Mobile LiDAR Crop Phenotyping for Lightweight Field Measurement
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Solution Overview
Problem
Existing methods for crop phenotyping, such as manual height and biomass measurements, are inefficient, laborious, and prone to errors, and existing digital sensors are often too large, heavy, or require excessive computational power, making them unsuitable for high-throughput plant phenotyping (HTPP).
Innovation Solution
A lightweight, compact LiDAR-based sensor system with wireless communication and GNSS integration, mounted on a mobile platform, that measures crop height and biomass non-destructively, transmitting data to a remote computing system for high-throughput phenotyping.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If manual methods are used for crop height and biomass measurements, then measurement accuracy is maintained, but productivity is low and labor requirements are high
Solution Approach 1:
The patent replaces manual mechanical measurement methods with an automated optical sensing system. A sensor system comprising a time-of-flight sensor, processor, and display device automatically captures and processes light signals to determine crop height and biomass, eliminating the need for manual measurement while maintaining measurement capability through optical detection principles
Solution Approach 2:
The system enables self-service measurement by automatically capturing images, processing depth information, and calculating phenotypic traits without human intervention. The processor autonomously analyzes the captured light signals and generates measurements, allowing the system to serve itself in the measurement process
2Extent of automation
If existing digital sensors are used for plant phenotyping, then automation is achieved, but device size and weight become problematic
Solution Approach 1:
The patent extracts only the essential functional components needed for phenotyping measurements, eliminating unnecessary bulk. The sensor system uses a focused time-of-flight sensing approach that captures only the relevant light signals from crops, removing extraneous sensing capabilities that would add weight and complexity
Solution Approach 2:
The system changes the operational parameters of the sensor to optimize for lightweight design. By adjusting the field of view, measurement range, and processing algorithms, the system achieves accurate phenotyping with a minimized sensor configuration that reduces overall system weight
3Measurement precision
If comprehensive sensor data is collected for accurate phenotyping, then measurement precision is improved, but data processing complexity increases
Solution Approach 1:
The patent extracts only the essential phenotypic information from the captured data, focusing on key traits such as height and biomass. The processor is configured to identify and extract specific depth information relevant to crop phenotyping, filtering out unnecessary data while maintaining measurement precision for the targeted traits
Solution Approach 2:
The system performs preliminary processing of the captured light signals to pre-identify relevant depth information before final analysis. By pre-processing the optical data to highlight critical phenotypic features, the system reduces the complexity of subsequent measurements while maintaining accuracy
4Measurement precision
If destructive harvesting methods are used for biomass measurement, then accurate biomass data is obtained, but crop continuity is interrupted and time is lost
Solution Approach 1:
The patent replaces destructive mechanical harvesting with non-destructive optical sensing. The time-of-flight sensor uses light signals to measure crop biomass and height without physical contact or damage to the plants, allowing continuous monitoring while maintaining measurement accuracy through optical detection
Solution Approach 2:
The system enables continuous self-service monitoring of crop biomass without interrupting crop growth. The automated optical sensing allows the crops to continue their natural development while providing ongoing biomass measurements, eliminating the time loss associated with destructive sampling
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 accurate, efficient, and scalable crop height and biomass estimation with reduced data volume, allowing for rapid data collection and processing, suitable for large-scale agricultural applications.
Implementation Method 1
a Light Detection And Ranging (LiDAR) module 302 with a laser emitter 318 configured to generate measurement data representing raw range measurements to measure heights of a crop 104
Implementation Method 2
generate measurement data representing raw range measurements
Data Source
AI summary
A measurement system including: a. a sensor system that includes: i. a Light Detection And Ranging (LiDAR) module with a laser emitter configured to generate measurement data representing raw range measurements to measure heights of a crop, and ii. a computing module, including: at least one wireline/wired communications module configured to communicate with the LiDAR module for the computing module to acquire the measurement data from the LiDAR module; and at least one wireless communications module configured for the computing module to communicate using a wireless connection/link with a remote computing system that is configured receive the acquired measurement data and to determine/calculate/estimate phenotypic quantities of the crop based on the measured heights for the purpose of high-throughput plant phenotyping (HTPP); and b. a mobile/vehicle mount configured to hold/support the sensor system above the crop and to direct the laser emitter towards the crop.