Visual AI for Agriculture

Blue River Technology, a subsidiary of John Deere, uses computer vision and deep learning to build intelligent machines that help farmers grow more food more efficiently. By enabling robots to tell the difference between crops and weeds and then only spraying the weeds, these machines are revolutionizing agriculture’s approach to chemical usage. By outfitting tractors with perception sensors and autonomous driving capabilities, we are freeing farmers from tedious jobs like tillage so they can spend more time doing higher-value tasks. In this session we will share how we solve machine vision problems using deep learning, and some of the specific challenges we’ve addressed along the way (such as dust interference and the visual similarities between weeds and crops). We will deep dive into the tech stack, including on-premise compute, image augmentations, 8-bit quantization trade-offs and tips and tricks to improve model performance.

AI in agriculture is only as good as the data behind it, but messy datasets, poor annotations, and hidden biases slow progress. Join Paula for a dynamic session on semantic segmentation, where she will show how FiftyOne can transform dataset curation, annotation analysis, and model evaluation for agricultural AI. Using real-world coffee datasets from Colombia, we’ll dive into the segmentation of coffee fruits at different maturation stages, leveraging FiftyOne’s powerful tools, from uniqueness detection to similarity search and embedding visualizations. Whether interested in farm robotics, remote sensing, or plant phenotyping, this talk will give you actionable techniques to refine your datasets and supercharge your AI workflows.