Plant Structural Representation via Neural Network Encoding
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Solution Overview
Problem
Existing machine vision technologies face challenges in accurately describing the complex morphological structures of plants due to occlusions and the need for sufficient labeled training data, particularly in unsupervised or semisupervised learning contexts.
Innovation Solution
A system and method that train a machine learning model to generate structural representations of plants using an encoder to transform input images into predicted structural representations, a decoder to reconstruct images, and a discriminator to classify and train parameters, allowing for unsupervised or semisupervised training with limited labeled data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If general-purpose skeleton extraction algorithms are used to describe plant structure, then the processing speed is fast, but the accuracy of plant morphology representation deteriorates due to occlusions and divergence from actual plant structure
Solution Approach 1:
The patent replaces traditional mechanical skeleton extraction algorithms with a neural network-based deep learning system. The neural network learns plant structure representations directly from images, substituting the mechanical algorithmic approach with a data-driven learning system that can handle occlusions and complex plant morphologies more effectively
Solution Approach 2:
The patent transforms the plant image into a different parameter space (structural representation) using a neural network encoder. This parameter transformation allows the system to capture essential plant structure characteristics while being invariant to occlusions and variations, improving morphology representation accuracy
2Measurement precision
If fully-supervised training with extensive labeled data is used to improve model accuracy, then the plant structure representation accuracy is improved, but the data acquisition cost and time increase significantly
Solution Approach 1:
The patent implements self-supervised learning where the model learns from unlabeled plant images by creating its own training signals. The neural network learns to represent plant structures by processing raw images without requiring manual annotation, enabling the system to improve accuracy while avoiding the time-consuming data labeling process
Solution Approach 2:
The patent uses image-to-image translation to create synthetic training data and structural representations. By learning to translate between image domains and structural representations, the system can generate training examples from unlabeled data, reducing dependence on extensive labeled datasets
3Adaptability or versatility
If image-to-image translation models are used to generate structural representations, then the ability to handle varied plant morphology is improved, but the requirement for labeled training data increases
Solution Approach 1:
The patent applies self-supervised learning to image-to-image translation, where the model learns to translate between image domains and structural representations using unlabeled plant images. This eliminates the need for paired labeled training data while maintaining the ability to handle diverse plant morphologies
Data Source
AI summary
Systems and methods for training a machine learning model for generating a structural representation of a plant are provided, as well as systems and methods for generating a structural representation of a plant via such a model. The training method involves encoding a plant image into a structural representation of the plant (e.g. a “skeleton”), decoding the structural representation of the plant into a reconstructed image of the plant, and classifying the reconstructed image as having been generated based on a ground-truth structural representation or output of the encoder. Such classification incentivizes the encoder to produce structural representations which do not “smuggle” texture information (e.g. appearance, such as color). Texture information may be separately represented. The encoder, once trained, may be used to generate structural representations from plant images without necessarily requiring decoding or classification.