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ARS Home » Plains Area » Fargo, North Dakota » Edward T. Schafer Agricultural Research Center » Weed and Insect Biology Research » Research » Research Project #444557

Research Project: Fusion of Machine Learning and Electromagnetic Sensors for Real-Time Local Decisions in Agriculture

Location: Weed and Insect Biology Research

Project Number: 3060-21220-032-017-S
Project Type: Non-Assistance Cooperative Agreement

Start Date: Jul 1, 2023
End Date: Oct 30, 2024

The overall goal of this multi-year project is to bring together personnel from both USDA and NDSU to develop novel DC-to-Light electromagnetic sensors, unique field-ready platforms to bring sensors to the grower, and fusing them with Machine Learning for real-time decision making. Towards that goal, the project will focus on the following objectives: (1) Partner with USDA and various constituents to conduct fundamental research on new electromagnetic sensor technologies for agriculture; (2) Carry-out an extensive technology readiness level study of existing sensor technologies and apply them to agriculture; (3) Develop Machine Learning (ML) theory specific for edge computing, electromagnetic sensors and applications in agriculture; (4) Implement secure Information Technology (IT) platforms for this initiative; and (5) Align and deploy these new technologies in the field for grower needs.

During the course of the first year of the project, the objectives will focus on several crop varieties and insects; namely sugar beets, potatoes, the honeybee, sunflowers and cereal crops (oats, wheat and barley) and will have the following start-up approaches: (1) assess the immediate sensing needs for the aforementioned crop varieties and insects; (2) develop new and survey existing electromagnetic sensors & instrumentation circuitry to conduct the needed measurements, in which the sensors are not limited to imaging, LiDAR and microwave radar; (3) develop lab or greenhouse prototype systems that closely resemble the field-based sensing needs; and (4) deploy the sensor system in the field for real-time measurements and ML-based decisions.