Treatment Action Detection for Real-Time Agricultural Image Annotation
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Solution Overview
Problem
Current agricultural technologies face challenges in efficiently identifying and managing undesirable vegetation in fields, as existing methods require extensive data for machine learning model training, which is impractical due to the vast amount of data generated daily, and often result in inaccurate object detection, leading to improper treatment or missed tasks.
Innovation Solution
A computer-implemented method using a processor onboard a vehicle for machine learning processing of sensor inputs, which includes annotating agricultural images using multiple machine learning models, providing user feedback for real-time training, and selectively reducing data for training to improve accuracy and efficiency in identifying agricultural objects and their growth stages.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If extensive data is used for machine learning model training, then model accuracy is improved, but data processing time and computational complexity increase
Solution Approach 1:
The patent segments the training data by using multiple specialized machine learning models, each trained on specific subsets of agricultural data (e.g., different crop types, growth stages, or object categories). This segmentation allows the system to process and train on manageable portions of data while maintaining high detection accuracy across diverse agricultural scenarios.
Solution Approach 2:
The system applies partial action by selectively processing only the most relevant data subsets for each specific detection task rather than processing all available agricultural data uniformly. This approach reduces computational overhead while maintaining accuracy by focusing processing resources on critical data portions.
2Measurement precision
If multiple machine learning models are used for annotation, then detection accuracy is improved, but device complexity increases
Solution Approach 1:
The patent merges multiple machine learning models into a unified annotation system where models work collaboratively. The system combines predictions from multiple models and resolves conflicts through predefined rules or consensus mechanisms, achieving high annotation accuracy while managing system complexity through integrated architecture.
Solution Approach 2:
An intermediary layer is introduced that manages the coordination between multiple machine learning models. This intermediary handles model output aggregation, conflict resolution, and annotation finalization, simplifying the overall system architecture while maintaining the benefits of multiple specialized models.
3Adaptability or versatility
If real-time training is implemented, then model adaptability is improved, but computational resource consumption increases
Solution Approach 1:
The system implements periodic training cycles rather than continuous real-time training. Models are trained at intervals using newly acquired data, balancing adaptability with resource conservation. This periodic approach allows the system to adapt to changing agricultural conditions while managing computational energy consumption through scheduled rather than constant training operations.
4Productivity
If selective data reduction is applied, then processing efficiency is improved, but information loss may occur
Solution Approach 1:
The system applies local quality by selectively processing data based on its relevance and importance to specific detection tasks. Different data subsets are processed with different levels of detail and priority, ensuring that critical information is retained while reducing processing of less important data, thus maintaining information quality while improving efficiency.
Data Source
AI summary
A computer-implemented method of sensor input processing, implemented by an agricultural platform comprising a processor and a sensor, includes capturing, using the sensor, sensor images of a vicinity of a target object of a time interval during which a treatment is applied to the target object; processing the sensor images using one or more machine learning (ML) algorithms wherein at least one ML algorithm uses an ML model trained to detect a presence of a treatment action in the vicinity of the target object; and providing, selectively based on a result of detecting the presence of the treatment action in the vicinity of the target object, an outcome of the processing for further processing.