Performance Evaluation of Deep Learning Model according to the Ratio of Cultivation Area in Training Data

Compact Advanced Satellite 500 (CAS500) can be used for various purposes, including vegetation, forestry, and agriculture fields. It is expected that it will be possible to acquire satellite images of various areas quickly. In order to use satellite images acquired through CAS500 in the agricultural field, it is necessary to develop a satellite image-based extraction technique for crop-cultivated areas. In particular, as research in the field of deep learning has become active in recent years, research on developing a deep learning model for extracting crop cultivation areas and generating training data is necessary. This manuscript classified the onion and garlic cultivation areas in Hapcheon-gun using PlanetScope satellite images and farm maps. In particular, for effective model learning, the model performance was analyzed according to the proportion of crop-cultivated areas. For the deep learning model used in the experiment, Fully Convolutional Densely Connected Convolutional Network (FC-DenseNet) was reconstructed to fit the purpose of crop cultivation area classification and utilized. As a result of the experiment, the ratio of crop cultivation areas in the training data affected the performance of the deep learning model.