Sub-Field Weed Spot Spraying for Fast ML-Based Nozzle Control
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Solution Overview
Problem
Existing automated weed detection and spot spraying systems face challenges in accurately identifying various types and sizes of weeds under diverse operating environments, requiring extensive training data and ensuring timely activation of herbicide application without system delays.
Innovation Solution
A machine learning-based system that processes sub-field images from a camera's field of view, using a neural network to classify and activate spray nozzles based on weed presence, with continuous training and farmer feedback to improve accuracy and responsiveness.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If complex graphical image pattern recognition is used to detect weeds, then weed detection capability is improved, but processing time increases and system complexity increases
Solution Approach 1:
The patent divides the full field of view image into multiple sub-field images, each corresponding to a specific spray nozzle's coverage area. This segmentation allows the machine learning model to process smaller, more manageable image regions in parallel, reducing overall processing time while maintaining detection accuracy for weeds within each sub-field
Solution Approach 2:
The system processes only the necessary sub-field images corresponding to active spray nozzles rather than analyzing the entire field of view. This partial processing approach reduces computational load and processing time while still achieving comprehensive weed detection coverage across the spray area
2Adaptability or versatility
If large datasets are used to train machine learning models for weed detection, then detection accuracy across diverse conditions is improved, but data processing and training time increases
Solution Approach 1:
The training process is segmented by creating specialized machine learning models for different environmental conditions (wet/dry ground, light/dark soil, different times of day). Each model is trained on specific subsets of the large dataset corresponding to particular conditions, allowing parallel training and faster deployment while maintaining high accuracy across all environments
Solution Approach 2:
The system changes environmental parameters in the training data by collecting and processing images under various conditions (different lighting, moisture levels, soil types). This parameter-based approach allows the creation of multiple specialized models that can be selected based on current field conditions, improving adaptability without requiring all models to process all data
3Measurement precision
If image processing is slowed to ensure accurate weed identification, then classification accuracy is improved, but productivity decreases as the implement must slow down
Solution Approach 1:
The field of view is segmented into multiple sub-fields, and the machine learning model processes these smaller regions in parallel rather than analyzing one large image sequentially. This segmentation enables near-instantaneous processing that maintains high identification accuracy while allowing the implement to operate at full speed without slowing down
Solution Approach 2:
The system performs preliminary actions by pre-processing images to extract only the relevant sub-field regions before classification. This preliminary extraction of critical information reduces the computational burden during real-time operation, enabling fast processing that maintains productivity while ensuring accurate weed identification
Data Source
AI summary
A weed spot-spraying system is described for carrying out a spot-based weed spraying method based upon a classification value rendered from a sub-field image in accordance with a machine learning-based trained model applied by a processor to the sub-field image. The system includes a camera; a spray nozzle assembly including a spray nozzle; and a processor. The method carried out by the system includes acquiring, by the camera, a full field of view image of a crop floor. The method further includes extracting, from the full field of view image, a sub-field image corresponding to the spray nozzle positioned to provide a spray field extending over a part of the crop floor depicted in the sub-field image; rendering, by the processor in accordance with the machine learning-based trained model, a classification for the sub-field image; and selectively activating the spray nozzle in accordance with the classification for the sub-field image.