Plant Treatment Model Training Using Agricultural Image Interaction
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Solution Overview
Problem
Current plant treatment methods are labor-intensive and costly when applied individually to plants in a field, as they often require manual application and lack customization to user preferences, leading to inefficient resource usage and weed control.
Innovation Solution
A method for selecting and training plant treatment models based on user preferences, using images of plants to generate customized treatment plans that allow farming machines to identify and treat specific plants according to user-defined actions, optimizing resource usage and weed control.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If manual treatment application is used for individual plants, then treatment precision is improved, but labor intensity and cost increase
Solution Approach 1:
The patent replaces manual mechanical treatment application with an automated system that uses image processing and machine learning models to identify plants and a robotic mechanism to apply treatment. The system captures images of plants, processes them through trained models to identify target plants, and automatically applies treatment without manual intervention, thus maintaining precision while dramatically improving productivity
Solution Approach 2:
The system enables the farming machine to autonomously perform treatment operations by integrating image capture, plant identification through machine learning models, and automated treatment application. The machine serves itself by making independent decisions about which plants to treat based on real-time image analysis, eliminating the need for human operators to manually identify and treat each plant
2Ease of operation
If uniform treatment is applied to all plants, then operation simplicity is improved, but adaptability to user preferences deteriorates
Solution Approach 1:
The system dynamically adapts treatment operations to user preferences by allowing configuration of multiple plant treatment models with different parameters. Users can adjust model parameters such as plant size thresholds, treatment thresholds, and resource allocation settings. The system flexibly switches between different models and adjusts their parameters in real-time based on field conditions and user preferences, making the operation adaptable without sacrificing simplicity
Solution Approach 2:
The patent implements adaptability through parameter configuration of plant treatment models. Users can modify parameters such as minimum plant size for treatment, treatment intensity, and resource allocation ratios. The system processes these parameter changes and adjusts its treatment decisions accordingly, enabling customized treatment strategies for different scenarios while maintaining ease of operation through a unified interface
3Adaptability or versatility
If multiple plant treatment models are trained with different parameters, then adaptability is improved, but model selection complexity increases
Solution Approach 1:
The system incorporates feedback mechanisms that automatically evaluate the performance of different plant treatment models based on field conditions and treatment outcomes. The feedback loop monitors treatment effectiveness, resource consumption, and model accuracy, then uses this information to automatically select or adjust the appropriate model. This feedback-driven approach eliminates the need for manual model selection, reducing complexity while preserving the benefits of multiple specialized models
4Reliability
If treatment resources are allocated to all identified plants, then weed control effectiveness is improved, but resource consumption increases
Solution Approach 1:
The system applies local quality by differentiating treatment allocation based on local field conditions and plant characteristics. Instead of uniform treatment allocation, the system analyzes each plant's identity, size, location, and threat level to determine appropriate treatment. High-priority plants receive treatment resources while low-priority plants may be monitored or treated with reduced resources, achieving effective weed control with optimized resource consumption
Data Source
AI summary
Embodiments relate to training a plant treatment model. A control system may provide images of plants for display to a user. The control system may receive one or more plant treatment action preferences of the user. The one or more plant treatment action preferences include labels for the images that identify plants in the images and treatment actions to be applied to the identified plants. The control system may train the plant treatment model based on the one or more plant treatment action preferences. The trained plant treatment model is configured to, when applied to an image of plants in a field, determine treatment actions to be applied to the plants in accordance with the one or more plant treatment action preferences of the user. The control system may configure a farming machine to operate based on the trained plant treatment model.