Abstract
In the seed-producing industry, accurate assessment of harvested seeds for technical purity is a necessary, yet time-consuming and labor-intensive task. Automating this task holds immense potential for enhancing agricultural seed productivity, and using computer vision methods to classify seeds has already demonstrated promising results. Here, we propose a novel spectral-enhanced image anomaly detection approach to accurately discriminate Canola seeds (Brassica napus L.) from visually similar non-Canola seeds. Our bimodal approach exploits both RGB and data captured by a hyperspectral camera of the same sample. For efficient processing of this data, we suggest a novel bimodal convolutional autoencoder (BiCAE) architecture, which combines the strengths of high spatial resolution in RGB and high spectral resolution in hyperspectral data. We demonstrate that training our BiCAE model on a Canola dataset allows to learn a joint latent representation that effectively extracts spatio-spectral information from both RGB and hyperspectral data. Experiments show promising results in differentiating between Canola and non-Canola samples, in particular in detecting various types of non-Canola seeds in previously unseen test data. The obtained results highlight the model's ability to generalize beyond the training data, surpassing unimodal models that rely solely on a single modality.
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