Harvester Crop Stream Imaging for Georeferenced Impurity Mapping
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Solution Overview
Problem
Existing self-propelled harvesting machines lack the ability to effectively analyze and map the proportion of impurities in harvested crops in real-time, which affects the quality and efficiency of the harvesting process.
Innovation Solution
A self-propelled harvesting machine equipped with a camera system and an image evaluation device using machine learning algorithms, specifically EfficientNet-based neural networks, to analyze crop material streams for impurities, and a mapping unit to georeference the contamination data for subsequent field machines.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional harvesting machines are used without advanced image analysis systems, then the device complexity is low, but the measurement precision of impurity proportion and the productivity are reduced
Solution Approach 1:
The patent replaces manual visual inspection and traditional mechanical sorting methods with an automated optical image analysis system. The camera system captures images of the crop stream, and machine learning algorithms automatically identify and quantify impurities, substituting human labor and mechanical sorting with intelligent automated detection.
Solution Approach 2:
The patent introduces an image evaluation device as an intermediary between the harvesting machine and the final product quality assessment. This device acts as a mediator that captures visual data of the crop stream and provides quantitative impurity information to control systems, enabling automated adjustments without direct human intervention.
2Productivity
If real-time image analysis with machine learning algorithms is implemented, then the measurement precision and productivity are improved, but the use of energy and device complexity increase
Solution Approach 1:
The system performs image analysis at periodic intervals during the harvesting process rather than continuously analyzing every single crop element. The camera system captures images at regular frames, and the machine learning model processes these periodic inputs to provide timely impurity assessments while reducing computational energy consumption compared to continuous analysis.
Solution Approach 2:
The system focuses image analysis resources on detecting and analyzing only the impurity portions of the crop stream rather than processing every crop element in equal detail. The machine learning model identifies regions of interest containing impurities and concentrates computational effort on those areas, reducing overall energy consumption while maintaining detection accuracy.
3Loss of information
If georeferenced mapping of impurities is implemented, then the loss of information is reduced for subsequent field machines, but the device complexity and data processing requirements increase
Solution Approach 1:
The mapping system serves multiple functions: it records impurity locations, stores georeferenced data for future reference, provides real-time feedback to the harvesting machine, and creates actionable maps for subsequent field operations. This multi-functional approach consolidates various data management tasks into a single integrated system, justifying the added complexity through enhanced information utility.
Solution Approach 2:
The system performs georeferenced mapping and contamination documentation during the harvesting process itself, preparing and organizing data in advance before subsequent field machines arrive. This preliminary action ensures that when follow-up operations occur, all necessary contamination information is already available and properly formatted, eliminating the need for later data collection and reducing information loss.
Data Source
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AI summary
The present invention relates to a self-propelled harvesting machine (1), wherein the harvesting machine (1) comprises a header (4) for receiving crop (2), working units (20) for processing the received crop (2), a transfer device (15) for discharging the processed crop (2), a camera system (16) for capturing images (41) of a crop stream (21) passing through the harvesting machine (1), and an image evaluation device (27) for evaluating the images (41), wherein the image evaluation device (27) is configured to analyze the images (41) of the crop stream (21) for a proportion of impurities contained in the crop stream (21) that was captured by the header (4) when the crop (2) was received, and wherein the image evaluation device (27) is configured to georeference the determined proportion of detected impurities using position data (58).which provides a position tracking sensor (57) of the harvesting machine (1) for the continuous determination of a geographical position on a field (60) to be cultivated.