Targeting agricultural objects to apply units of treatment autonomously

This invention describes an autonomous system designed to identify specific plants, weeds, or pests in a field and apply a targeted treatment to them. It uses cameras to capture images of agricultural objects, then advanced computer vision software analyzes these images to pinpoint individual objects by identifying their unique pixel patterns. Once an object is identified and tracked, the system correlates it with a specific action, like spraying a fertilizer or pesticide, and activates a precise emitter to deliver the treatment.

Why it matters: Deep learning models for object detection and tracking have significantly matured since 2019, making the core computer vision and identification tasks more accurate and deployable on edge devices for real-time agricultural applications. This advancement directly addresses the patent's focus on identifying pixel subsets and correlating objects to actions.

Status: Not in forceListed as no longer active. The specific reason is not in the record we hold.
How hard to build: SpecializedHeavy software system, robotics, precision emitter, image sensors

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