Comparing classical and quantum machine learning approaches for crop and weed detection

Abstract

Crop and weed detection is an essential task in precision agriculture which is typically approached through classical machine learning, and more recently, through quantum machine learning (QML). This research compares classical and quantum approaches for crop-weed distinctions in aerial field photography. A convolutional neural network (CNN) stands as a classical baseline, and two QML models, a quantum support vector classifier and a variational quantum classifier (VQC) are utilized with a publicly accessible annotated dataset. The models are evaluated in terms of classification accuracy, inference velocity, and computational complexity. The CNN achieves a peak test accuracy of approximately 94%, which surpasses the QML approaches yielding 85%–89% accuracies. fiCNN achieves substantially faster inference, 10 ms per image and hundreds of milliseconds for quantum models due to circuit execution and simulation cost. While worse at predicting, QML models still consume considerably fewer trainable parameters (tens compared to over one million for the CNN), which may signal benefits on model compactness. The results show that classical deep learning is the most effective and reliable approach for crop and weed detection in 2025, while QML models represent a promising future direction and will become more competitive as the computing hardware and algorithms advance.