Machine Vision Weed Detection for Selective Herbicide Spraying
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Solution Overview
Problem
Existing agricultural practices uniformly apply herbicides across entire fields, which can be inefficient, lead to chemical resistance in weeds, and result in excessive environmental impact, without effectively targeting specific weed types or growth stages.
Innovation Solution
A machine learning model is trained on agricultural field images to identify specific weed parameters, allowing for customized application of herbicides based on the likelihood of weed presence, using spot treatments for identified weeds and broad treatments for undetected areas.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Area of stationary object
If standard spray booms are used to broadly apply herbicide to entire agricultural field, then complete field coverage is achieved, but herbicide efficiency decreases and environmental impact increases
Solution Approach 1:
The patent divides the agricultural field into multiple zones based on machine vision analysis of weed presence. Instead of uniform treatment, the system segments the field into treated and untreated areas, applying herbicide only where weeds are detected. This segmentation resolves the contradiction by maintaining complete field coverage awareness while reducing herbicide application to only necessary areas, improving efficiency and reducing environmental impact.
Solution Approach 2:
The patent implements local quality by varying the herbicide application approach across different local areas of the field. Based on machine vision detection, the system applies spot treatments to specific locations where weeds are present, while leaving other areas untreated. This local differentiation maintains field coverage awareness while improving herbicide efficiency through targeted application only where needed.
2Adaptability or versatility
If broad herbicide application is used, then all weed types are covered, but chemical resistance develops in weeds
Solution Approach 1:
The patent segments the weed population into different types and growth stages through machine vision analysis. By identifying specific weed species and their developmental stages, the system can select and apply appropriate herbicides targeted to each segment rather than using a single broad-spectrum herbicide on the entire field. This segmentation approach maintains versatility in covering different weed types while improving chemical effectiveness by avoiding resistance through targeted, varied treatment strategies.
3Ease of operation
If uniform herbicide application is applied, then application process is simple, but environmental impact increases
Solution Approach 1:
The patent applies preliminary action by using machine vision systems to scan and analyze the agricultural field before herbicide application. The system pre-identifies weed locations, types, and growth stages, then uses this information to plan targeted herbicide application. This preliminary detection and planning phase maintains operational simplicity by automating the decision-making process while significantly reducing environmental impact through precise, targeted application only where weeds are present.
4Loss of substance
If spot treatment is used for identified weeds, then herbicide efficiency improves, but treatment precision requirements increase
Solution Approach 1:
The patent introduces machine vision technology as an intermediary between the herbicide application system and the field conditions. This intermediary component (the vision system) provides precise identification and location data of weeds, which then guides the application system. By using this intermediary, the system achieves high herbicide efficiency through spot treatment while managing precision requirements through automated detection and guidance rather than requiring manual precision from operators.
Data Source
AI summary
There is provided a method for customized application of herbicides, comprising, in 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.