20240008389. SYSTEMS, METHODS AND DEVICES FOR USING MACHINE LEARNING TO OPTIMIZE CROP RESIDUE MANAGEMENT simplified abstract (Groundtruth Ag, Inc.)

SYSTEMS, METHODS AND DEVICES FOR USING MACHINE LEARNING TO OPTIMIZE CROP RESIDUE MANAGEMENT

Organization Name

Groundtruth Ag, Inc.

Inventor(s)

John Richard Anderson, Jr. of Raleigh NC (US)

Graham Hunter Bowers of Raleigh NC (US)

Clay Honeycutt of Roseboro NC (US)

Lars Dyrud of Great Falls VA (US)

SYSTEMS, METHODS AND DEVICES FOR USING MACHINE LEARNING TO OPTIMIZE CROP RESIDUE MANAGEMENT - A simplified explanation of the abstract

This abstract first appeared for US patent application 20240008389 titled 'SYSTEMS, METHODS AND DEVICES FOR USING MACHINE LEARNING TO OPTIMIZE CROP RESIDUE MANAGEMENT

Simplified Explanation

The patent application describes systems, methods, and devices for using machine learning to optimize crop residue management. The methods involve receiving crop residue data from multiple sensors that measure the surface of a soil area, as well as receiving geographic location data corresponding to the crop residue data from a location sensor. The processing circuit then generates multizone tillage data based on the crop residue data, which corresponds to a plurality of zones defined in the soil area.

• The patent application proposes using machine learning to optimize crop residue management.
• Multiple sensors are used to collect crop residue data from the surface of a soil area.
• A location sensor provides geographic location data corresponding to the crop residue data.
• The processing circuit generates multizone tillage data based on the crop residue data.
• The multizone tillage data corresponds to a plurality of zones defined in the soil area.

Potential Applications:

• Precision agriculture: The technology can be used to optimize crop residue management in precision agriculture systems, allowing farmers to make more informed decisions about tillage practices.
• Soil health management: By analyzing crop residue data and generating multizone tillage data, the technology can help farmers improve soil health and reduce erosion.
• Resource optimization: Optimizing crop residue management can help farmers maximize the use of resources such as water and fertilizer, leading to more sustainable agricultural practices.

Problems Solved:

• Inefficient tillage practices: The technology addresses the problem of inefficient tillage practices by using machine learning to analyze crop residue data and generate multizone tillage recommendations.
• Soil erosion: By optimizing crop residue management, the technology helps reduce soil erosion, which is a significant problem in agriculture.
• Resource wastage: The technology helps farmers optimize the use of resources such as water and fertilizer, reducing wastage and promoting sustainable farming practices.

Benefits:

• Increased crop yield: By optimizing crop residue management, farmers can improve soil health and nutrient availability, leading to increased crop yield.
• Reduced soil erosion: The technology helps minimize soil erosion by providing recommendations for tillage practices that preserve soil structure and reduce runoff.
• Resource efficiency: Optimizing crop residue management allows farmers to make more efficient use of resources such as water and fertilizer, reducing costs and environmental impact.

Original Abstract Submitted

systems, methods and devices for using machine learning to optimize crop residue management are provided. operations of such methods include receiving, using a processing circuit and from multiple of sensors, crop residue data of a surface of a soil area, receiving, into the processing circuit and from a location sensor, geographic location data that corresponds to the crop residue data and generating multizone tillage data that is based on the crop residue data and that corresponds to a plurality of zones that are defined in the soil area.