Crop Residue Imaging for Accurate Bunch and Evenness Detection
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Solution Overview
Problem
Existing systems for determining crop residue parameters in agricultural fields lack accuracy and precision, particularly in identifying residue bunches and evenness, which can hinder agricultural performance.
Innovation Solution
An agricultural machine equipped with an imaging device and a machine-learned model, such as a convolutional neural network or transformer, processes image data to classify pixels and identify residue bunches or evenness, enabling precise control of ground-engaging tools to manage residue distribution.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional image processing methods are used to determine crop residue parameters, then the system complexity remains low, but the measurement precision and accuracy of residue identification are insufficient
Solution Approach 1:
The patent replaces traditional mechanical/image processing methods with a machine-learned model (neural network) to analyze crop residue images. The model automatically identifies residue bunches and evenness parameters, substituting manual or rule-based image processing with intelligent algorithms that achieve higher measurement precision without requiring complex manual intervention.
Solution Approach 2:
The patent introduces an imaging device as an intermediary between the crop residue and the analysis system. This device captures images of the residue, which are then processed by the machine-learned model, creating an intermediary digital representation that enables precise non-contact measurement of residue parameters.
2Productivity
If manual monitoring of crop residue is performed, then the equipment cost is low, but the productivity and efficiency of field monitoring are insufficient
Solution Approach 1:
The system enables self-service monitoring by automatically capturing images of crop residue and processing them through the machine-learned model without requiring manual field inspection. The agricultural machine performs its own residue assessment during operation, eliminating the need for separate manual monitoring activities and significantly improving productivity.
Solution Approach 2:
The imaging device continuously captures residue information as the agricultural machine moves through the field, providing ongoing real-time monitoring rather than discrete manual checks. This continuous data collection maintains useful action throughout the entire field operation, maximizing monitoring efficiency.
3Object-affected harmful factors
If crop residue is left in excessive amounts, then soil protection from erosion is improved, but the productivity potential of the soil is reduced due to slowed warming and seed germination
Solution Approach 1:
The machine-learned model provides real-time feedback on residue bunches and evenness, enabling the control system to adjust ground-engaging tool operations dynamically. This feedback loop allows optimization of residue distribution to maintain protective coverage while preventing excessive accumulation that would harm productivity, balancing erosion protection with soil warming and germination needs.
Solution Approach 2:
The system monitors and adjusts residue distribution parameters (bunches, evenness, coverage) to optimal levels. By changing these parameters from excessive to optimized amounts, the system maintains sufficient residue for erosion protection while reducing excess residue that would slow soil warming and seed germination, thereby preserving productivity potential.
Data Source
AI summary
An agricultural machine includes a computing system communicatively having one or more processors and one or more non-transitory computer-readable media that collectively store a machine-learned model configured to receive the image data and to process the image data to output classifications for pixels of the image data. Furthermore, the one or more non-transitory computer-readable media collectively store instructions that, when executed by the one or more processors, configure the computing system to perform operations. The operations, in turn, include receiving the image data from the imaging device and inputting the image data into the machine-learned model. Additionally, the operations include receiving the classifications for the pixels of the image data as an output of the machine-learned model and identifying residue bunches or residue evenness of within the portion of the field based on the classification for the pixels.