Seed Cohort Phenotyping System for Plant Factories
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Solution Overview
Problem
Conventional phenotyping methods in plant factories with artificial lighting are labor-intensive and destructive, lacking efficient systems to track and automatically evaluate plant traits during the growth process, especially from sowing to germination, and require expensive equipment, making them costly and impractical for large-scale production.
Innovation Solution
A seed cohort phenotyping system that integrates control, data storage, machine learning, image processing, and statistical analysis to non-destructively measure and track plant traits using RGB and IR image information, environmental factors, and genetic characteristics, enabling continuous monitoring and data collection during the germination period.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional phenotyping methods are used to measure plant traits, then measurement can be performed, but the process becomes labor-intensive and time-consuming
Solution Approach 1:
The patent replaces manual mechanical measurement methods with an automated image processing system. Cameras capture images of plants, and computer algorithms automatically analyze plant traits such as height, leaf area, and morphology. This substitution of mechanical/manual operations with automated optical and computational systems eliminates labor-intensive manual measurement while maintaining measurement precision.
2Measurement precision
If conventional phenotyping methods are used to measure plant traits, then measurement can be performed, but the process requires significant labor input
Solution Approach 1:
The system replaces manual labor with automated image capture and analysis. Multiple cameras positioned around the cultivation chamber continuously capture plant images, and computer vision algorithms automatically extract phenotypic data. This eliminates the need for researchers to manually measure each plant, dramatically reducing labor input while maintaining or improving measurement precision through consistent automated data collection.
Solution Approach 2:
The patent implements continuous automated monitoring of plant traits throughout the growth process. Rather than periodic manual measurements, the system continuously captures images and updates phenotypic data as plants grow. This continuous automated operation eliminates the intermittent labor input required by conventional methods while providing more comprehensive measurement data.
3Measurement precision
If destructive sampling is used for phenotyping, then plant traits can be measured, but the plant cannot be used for further cultivation
Solution Approach 1:
The patent replaces destructive physical sampling with non-destructive optical imaging. Cameras capture images of living plants, and image processing algorithms extract phenotypic information without removing or damaging the plants. This allows continuous monitoring of the same plants throughout their growth cycle, maintaining plant viability for further cultivation and harvest, thereby dramatically improving productivity.
4Productivity
If automated phenotyping systems are implemented, then measurement efficiency improves, but equipment cost increases
Solution Approach 1:
The patent employs a multi-functional integrated system where cameras serve multiple purposes: capturing plant morphology, monitoring growth patterns, and detecting environmental conditions. The same image processing platform analyzes multiple plant traits simultaneously. This multi-functionality reduces the need for separate specialized equipment for each measurement type, thereby improving productivity while controlling equipment costs.
Solution Approach 2:
The system uses optical copying through image capture instead of physical interaction with plants. Digital images serve as copies that can be analyzed repeatedly without additional resource consumption. This approach to measurement creates a scalable system where the same imaging infrastructure can handle increasing numbers of plants without proportional increases in equipment complexity or cost.
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
This system significantly reduces labor and time required for seed selection and cultivation, improving germination rates from 80% to 99.9% and enabling the production of uniform seedlings with reduced costs and equipment needs, enhancing automation and efficiency in large-scale plant factories.
Implementation Method 1
employing an RGB camera and an IR camera to obtain image information of the plant
Implementation Method 2
employing an RGB camera and an IR camera to obtain image information of the plant
Data Source
AI summary
The invention tracks and automatically evaluates plant traits of “individuals of plant canopy” in a growth process of a germination period (from sowing until immediately prior to primary seedling culture), at a plant factory with artificial lighting. Specifically, a two-dimensional distribution of the plant traits is calculated by non-destructively and continuously measuring plant trait information in the growth process of the germination period, on the basis of image information 2a, environmental factor information 2b, genetic characteristic information 2c, and management information 2d. In addition, physiological performance reactions of the individuals of plant canopy cultivated in an environmentally-controlled closed space are continuously measured, and measurements are continuously taken of the two-dimensional distribution of the environmental factor information, and items such as temperature, vapor pressure deficit, nutrient solution percentage, nutrient solution temperature, pH, and electric conductivity.