Aquatic Biomass Estimation Using Point Cloud Segmentation
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Estimating biomass in aquatic environments, such as oceans, is challenging due to difficulties in data gathering, processing bandwidth, and model quality, particularly for large-scale underwater ecosystems like seagrass, which complicates the measurement of carbon sequestration.
Innovation Solution
The use of electronic systems equipped with cameras and sonar to estimate biomass by obtaining images and sensor data, generating point clouds, and identifying specific subsets of points to calculate the volume and density of aquatic grass, allowing for the prediction of carbon sequestration potential.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional data gathering methods are used in aquatic environments, then equipment simplicity is maintained, but measurement precision and data quality deteriorate due to scale and accessibility challenges
Solution Approach 1:
The system segments the aquatic environment into discrete image frames captured by cameras or sonar devices. Each frame is independently processed to identify biomass features, and results are aggregated to estimate total biomass. This segmentation approach enables precise local measurements while managing the complexity of large-scale aquatic environments through modular data collection and processing.
2Measurement precision
If comprehensive sensor data is collected from aquatic environments, then measurement precision improves, but processing bandwidth requirements increase
Solution Approach 1:
The system extracts only the essential features needed for biomass estimation from the comprehensive sensor data. Image processing algorithms identify and extract key characteristics such as organism boundaries, densities, and spatial distributions, discarding redundant information. This extraction process maintains measurement precision by preserving critical biomass indicators while significantly reducing the quantity of data that requires further processing.
3Measurement precision
If detailed biomass estimation is performed, then carbon sequestration measurement accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The system performs preliminary classification and segmentation of biomass types before detailed carbon sequestration calculations. Pre-trained machine learning models quickly categorize organisms by species and density categories, enabling subsequent carbon estimation to focus computational resources on refined measurements of already-identified biomass. This preliminary action reduces processing time by avoiding redundant analysis while maintaining carbon sequestration measurement accuracy.
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 estimation of aquatic biomass, facilitating the calculation of future carbon credits by determining the volume and density of seagrass and other underwater organisms, thereby supporting sustainable management and carbon offsetting.
Implementation Method 1
providing the image to a network model trained to construct a point cloud indicating a portion of the image that represents the aquatic grass
Implementation Method 2
systems can estimate seagrass biomass using various sensors such as stereo cameras and sonar
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on computer storage media, for aquatic biomass estimation. One of the methods includes obtaining an image of an aquatic environment including aquatic grass; providing the image to a network model trained to construct a point cloud indicating a portion of the image that represents the aquatic grass; generating a floor model indicating a floor of the aquatic environment where the aquatic grass grows; identifying, using (i) the floor model and (ii) the point cloud indicating the aquatic grass, (i) a first subset of points in the point cloud as indicating aquatic grass and (ii) a second subset of points in the point cloud as indicating the floor of the aquatic environment; and generating, using the first subset of points in the point cloud, an indication of biomass within the aquatic environment.