Multi-Model Sensor Inference for Faster Agricultural Object Detection
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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 large datasets for machine learning model training, which are impractical and lead to errors in object detection, resulting in improper treatment or missed tasks due to the dynamic nature of agricultural environments.
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 object detection accuracy and efficiency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If large datasets are used for machine learning model training, then model accuracy may improve, but data processing time and computational complexity increase significantly
Solution Approach 1:
The patent extracts and processes only a subset of sensor data that is most relevant for training, rather than using entire large datasets. This selective extraction reduces processing time while maintaining training effectiveness by focusing on high-value data portions.
Solution Approach 2:
The training dataset is segmented into multiple smaller batches that are processed incrementally. The system divides large datasets into manageable chunks, allowing parallel processing and reducing the time burden of processing complete large datasets while still achieving comprehensive model training.
2Measurement precision
If multiple machine learning models are used for annotation, then detection accuracy improves, but computational complexity and processing time increase
Solution Approach 1:
The system applies multiple ML models selectively rather than universally to all data. By using ensemble methods only where needed and applying simpler models elsewhere, the system achieves improved accuracy in critical detection areas without the full computational burden of multiple models across all processing stages.
Solution Approach 2:
Different ML models are segmented and assigned to different processing stages or data types. Rather than running all models simultaneously on all data, the system divides the computational task across multiple specialized models operating in sequence or parallel on specific data subsets.
3Loss of information
If comprehensive sensor data is processed, then complete information is obtained, but processing efficiency decreases
Solution Approach 1:
The system extracts only the most informative and relevant features from sensor data for processing, discarding redundant information. This selective feature extraction maintains the essential information needed for accurate detection while significantly reducing the volume of data requiring computational processing.
Solution Approach 2:
Data preprocessing and filtering operations are performed in advance to identify and retain only high-value information before main processing occurs. By pre-processing data to extract key features and remove noise beforehand, the system ensures information completeness is maintained while subsequent processing operates on a reduced, more efficient dataset.
Data Source
AI summary
A computer-implemented method of sensor input processing, implemented by an agricultural platform comprising a processor and a sensor includes receiving sensor input from the sensor; processing the sensor input by multiple machine learning (ML) algorithms, each using a corresponding ML model for generating labels for objects identified in the sensor input; combining labels generated by each ML algorithm to generate a super-imposed labeled sensor input frame; comparing outputs of the ML algorithms to determine similarities or differences; and using results of the comparing for improving an operational characteristic of the sensor input processing.