AI Harvester Control for Yield and Impurity Reduction
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Solution Overview
Problem
Existing plant harvester machines introduce impurities and fail to maximize yield due to issues like chaffing, breakage, and sub-optimal threshing processes, leading to additional post-harvest costs and loss of usable plants.
Innovation Solution
The implementation of machine learning techniques for real-time impurity detection and yield optimization using cameras and controllers to adjust harvester settings, such as cutter height and speed, to minimize impurities and improve yield.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If the cutter moves quickly to increase harvesting speed, then productivity is improved, but plant breakage increases introducing impurities
Solution Approach 1:
The system dynamically adjusts the cutter speed based on real-time detection of plant conditions and impurity levels. The controller modulates the cutter motor to vary speed, transitioning from high speed when plants are healthy to reduced speed when breakage is detected, resolving the contradiction between maintaining high productivity and preventing plant breakage.
Solution Approach 2:
The system implements a feedback loop where cameras detect plant conditions and impurities in real-time, the controller processes this information, and automatically adjusts cutter speed accordingly. This closed-loop control enables the system to respond to changing conditions, maintaining optimal speed to prevent breakage while maximizing harvesting efficiency.
2Productivity
If the thresher moves quickly to increase processing speed, then productivity is improved, but de-stemming effectiveness decreases introducing stems as impurities
Solution Approach 1:
The thresher speed is dynamically adjusted based on real-time feedback from impurity detection. When stems are detected in the harvested material, the controller automatically reduces thresher speed to improve de-stemming effectiveness. This dynamic control resolves the contradiction by adapting processing speed to maintain both productivity and separation quality.
Solution Approach 2:
The system uses a feedback mechanism where cameras monitor the harvested material for stem impurities, and the controller responds by adjusting thresher speed. This real-time feedback loop enables the system to maintain optimal processing speed that ensures effective de-stemming while preserving overall productivity.
3Object-generated harmful factors
If manual detection and removal of impurities is performed after harvesting, then impurity removal is achieved, but post-harvest costs increase and usable plants are lost
Solution Approach 1:
The system performs preliminary detection and prevention of impurities during the harvesting process itself, rather than after harvesting is complete. By detecting plants at risk of breakage and adjusting cutter speed in advance, the system prevents impurity formation, eliminating the need for costly post-harvest processing and preserving usable plants.
Solution Approach 2:
The system replaces manual post-harvest inspection and sorting with an automated optical detection system using cameras and image processing. This substitution of mechanical/manual processes with automated sensing and control eliminates labor-intensive post-harvest operations, reducing costs and preventing loss of usable plants through early detection and prevention.
Data Source
AI summary
Systems and methods are disclosed herein for detecting impurities of harvested plants in a receptacle of a harvester. In an embodiment, a harvester controller receives, from a camera facing the contents of the receptacle, an image of the contents. The harvester controller applies the image as input to a machine learning model. The harvester controller receives, as output from the machine learning model, an identification of an impurity of the harvested plants. The harvester controller transmits a control signal based on the impurity.