Differentiable Fish Biomass Model for Unified Image-Based Estimation
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Solution Overview
Problem
Existing biomass estimation methods in aquaculture are inefficient and inaccurate, often requiring multiple non-differentiable models that introduce computational complexity and degrade overall accuracy, making it difficult to determine optimal feeding schedules and maintain fish health.
Innovation Solution
An end-to-end differentiable model that uses a multi-layer neural network to directly estimate fish biomass, allowing joint optimization across layers and reducing computational complexity, while accounting for un-modeled effects to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple separate machine learning models are used to perform sequential tasks in biomass estimation, then each task can be optimized individually, but the overall system complexity and computational load increase significantly
Solution Approach 1:
The patent combines multiple separate machine learning models into a single end-to-end differentiable model that performs all biomass estimation tasks sequentially in one unified system. This merging approach maintains the ability to perform distinct tasks (detection, segmentation, measurement) while eliminating the complexity of coordinating multiple independent models and their separate training pipelines.
2Ease of manufacture
If multiple non-differentiable models are used in sequence for biomass estimation, then each model can be trained independently, but the overall computational load and training complexity increase
Solution Approach 1:
The patent merges multiple independent training processes into a single end-to-end training pipeline where all model components are trained simultaneously with unified gradient descent. This approach reduces the total computational energy required by eliminating redundant forward and backward passes across multiple separate models while maintaining independent learnable parameters throughout the pipeline.
3Measurement precision
If separate models are used for each task in the biomass estimation pipeline, then each model can be optimized for its specific task, but adjustments to one model may cause unwanted downstream effects on other models
Solution Approach 1:
The patent implements a unified end-to-end differentiable pipeline where gradient feedback flows continuously from the final biomass estimation output back through all intermediate tasks. This allows the system to optimize for task-specific performance while maintaining global consistency, as adjustments in any component automatically propagate feedback to ensure overall estimation accuracy without causing unwanted downstream effects.
4Adaptability or versatility
If multiple separate models are deployed for biomass estimation, then comprehensive task coverage is achieved, but the computational resources and processing time required increase
Solution Approach 1:
The patent merges multiple task-specific models into a single end-to-end differentiable architecture that processes images through all tasks in one unified forward pass. This maintains comprehensive task coverage (detection, segmentation, keypoint identification, biomass calculation) while significantly improving processing efficiency by eliminating redundant computations and enabling parallel optimization across all tasks simultaneously.
Data Source
AI summary
Methods, systems, and apparatus, including computer programs encoded on a computer storage medium, that obtain fish images from a camera device and generate predicted values by providing one or more of the fish images to an end-to-end model. The end-to-end model is trained to estimate weight of fish from the fish images and includes one or more differentiable layers configured to adjust one or more parameters of the end-to-end model. By comparing the predicted values to ground truth data representing weights of one or more fish, one or more parameters of the end-to-end model can be updated based on the comparison of the predicted values.