With the recent success of computer vision and deep learning in various applications, there has been significantly increasing attention paid to its use in agriculture. Agriculture-related vision problems are of great economic and social value. For example, robotics has recently been reinvigorated with work on Vision-Language-Action models. Building on these successes, researchers are using multi-modal computer vision foundation models to make progress on agricultural tasks and topics. Some relevant examples include: 1) Agricultural models that leverage data from different remote sensing platforms; 2) Multi-temporal yield prediction models using unsupervised domain adaptation; 3) Multi-modal models for identifying pests and weeds. This tutorial will encourage research in ML, CV, and agriculture, featuring leading researchers discussing the evolution and trends in this field.