Computer vision and image segmentation algorithms in agricultural robotics applications

Agriculture is a cornerstone of global food security, yet it faces significant challenges in the modern era, including labor shortages, environmental pressures, and the need for sustainable practices. Traditional farming methods are often labor-intensive and inefficient, leading to increased costs and reduced productivity. To address these challenges, the integration of advanced technologies into agricultural practices has become essential. Among these technologies, computer vision has emerged as a powerful tool, offering precise and automated solutions that can significantly enhance farming efficiency and sustainability. This paper presents a comprehensive review of computer vision technologies and their algorithmic applications in agricultural robotics, highlighting their transformative role in modernizing traditional farming practices. The study underscores computer vision as a pivotal driver for scalable, efficient, and sustainable agriculture, with future potential in resource optimization and food security. Proposals for future computer vision-enabled agricultural robots in the fields of picking and weeding, crop identification, path planning, pest detection, seed screening, etc.