Artificial Intelligence (AI) and Deep Learning are increasingly finding their way into technology solutions in agriculture. However, development of these models is extremely data-intensive and requires a lot of labeling work.
Within this project, the application of semi-supervised learning is investigated for anomaly detection in potato cultivation as a decision support tool for farmers and advisors.
