Quantum-Enhanced Deep Learning Framework for Leaf Disease Prediction and Classification

Recognising plant leaf diseases from images is difficult because symptoms of different diseases often look alike, and expert inspection is not always available to growers. This paper presents a hybrid quantum deep learning framework that predicts both the plant species and the disease of a leaf image in a single pass. An ImageNet-pretrained MobileNetV2 backbone extracts a high-dimensional feature vector, which a small projection reduces to four values that drive a four-qubit variational quantum circuit built from angle embedding and strongly entangling layers. The resulting Pauli-Z expectation values are concatenated with the classical features and passed to separate plant and disease classification heads. The model is compared with a MobileNetV2-only baseline on an identical stratified train–test split covering six crops and twenty disease and healthy classes. The hybrid model scores higher than the baseline on accuracy, precision, recall and F1-score for both tasks, although the margins are modest and come from a single split. The framework is also deployed as a Flask web application that adds blur and leaf validation, leaf segmentation, Grad-CAM explanations and disease-management guidance. The results are therefore presented as preliminary evidence of the value of a compact quantum feature stage rather than a definitive advantage.