The role of machine learning technology for fintech security: a Literature Review

Abstract

Abstract. Technological advances such as the Internet of Things (IoT), remote sensing, and artificial intelligence (AI) have driven the emergence of various innovations in the field of precision agriculture. This study summarizes the results of five recent studies discussing the utilization of machine learning, deep learning, and satellite data in land cover classification, crop productivity prediction, and the development of smart irrigation systems. Overall, algorithms such as Random Forest, Convolutional Neural Networks (CNN), as well as the combined use of temporal and spectral features from Sentinel-2 imagery show significant improvements in plant classification accuracy and agricultural yield modeling. Meanwhile, the application of IoT through environmental sensors and plant image processing based on computer vision also offers efficient solutions for irrigation automation and water management. These results indicate that the integration of data-driven approaches and cross-disciplinary technologies can enhance resilience. food and production efficiency in modern agricultural systems.