Seed Trench Imaging With ML Feedback for Planting Depth Control
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Modern planting operations face challenges such as crop residue, soil clods, dry topsoil, collapsing trenches, improper seed planting depth, and invisible application of crop care additives, which reduce germination and yield, requiring manual and subjective operator adjustments that are time-consuming and inefficient.
Innovation Solution
An agricultural image analysis system with vision sensors, machine learning modules, and supplemental lighting to monitor seed trench formation, detect issues like peeling, smearing, collapsing, debris, and improper depth, and adjust planter settings automatically or alert operators.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If manual observation of seed trench is performed by stopping equipment, then operator can assess trench quality, but productivity is reduced and time is lost
Solution Approach 1:
The patent replaces manual mechanical observation with an automated optical imaging system. A camera mounted on the planter captures images of the seed trench during operation, and these images are transmitted to a display in the tractor cabin, allowing the operator to assess trench quality without stopping the equipment.
Solution Approach 2:
The patent creates a visual copy of the seed trench by capturing images with a camera. These images serve as a replica of the actual trench conditions, allowing the operator to examine trench quality remotely without physically interrupting the planting process.
2Loss of information
If manual trench observation is performed by stopping equipment, then operator can identify issues, but time consumption increases
Solution Approach 1:
The patent enables continuous monitoring of trench quality by capturing images during planter operation. The imaging system operates continuously or at intervals without requiring the planter to stop, allowing the operator to receive real-time feedback on trench formation quality and make adjustments while maintaining planting speed.
Solution Approach 2:
The patent implements a feedback loop where images of the seed trench are captured, transmitted to the operator, and used to inform immediate adjustments. This continuous feedback mechanism allows the operator to respond to trench quality issues promptly without significant time loss.
3Stability of the object's composition
If downforce is increased to prevent trench collapse, then trench stability improves, but soil compaction increases
Solution Approach 1:
The patent uses image feedback to monitor trench sidewall conditions and enable the operator to optimize downforce settings. By observing trench formation quality in real-time, the operator can adjust downforce to the minimum level needed to prevent collapse, avoiding excessive compaction while maintaining stability.
Solution Approach 2:
The patent enables dynamic adjustment of downforce parameters based on observed trench conditions. The operator can modify downforce settings in response to real-time images, changing the applied force parameter to achieve optimal balance between stability and compaction prevention.
4Manufacturing precision
If operator manually checks seed trench multiple times, then planting accuracy improves, but operational efficiency decreases
Solution Approach 1:
The patent creates visual copies of the seed trench at different stages of formation, allowing the operator to assess planting accuracy without repeated physical interruptions. The image transmission system delivers these copies to the cabin display for continuous monitoring.
Solution Approach 2:
The patent replaces repeated manual inspection operations with a single automated imaging and transmission system. The camera and communication system substitute for multiple manual checks, maintaining planting accuracy monitoring while improving operational efficiency.
Data Source
AI summary
An agricultural image analysis system comprising at least one vision sensor configured to view a seed trench; a storage module in communication with the at least one vision sensor; a processor in communication with the storage module, the processor executing at least one machine learning module for analysis of images from the at least one vision sensor. The system including at least one laser configured to emit a beam at an open seed trench and at least one vision sensor configured to view the open seed trench and the beam. The system including a thermal camera mounted to a row unit.