Visual data are becoming ubiquitous in agriculture, driven by the widespread availability of low-cost imaging systems, drones, and satellite observations. This rapid expansion of visual data creates unprecedented opportunities to automate animal and plant phenotyping, monitor production systems over time, and extract actionable insights at scales that were previously unattainable. In this talk, we present a series of applied research projects in which visual artificial intelligence has been successfully deployed to address concrete challenges in agricultural systems. These examples illustrate how computer vision and deep learning enable non-invasive monitoring, improved decision support, and increased efficiency across livestock and crop production. Beyond current successes, we also discuss the key barriers that still limit large-scale adoption, including data availability, model robustness in real-world environments, and integration into operational workflows. Finally, we outline how closer collaboration between research institutions, technology providers, and industry stakeholders (particularly SMEs) can help close these gaps and accelerate the practical deployment of visual AI in agriculture.