Multimodal Plant Recognition With Interactive Symptom Questioning
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Solution Overview
Problem
Existing plant recognition systems struggle to accurately and comprehensively understand and express information from plant images and related questions due to limitations in multimodal data processing, particularly in distinguishing between different plant types and symptoms.
Innovation Solution
A plant recognition method utilizing a plant recognition model combining a visual model, such as a convolutional neural network transformer model, with a multimodal large language model, trained with plant image-text pairs through contrastive learning, and enhanced by a second visual model like CLIP, to process plant images and questions, and interactively gather additional information when needed.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a single visual model is used for plant recognition, then the device complexity is low, but the recognition accuracy and reliability are insufficient
Solution Approach 1:
The patent combines multiple visual models (first visual model and second visual model) with different strengths into a unified plant recognition system. The first visual model extracts comprehensive image features while the second visual model provides additional visual understanding, and their features are fused to improve recognition accuracy and reliability for distinguishing plant types and symptoms.
Solution Approach 2:
The plant recognition model is designed to handle multiple functions including plant type identification, symptom detection, and interactive questioning. The multimodal large language model serves as a universal processor that can answer various types of questions about plants based on image features and text inputs, making the system adaptable to different recognition tasks.
2Loss of information
If traditional unimodal models are used, then the system is simpler to operate, but the ability to comprehensively understand and express plant information is limited
Solution Approach 1:
The multimodal large language model acts as an intermediary that bridges visual features from the visual models and text questions. It processes the fusion of image features and question text to generate comprehensive answers, enabling the system to understand and express plant information thoroughly while managing the complexity through a unified interface.
Solution Approach 2:
The system uses composite multimodal data including image features from multiple visual models, question text, and answer text. This composite information structure allows the system to comprehensively understand plant characteristics and symptoms by integrating different types of data representations.
3Reliability
If interactive questioning is implemented, then the recognition reliability improves through additional information gathering, but the processing time increases
Solution Approach 1:
The system performs preliminary processing by extracting image features using multiple visual models before interactive questioning begins. This preliminary feature extraction prepares the data in advance, allowing the multimodal large language model to quickly process questions and generate answers during interaction, reducing the overall processing time while maintaining reliability.
Solution Approach 2:
The interactive questioning mechanism implements feedback loops where the system generates answers based on current image features and questions, then uses the quality and completeness of these answers to determine whether additional questioning is needed. This feedback-driven approach ensures reliable recognition while minimizing unnecessary interaction steps that would increase processing time.
Data Source
AI summary
A plant recognition method and related devices. The plant recognition method includes: obtaining a plant image and question text about recognizing the plant in the plant image; inputting the plant image and the question text to a plant recognition model, the plant recognition model includes a first visual model and a multimodal large language model, the first visual model is configured to receive the plant image to extract first image features of the plant image, the multimodal large language model is configured to receive the first image features and the question text to recognize the plant in the plant image, the plant recognition model is trained with multimodal data, the multimodal data includes plant images, questions about recognizing plants in the plant images, and answers to the questions; and outputting answer text provided by the plant recognition model about recognizing the plant in the plant image.