Multi-Modal Computer Vision and Foundation Models In Agriculture in conjunction with IEEE CVPR 2025

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.