Combine Field Mapping Using Collapsed Grain Culm Detection
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Solution Overview
Problem
Existing combines fail to utilize collapsed grain culm detection information for future farming plans, limiting the effectiveness of harvesting and farming strategies.
Innovation Solution
A combine equipped with an image recognition module to detect collapsed grain culms, generating position information and produce evaluation values, which are used to create a field farming map aligning culm distribution with yield and taste values, enabling informed farming decisions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If collapsed grain culm detection is performed during harvesting, then harvesting work control can be adjusted, but the detection information cannot be utilized for future farming plans
Solution Approach 1:
The system performs preliminary detection of collapsed grain culms during harvesting and stores the spatial distribution information in advance. This allows the information to be reused for future farming plan creation without requiring additional detection operations, thus preventing information loss while maintaining harvesting efficiency.
Solution Approach 2:
The system creates a digital copy (spatial distribution map) of the collapsed grain culm detection information during harvesting. This copied information can then be stored and reused for multiple farming plans without losing the original detection data, enabling both harvesting control and future agricultural decision-making.
2Adaptability or versatility
If spatial distribution mapping of collapsed grain culms is implemented, then farming plan optimization becomes possible, but system complexity increases
Solution Approach 1:
The spatial distribution mapping system serves multiple functions: it enables harvesting work control, supports creation of various farming plans (fertilizer application, planting density adjustment), and provides historical data for analysis. This multi-functionality justifies the system complexity by delivering diverse agricultural benefits from a single information processing framework.
Solution Approach 2:
The system introduces a spatial distribution map as an intermediary data structure that bridges harvesting detection information and farming plan creation. This intermediary layer simplifies the complexity by providing a standardized format that can be easily processed for different farming scenarios without requiring complex direct transformations between detection data and various plan types.
3Measurement precision
If detailed produce evaluation per unit traveling is performed, then yield and taste value distribution can be confirmed, but measurement and processing complexity increases
Solution Approach 1:
The system divides the field into discrete measurement segments corresponding to unit traveling distances of the combine. By evaluating produce quality in these segmented units rather than as a whole, the system achieves precise spatial distribution mapping of yield and taste values while making the measurement process manageable through systematic breakdown.
Solution Approach 2:
The produce evaluation is performed continuously during harvesting operations at unit traveling intervals. This continuous measurement approach maintains high precision by capturing spatial variations without interruption, while the automated nature of continuous measurement during normal harvesting reduces the practical difficulty compared to discrete sampling methods.
Data Source
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AI summary
Need exists for information management technique that allows utilization of collapsed grain culms detection for a future farming plan. A combine includes a machine body position calculation section 66 for calculating a machine body position comprising map coordinates of a machine body, an image capturing section 70 configured to image-capture a field at time of a harvesting work, an image recognition module 5 configured to input image data of captured images acquired by the image capturing section 70 and to estimate a collapsed grain culm area in the captured images and then to output recognition output data indicative of the estimated collapsed grain culm area, an evaluation module 4A configured to output a produce evaluation value per unit traveling acquired by evaluating the agricultural produces that are harvested sequentially, a collapsed grain culm position information generation section 51 configured to generate collapsed grain culm position information indicative of a position of the collapsed grain culm area on a map, based on the machine body position and the recognition output data and a harvest information generation section 4B configured to generate harvest information from the machine body position at the time of harvest of the agricultural produces and the produce evaluation value.