Hybrid AI–Quantum Architecture for Sustainable Plant Disease Detection and Pesticide Recommendation

Plant diseases, pest attacks, and nutrient imbalances significantly affect agricultural productivity and can reduce both yield and quality. Timely and accurate identification of these issues is essential for effective crop management and sustainable farming. Conventional manual inspection methods are often inefficient and inconsistent, while existing deep learning approaches may struggle to differentiate between diseases with similar visual characteristics, leading to inaccurate predictions and suboptimal treatment choices. To overcome these limitations, this work introduces a hybrid classical–quantum framework aimed at improving disease detection and decision-making. The proposed system uses EfficientNet-B0 to learn rich visual representations from plant images, then processes them with a variational quantum circuit to capture complex, nonlinear feature interactions. By integrating classical and quantum techniques, the model enhances classification performance beyond traditional methods. Beyond accurate disease identification, the system provides environmentally conscious pesticide recommendations, supporting responsible chemical use. Overall, this approach serves as an effective intelligent decision-support system, contributing to more precise and sustainable agricultural practices.