Plant Blooming Period Broadcast Map Using Image Analysis
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
People face challenges in finding information about available plants for viewing and predicting the best viewing periods for blooming plants in outdoor locations, such as parks and botanical gardens, as current technologies lack effective methods to provide real-time and accurate information on plant species, blooming states, and viewing locations.
Innovation Solution
A plant blooming period broadcast method and system that uses image analysis, neural network models for plant identification, and geographic data to determine the blooming period and display this information on a broadcast map, allowing users to upload images to identify plant species, blooming states, and photographing locations, thereby updating the map with specific variety and blooming period information.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If image analysis and neural network models are used to identify plant species and blooming states, then measurement precision of plant information is improved, but device complexity increases
Solution Approach 1:
The patent uses neural network models as intermediary components between the image input and the plant identification output. The model serves as a mediator that processes complex image data and translates it into identified plant species and blooming states, resolving the contradiction by introducing a specialized processing layer that improves accuracy while managing complexity through modular architecture
Solution Approach 2:
The patent replaces manual plant identification methods with automated image analysis using neural networks. This substitution transitions from mechanical/human observation to an automated computational system, significantly improving measurement precision while the system complexity is managed through the use of established deep learning frameworks
2Loss of information
If real-time plant information is provided through image upload and analysis, then loss of information is reduced, but use of energy increases
Solution Approach 1:
The patent performs preliminary actions by pre-training neural network models with extensive plant image data before deployment. This preliminary training phase, though energy-intensive, is done once during system setup rather than continuously during operation. During actual use, the pre-trained model efficiently processes user uploads with reduced energy consumption while maintaining high information availability
Solution Approach 2:
The patent uses digital copies of plant images for analysis rather than requiring physical plant specimens or extensive field surveys. This copying approach minimizes information loss by allowing multiple analyses of the same image data without additional energy expenditure, as the digital copy can be processed repeatedly without degradation
3Loss of time
If plant blooming period information is broadcast to users, then loss of time for finding viewing locations is reduced, but device complexity increases
Solution Approach 1:
The patent implements a feedback mechanism where user uploads of plant images automatically trigger identification and blooming period determination, which is then broadcast to relevant users. This automated feedback loop reduces time loss by eliminating manual information gathering, while the system complexity is managed through event-driven architecture that activates processing only when needed
Solution Approach 2:
The system enables self-service by allowing users to upload their own plant images for identification and blooming period prediction. The system automatically processes these uploads and provides information without requiring manual intervention from operators, reducing time loss while keeping the core broadcasting mechanism relatively simple through automated workflows
Data Source
AI summary
The disclosure provides a plant blooming period broadcast method and system, and a computer-readable storage medium. The method comprises: receiving an image, and identifying a plant in the image to obtain the species of the plant; using a plant variety identification model corresponding to the species of the plant to identify the specific variety and blooming state of the plant; obtaining the photographing time and photographing position of the image, and determining the blooming period of the plant according to the photographing time and the blooming state; and marking the photographing position on a blooming period broadcast map as a viewing place of the plant, and correspondingly displaying the specific variety and blooming period of the plant.