Autonomous Grain Sampling and Grading With LiDAR and NIR
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Solution Overview
Problem
Current grain processing facilities require constant human operator presence, leading to high operational costs and limited delivery times for farmers, as well as bottlenecks at facilities due to synchronized delivery demands.
Innovation Solution
An autonomous grain receiving and loadout facility that autonomously interacts with truck drivers, samples, grades, and manages grain storage and unloading without constant operator coverage, using LiDAR sensors and NIR sensors for precise sampling and grading.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If human operators are used to handle grain sampling and grading processes, then operational flexibility and decision-making quality are maintained, but labor costs increase and continuous operation capability is reduced
Solution Approach 1:
The system enables self-service automation where the grain processing facility autonomously performs sampling, grading, and decision-making without human operators. The robot navigates independently, sensors automatically detect grain characteristics, and the system self-determines storage bin assignments based on grading results, eliminating the need for continuous human presence while maintaining operational reliability
Solution Approach 2:
Manual mechanical operations are replaced with an integrated system combining mobile robotics, LiDAR sensing, NIR spectroscopy, and automated control. The robot substitutes human physical sampling, sensors replace visual inspection, and automated algorithms replace human decision-making, achieving both high automation and reliable operation
2Productivity
If facilities operate with constant human coverage, then grain processing can continue at any time, but operational costs increase significantly
Solution Approach 1:
The autonomous system performs all grain processing operations independently without requiring human energy input. The robot self-navigates, self-samples, the system self-grades and self-manages grain routing, eliminating labor costs entirely while maintaining continuous productivity during facility operating hours
Solution Approach 2:
The system performs preliminary actions by pre-positioning the robot and pre-configuring sampling equipment before grain arrivals. The autonomous capability allows the facility to be ready for immediate processing without waiting for human operator availability, enabling continuous operation during facility hours
3Productivity
If farmers deliver grain during peak facility hours, then all processing operations can be completed efficiently, but bottlenecks form due to synchronized delivery demands
Solution Approach 1:
The autonomous system enables continuous grain processing operations without interruption by human breaks, shifts, or availability constraints. The robot operates continuously during facility hours, processing grain as it arrives without waiting for operator availability, eliminating delivery waiting time and preventing bottlenecks from synchronized arrivals
Solution Approach 2:
The system prepares for grain processing in advance by having the robot positioned and ready before each delivery arrives. The autonomous readiness allows immediate processing upon grain arrival, preventing queue formation and reducing delivery time regardless of arrival synchronization
4Measurement precision
If manual sampling methods are used, then equipment complexity is reduced, but sampling precision and area coverage are limited
Solution Approach 1:
Manual sampling is replaced with an automated robot system equipped with LiDAR for precise positioning and sensors for grain detection. The complex automated system achieves superior sampling precision by systematically covering multiple areas and using technological sensors rather than manual visual inspection and physical sampling
Solution Approach 2:
The sampling system transitions from manual point-based sampling to multi-dimensional automated sampling. The robot moves through three-dimensional space to access multiple sampling areas within the grain storage structure, while sensors detect grain characteristics across different spatial dimensions, achieving comprehensive coverage and high precision
5Measurement precision
If multiple sensors are used for grain grading, then grading accuracy improves, but system complexity and data processing requirements increase
Solution Approach 1:
Multiple sensing modalities (LiDAR for spatial mapping, NIR for compositional analysis, and other sensors for physical properties) are merged into a single integrated grading system. The combined sensor data is processed together to comprehensively assess grain quality across multiple parameters, achieving high grading accuracy while managing system complexity through integration
Solution Approach 2:
The sensor system is designed with multi-functionality where sensors serve multiple purposes: LiDAR provides both navigation and grain pile mapping, NIR sensors detect both moisture and compositional properties. This universal approach allows accurate grading across multiple grain parameters using a unified sensor platform rather than separate specialized systems
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
Enables continuous grain processing operations, reducing labor costs and delivery time constraints, while improving efficiency and accuracy in sampling and grading processes.
Implementation Method 1
The sampling system comprises at least a robot with a light detection and ranging (LiDAR) sensor system
Implementation Method 2
a first sensor system to measure first data for the respective sample, wherein the first sensor system comprises a near infrared (NIR) sensor
Data Source
AI summary
Processes for an autonomous grain facility are described herein. The process could include sampling and grading. For sampling, the system controls a robot to capture position data of a trailer and area data of grain, determines one or more sampling areas within the trailer, and controls the robot to obtain a sample from each of the sampling areas. For grading, the system measures data with both a near infrared sensor and a second sensor, analyzes all of the data to determine a dispositive action to be performed on the respective sample, and controls the system to perform the dispositive action.