Machine Vision Herbicide Application for Weed-Type Selectivity
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Solution Overview
Problem
Existing agricultural practices uniformly apply herbicides across entire fields, which can be ineffective against different weed types, lead to chemical resistance, inefficiency, and excessive environmental impact.
Innovation Solution
A machine learning model analyzes images of agricultural fields to identify specific weed parameters, allowing for targeted application of herbicides using spot treatments for identified weeds and broad treatments for unidentified weeds, improving accuracy and reducing chemical use.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If standard spray booms are used to broadly apply herbicide to entire agricultural field, then coverage is complete, but effectiveness against different weed types decreases and chemical resistance increases
Solution Approach 1:
The patent segments the agricultural field into multiple treatment zones based on weed type detection. Different herbicides are applied to different segments of the field depending on the detected weed parameters, replacing the uniform broad-spectrum approach with targeted segment-specific treatments.
Solution Approach 2:
The system applies local quality by selecting and applying specific herbicides to specific locations in the field based on detected weed types. Each location receives the appropriate herbicide formulation matched to its weed composition, rather than applying the same herbicide uniformly across the entire field.
2Productivity
If broad herbicide application is used to treat entire field, then coverage is complete, but chemical use efficiency decreases and environmental impact increases
Solution Approach 1:
The patent extracts only the necessary herbicide components needed for each specific weed type detected in each field zone. By identifying and isolating the specific herbicide requirements for different weed parameters, the system eliminates unnecessary chemical applications and reduces overall chemical waste.
Solution Approach 2:
Instead of applying full-strength broad-spectrum herbicide to the entire field, the system applies partial treatments - only the specific herbicides needed for detected weed types - to specific zones. This partial action approach reduces chemical consumption while maintaining effectiveness.
3Measurement precision
If machine learning model analyzes images to identify specific weed parameters, then treatment accuracy improves, but system complexity increases
Solution Approach 1:
The patent introduces an intermediary machine learning model that mediates between the imaging system and the herbicide application system. The ML model processes images, identifies weed parameters, and translates this information into specific herbicide selection decisions, simplifying the overall system architecture while improving accuracy.
Solution Approach 2:
The system uses digital copies of field images analyzed by the machine learning model to make treatment decisions, rather than requiring direct physical analysis of each weed type. This copying approach allows for accurate weed parameter identification without complex real-time physical measurement systems.
Data Source
AI summary
There is provided a system for customized application of herbicides, comprising: a processor(s) executing a code for: feeding test images corresponding to a target agricultural field into a machine learning model trained on a training dataset of sample images of sample agricultural field(s) labelled with ground truth of weed parameters, selecting specific weed parameter(s) of according to performance metric(s) of the model, setting up instructions for triggering application of a first herbicide to a portion of the target agricultural field in response to an outcome of the model indicating likelihood of the specific weed parameter(s) being depicted in an input image of the portion of the target agricultural field, and setting up instructions for triggering application of a second herbicide to the portion of the target agricultural field in response to the outcome of the model indicating non-likelihood of the specific weed parameter(s) being depicted in the input image.