A multimodal dataset for strawberry disease detection contains strawberry image data, corresponding environmental parameters (air temperature, air humidity, soil moisture) and strawberry variety information. It can be used to study the correlation between environmental factors and strawberry disease occurrence, as well as multimodal fusion disease detection algorithms. The dataset was authored by Qin2006 and last updated on 2026-04-19.
Use Cases
- Train multimodal disease detection models based on strawberry images and environmental sensor data.
- Analyze correlations between disease occurrence and environmental factors like air temperature and soil moisture.
- Develop algorithms for multimodal fusion in agricultural monitoring based on image and sensor data.
- Study the impact of strawberry variety on disease susceptibility based on variety information.
Strengths
- Multimodal structure combines visual, environmental, and categorical data.
- Includes specific environmental parameters: air temperature, air humidity, and soil moisture.
- Contains strawberry variety information, enabling cultivar-specific analysis.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count is unknown, which may limit suitability assessment.
- Last updated 2026-04-19 11:28:02; freshness should be verified.
Provenance
- Source
- huggingface
- Freshness
- 2026-04-19