ML-Guided Vegetation Detection for Selective Crop Treatment
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Solution Overview
Problem
Current agricultural technologies face challenges in efficiently managing and optimizing crop growth and weed control, particularly in terms of land, chemical, time, and labor costs, with existing methods being incremental and not fully addressing the increasing demand for food production.
Innovation Solution
A machine learning-based system mounted on agricultural vehicles that uses image processing and sensors to detect and control undesirable vegetation by identifying agricultural targets, performing pose estimation, and activating treatment mechanisms for precise application of treatments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional agricultural methods are used for weed control and crop management, then labor and chemical usage are reduced, but productivity and precision are insufficient to meet increasing food demand
Solution Approach 1:
The patent replaces manual mechanical weed control and crop management with an automated system combining computer vision, machine learning algorithms, and robotic treatment mechanisms. The system captures images, processes them through ML models to identify targets, and applies treatments automatically, eliminating the need for manual labor while significantly increasing productivity and precision in agricultural operations.
2Productivity
If incremental improvements to existing agricultural techniques are implemented, then some efficiency gains are achieved, but land and resource utilization remain suboptimal
Solution Approach 1:
The patent implements site-specific treatment by identifying individual weed and crop locations through image processing and machine learning. The treatment mechanism applies chemicals or physical treatments only to specific targeted plants rather than blanket treatment of entire fields, optimizing land and chemical usage by treating only where necessary while maintaining high crop production efficiency.
Solution Approach 2:
The system applies treatments selectively to only those plants that require intervention based on real-time identification, avoiding excessive treatment of entire areas. This partial action approach reduces overall chemical usage while maintaining effectiveness in controlling weeds and managing crops where needed.
3Manufacturing precision
If automated treatment systems are deployed, then precision and productivity improve, but device complexity increases
Solution Approach 1:
The patent integrates multiple functions into a single automated platform: image capture, machine learning-based identification, pose estimation, tracking, and treatment application. This multi-functional system achieves high treatment precision while managing complexity by combining what would otherwise be separate systems into one integrated agricultural treatment platform.
Data Source
AI summary
A method includes obtaining, by the treatment system configured to implement a machine learning (ML) algorithm, one or more images of a region of an agricultural environment near the treatment system, wherein the one or more images are captured from the region of a real-world where agricultural target objects are expected to be present, determining one or more parameters for use with the ML algorithm, wherein at least one of the one or more parameters is based on one or more ML models related to identification of an agricultural object, determining a real-world target in the one or more images using the ML algorithm, wherein the ML algorithm is at least partly implemented using the one or more processors of the treatment system, and applying a treatment to the target by selectively activating the treatment mechanism based on a result of the determining the target.