Over the past six decades, significant advancements in computer vision, a key aspect of Artificial Intelligence (AI), have revolutionized fields such as robotics and environmental monitoring. Initially inspired by neurophysiological discoveries in 1959, computer vision now enables machines to interpret and extract information from digital images and videos. In robotics, it facilitates autonomous navigation, object recognition, and intelligent interaction with surroundings. In environmental monitoring, it supports ecological studies and climate research through UAVs and agricultural autonomous robots (AMRs). However, comprehensive and diverse datasets are essential for accurate analysis and training in computer vision. This thesis explores the synergy between computer vision, robotics, and environmental monitoring, leveraging the integration of visual perception technologies with robotic systems to address challenges and drive innovation in these interconnected fields.