Matthiola incana classification using CNN and class activation maps for model understanding

In the production of seedlings for flowers and vegetables, selective differentiation is widely performed for only seedlings with high market-value prospects; however, many tasks require specialized knowledge. Mistakenly discarding seedlings with high market value leads to economic losses for farmers, making it crucial to provide a reliable system that also explains the basis for differentiation. This study proposes a method for the reliable classification of Matthiola incana (M. incana) by utilizing neural network-based image classification, supported by visualizing class activation maps to provide evidence for differentiation. Additionally, the study introduces the MiLIC (Matthiola incana Leaf Image Classification) dataset, developed for use in training the classification model. The dataset is available at https://huggingface.co/datasets/Kentaro-Machida/Matthiola-incana-Leaf-Image-Classification.