Field Obstruction Detection Model for Real-Time Farming Machine Pathing
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Autonomous farming machines struggle to efficiently recognize a diverse range of obstructions in fields, including both common and uncommon obstacles, due to the time-consuming and labor-intensive process of data collection and labeling required for training obstruction detection models, which can lead to unsafe operating environments and equipment damage.
Innovation Solution
A farming machine equipped with sensors that capture image data, using a convolutional neural network trained on labeled images of obstructions with prescribed actions, to identify and navigate around obstacles, enhancing safety by modifying its path to avoid obstructions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional multiple models are used to detect obstructions, then detection coverage is improved, but data collection time and labeling effort increase significantly
Solution Approach 1:
The patent combines multiple obstruction detection models into a single unified model that can detect various types of obstructions (animals, people, equipment, rocks) simultaneously. This consolidation maintains comprehensive detection coverage while eliminating the need to collect and label separate datasets for each obstruction type, significantly reducing data collection and labeling time.
Solution Approach 2:
The unified obstruction detection model is designed to detect multiple categories of obstructions with a single model architecture. By making the model universal rather than specialized for each obstruction type, the system achieves broad detection coverage without requiring multiple separate training processes, thereby reducing overall data collection and labeling efforts.
2Reliability
If more obstruction types are detected, then safety is improved, but model training complexity increases
Solution Approach 1:
The patent merges multiple specialized detection models into one unified model that handles all obstruction types (animals, people, equipment, rocks) within a single training process. This approach improves safety by maintaining comprehensive detection coverage while reducing training complexity compared to managing and coordinating multiple separate models.
3Measurement precision
If manual labeling is performed for training data, then model accuracy is improved, but labor requirements increase
Solution Approach 1:
The unified model consolidates the training requirements for multiple obstruction types into a single labeling process. By combining all obstruction detection tasks into one model, the system achieves high identification accuracy while reducing the total labeling effort compared to creating and maintaining separate labeled datasets for each obstruction category.
Data Source
AI summary
A control system of a farming machine is configured to identify obstruction in a field from image data of the field. The control system accesses an obstruction model configured to identify obstructions in a field from image data of the field. The obstruction model is generated by accessing image data of obstructions in a training field, each obstruction corresponding to a prescribed action occurring at a prescribed time, labelling the image data of the obstructions, and training the obstruction model based on the labelled image data. The control system captures image data of the field including an obstruction and inputs the image data into the obstruction model. Responsive to identifying the obstruction in the field, the control system modifies treatment instructions of the farming machine such that the farming machine performs a implements a farming objective while avoiding the obstruction in the field.