{"type":"video","version":"1.0","html":"<iframe src=\"https://www.loom.com/embed/8cacc08516364edca28194267cb3f57e\" frameborder=\"0\" width=\"1920\" height=\"1440\" webkitallowfullscreen mozallowfullscreen allowfullscreen></iframe>","height":1440,"width":1920,"provider_name":"Loom","provider_url":"https://www.loom.com","thumbnail_height":1440,"thumbnail_width":1920,"thumbnail_url":"https://cdn.loom.com/sessions/thumbnails/8cacc08516364edca28194267cb3f57e-1717525199851.gif","duration":234.482,"title":"Harvest.AI","description":"In this video, I provide an overview of the project we worked on for the on-demand annotated hackathon using the AIRF on-demand plugins. The project focuses on a crop suggestion system based on geographical locations and factors that affect crop growth, such as weather, soil, heat stress, and water quality. I explain how the plugins work and demonstrate how they provide crop recommendations based on the selected location. Action requested: Watch the video to understand the project and ask any questions."}