Nguyen Minh Khiem

Abstract Diseases in shrimp farms in the Mekong Delta of Vietnam cause significant crop losses and are therefore of great concern to producers. Once a pond becomes infected, it is difficult to prevent spread of the disease to nearby shrimp farming areas. Thus, predicting the occurrence of disease is an essential part of reducing the risk for shrimp farmers. In this study, we applied an integrated geographic information system and machine learning system to predict three serious diseases of shrim

Predicting the export price of shrimp is important for Vietnam's fisheries. It not only promotes product quality but also helps policy makers determine strategies to develop the national shrimp industry. Competition in global markets is considered to be an important factor, one that significantly influences price. In this study, we predicted trends in the export price of Vietnamese shrimp based on competitive information from six leading exporters (China, India, Indonesia, Thailand, Ecuador, and

Applying Artificial Intelligence (AI) to the monitoring of live fish in natural environments represents a promising approach to the sustainable management of aquatic resources. Detecting and counting fish in water through video analysis is crucial for fish population statistics. This study employs AI algorithms, specifically YOLOv10 (You Only Look Once version 10) for identifying the presence fish in video frames, combined with the DeepSORT (Deep Simple Online and Realtime Tracking) algorithm to

ThTomatoes are a globally important crop, essential for human nutrition. However, their leaves are highly susceptible to various bacterial and fungal diseases, which can significantly reduce both the quantity and quality of yields. The use of Artificial Intelligence in disease management represents a major advancement in modern agriculture. Early and accurate identification of diseases is crucial for effective intervention, reducing crop losses, and improving overall productivity. In this study,