The Ciampitti Lab got its hands on an Apple Vision Pro thanks to Apple and Purdue University. I've been exploring how spatial computing can assist with digital plant phenotyping and data collection in the field. So far I got static object detection running on-device using Core ML with my YOLO models. Live camera access is currently enterprise-only on visionOS, so we're waiting on the entitlement. Check out the full write-up here.

AgriTiles is a multi-scale 3D visualization system built with Three.js for exploring agricultural landscapes through multiple geospatial data layers. You can inspect crop patterns and their relationship to terrain across the U.S. Corn Belt (Iowa, Illinois, Indiana, Nebraska, Missouri, Kansas) with USDA CDL, SRTM terrain, and Sentinel-2 imagery all in a single browser-based WebGL environment. Give it a try here.

This project was built for the Precision & Digital Agriculture Hackathon at UIUC alongside Gustavo Santiago, Leonardo Bosche, and Natalia Volpato. We built N Scout, a tool that detects nitrogen deficiency in corn fields by combining Sentinel-2 multispectral imagery (10m resolution, V6-V10 growth stages), soil nitrate and weather records for agronomic context, and a leaf-image neural classifier for rapid in-field confirmation at the plant level. On average, 50% of applied N is lost to the environment before ever reaching the plant. The goal is simple: apply the right amount, in the right place, at the right time. Give it a try here.

The Corn Depth project is a follow up from the Corn Grains project, and it got published at CVPR 2025. Crazy right? We introduce the first fully on-field pipeline that estimates maize-ear length, width and volume from a single RGB + depth capture and immediately forecasts grain yield per plant. A YOLOv12n-seg model isolates the ear in unconstrained lighting, a bespoke network we called EVNet regresses volume from the segmented point cloud, and gradient-boosted trees convert morphology into yield. On Kansas field data we reached 98.6% mAP@0.5 for segmentation, 28.9 ml RMSE for volume, and 13.9 g RMSE for yield. The whole pipeline runs in ~1s per image, needs no destructive sampling, and the images, code, and trained weights are all open-sourced. Check out the project page here.

AVYield is a web-based dashboard that allows users to visualize the results of crop variety trials programs. It also serves as a historical database to help organize all this information. The system is capable of handling and storing a large amount of data. Give it a try by clicking here and feel free to provide any feedback.

This project consists of two parts: the first is to develop a high-throughput image/video analysis system to determine the quantity of grains in a rotating corn cob. The hardware responsible for rotating the cob was provided by a third-party company. The second part aims to achieve the same results using just a regular smartphone. I'll update this topic with more information as soon as I can make it publicly available.

The Sorghum Architecture project aims to facilitate data collection by utilizing image processing and machine learning on images taken from the field. The results were really good, and the developed system is much faster than conventional methods. I'll update this soon with more information. For now, zoom in and take a look at the poster to know more.