A crop pest classification using deep learning & QML

Classifying agricultural pests is essential for sustaining good crop yields, but doing it manually is costly and time-consuming. We present a dl method that uses CNNs to reliably categories a wide variety of agricultural pests. In particular, we use a huge dataset of insect photos to train a CNN model, and then compare its performance to that of other state-of-the-art classifiers like support vector machines. We also investigate how QML may improve the quality of derived features in image analysis, classification, including feature extraction. Our findings demonstrate that the CNN-based system we developed outperforms competing classifiers, and also that QML is a useful tool for identifying pests in agricultural settings.