Using a curated dataset of 3000 annotated rice leaf images spanning major disease categories, the QVRDN framework applies dimensionality reduction and quantum angle encoding to transform the image features into quantum states, which are then processed by parameterized quantum circuits for disease classification. Experimental results demonstrate that QVRDN outperforms classical models, including SVM, random forest, CNN, and ResNet50‐achieving, the highest accuracy of 97.8%, faster inference times, and greater resilience to noise and limited data. The compact design of the framework enables edge deployment without GPU dependency, making it suitable for resource‐constrained agricultural environments. By demonstrating the feasibility and advantages of quantum machine learning in crop health monitoring, this study establishes a foundation for quantum‐enhanced, data‐efficient agricultural diagnostics and paves the way for future advances in intelligent, field‐ready quantum geoinformatics systems.