OpenHarvest is a platform designed to manage farmers, fields, and crops to ensure the farmers succeed in making profits. Task timelines are given for each crop by a recommendation engine, then confirmed by a verification engine utilizing drones and ML models to allow farmers to collect blockchain based reputation tokens for financial institutions.
While they apply to different types of data, the pandas DataFrame and FiftyOne Dataset classes share many similar functionalities. In this overview, we’ll present a side-by-side comparison of common operations in the two libraries.
Are there implied molecular structures that can be inferred from low-cost {NIRS, Light field, [...]} sensor data?
Task: Learn a function f() such that f(lowcost_sensor_data) -> expensive_sensor_data
Possibly worth reviewing these open source tools for feature ideas, annotation UI workflows:
As well, NIRS Near Infrared Spectroscopy: