Climate change has had a global effect on staple crops. Indonesia is a developed country facing a significant threat to climate change. The study uses the Normalized Difference Water Index (NDWI) obtained from Landsat 8 OLI to define the water scarcity in the study area. This research proposes a CNN-based YOLO model that can detect Drought in growing maize development stages. The study was observed in 2018. The detection drought based on the growing season using deep learning was found IoU, Precision, Recall, F1-Score, mean Average Precision (mAP), 83.4%, 98%, 99%, 98%, 96% in the drought-prone areas. The model allows combining remote sensing technology to detect object detection in real-time with acceptable accuracy.

Marine vessels or ships have been considered one of the primary vehicles used for sea transportation, which can also be used as an intermediary tool to serve numerous other marine-related activities. In tracking and monitoring the activities of these ships, automatic vessel object detection is undoubtedly challenging to extract the number and position of the vessels from complex seawater backgrounds. In this study, we build a one-stage network of YOLOv5x6 based deep learning model on ShipRSImageNet large-scale dataset. With 50 ship categories, our model obtained a promising performance with a mean average precision of 75.18%. Our findings are potentially beneficial to support maritime security enforcement policy including counter-measuring illegal fisheries and managing seawater traffic surveillance.