Dynamic Treatment Buffers for Uncertain Plant Targeting
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Solution Overview
Problem
Conventional farming machines face challenges in precisely and accurately applying treatment compounds to plants due to unreliable sensor data and varying terrain, leading to wasteful distribution and misapplication of treatments.
Innovation Solution
A farming machine dynamically adjusts a treatment buffer based on uncertainty measurements of its spatial relationship with plants, using image data and sensor signals to target plants with high fidelity, applying treatments to both the plant and a buffer zone.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If a farming machine uses a fixed treatment buffer for plant treatment, then the treatment application is simple to implement, but the precision and accuracy of treatment targeting deteriorates due to unreliable sensor data and varying terrain
Solution Approach 1:
The patent implements dynamic adjustment of the treatment buffer size based on real-time uncertainty measurements. The buffer is not fixed but adapts its size according to the reliability of sensor data and environmental conditions, allowing the system to maintain high treatment precision while managing the complexity through automated uncertainty assessment.
Solution Approach 2:
The system changes the parameter of treatment buffer size dynamically based on uncertainty measurements. When uncertainty is high (unreliable sensor data or varying terrain), the buffer size increases to ensure adequate coverage. When uncertainty is low, the buffer size decreases for more precise treatment application, thus resolving the contradiction between precision and complexity.
2Loss of substance
If a farming machine broadly distributes treatment compounds across a field, then the treatment coverage is comprehensive, but the waste of treatment compounds increases as only part of the treatment reaches targeted plants
Solution Approach 1:
The patent applies local quality by varying the treatment buffer size locally based on uncertainty measurements for different areas of the field. In regions with high uncertainty, a larger buffer ensures reliable treatment coverage. In regions with low uncertainty, a smaller buffer reduces treatment compound waste while maintaining adequate coverage, thus resolving the contradiction between substance loss and targeting reliability.
Solution Approach 2:
The system uses feedback from sensor data and uncertainty measurements to dynamically adjust treatment buffer sizes. This feedback mechanism allows the system to optimize treatment compound distribution in real-time, applying larger buffers where needed for reliability and smaller buffers where precision is already high, thereby reducing overall waste while maintaining treatment effectiveness.
3Manufacturing precision
If a farming machine precisely targets plants for treatment application, then the treatment compound usage is optimized, but the difficulty of accurate targeting increases particularly as the machine moves through varying terrain
Solution Approach 1:
The patent employs beforehand cushioning by incorporating an uncertainty measurement that accounts for potential errors in spatial relationship detection. Rather than attempting to eliminate all measurement difficulties, the system proactively adds a buffer zone sized according to the measured uncertainty, cushioning against the difficulties of detecting and measuring spatial relationships in varying terrain conditions.
4Productivity
If a farming machine uses automated control with sensor data, then the operational efficiency is improved, but the reliability of operation deteriorates due to frequent issues with sensor data variability
Solution Approach 1:
The system uses feedback from sensor data quality assessment to dynamically adjust treatment buffer sizes. When sensor data reliability is low, the system automatically increases buffer sizes to compensate for potential measurement errors, maintaining operational reliability while preserving automated efficiency. This feedback loop allows the system to continue operating automatically without human intervention while adapting to varying data quality.
Solution Approach 2:
The system changes the treatment buffer parameter based on sensor data reliability assessments. By automatically adjusting this parameter in response to sensor quality variations, the system maintains both productivity (through continuous automated operation) and reliability (through adaptive compensation for sensor issues), resolving the contradiction between operational efficiency and automated operation reliability.
Data Source
AI summary
As a farming machine travels through a field of plants, the farming machine accesses an image of a field including a plant and receives sensor signals from one or more sensors coupled to the farming machine. The farming machine applies the image and sensor signals to a computer model to determine a spatial relationship between a treatment mechanism of the farming machine and the plant. Determining the spatial relationship produces an uncertainty measurement for an expected position of the treatment mechanism respective to an expected position of the plant. The farming machine adjusts a treatment buffer based on the uncertainty measurement. The farming machine treats the plant in the field by applying the plant treatment to the plant based on the treatment buffer.