A hybrid quantum–classical convolutional neural network with EfficientNet-B0 and PSO-based feature optimization for multiclass plant leaf disease classification

Plant leaf diseases are a critical threat to global food security, with early detection complicated by high inter-class similarity, environmental variations, and the demand for scalable multiclass classification across diverse crops. To address these challenges, we propose P-HQNN, a lightweight yet robust 4-qubit Hybrid Quantum–Classical Convolutional Neural Network designed for near-term quantum devices. The model uses EfficientNet-B0 to extract deep feature, and Particle Swarm Optimization (PSO) with Random Forest ranking is used to reduce the 1280-dimensional embeddings into four highly discriminative features that can be represented using quantum encoding. These features are next coded as a 4-qubit quantum circuit of ZZFeatureMap and RealAmplitudes ansatz and a hybrid quantum layer is formed whose outputs are concatenated with a CNN classifier to arrive at final predictions. This optimization strategy of features representation based on quantum awareness balances high-dimensional representation and qubit constraints, and is proved to be more stable in 4-qubit encoding and better in comparison to higher-qubit encoding under noisy conditions. Experimental evaluation on the “New Plant Diseases Dataset” achieved 98.2% accuracy, 98.5% precision, 97.7% recall, 98% F1-score, and a macro ROC-AUC of 0.985, confirming that low-qubit hybrid models can deliver state-of-the-art plant disease classification while ensuring practicality and scalability for real-world agricultural applications on near-term quantum computing platforms.