AI agriculture enables disease detection, quality grading, and environmental forecasting to boost productivity and resilience.
Use image recognition to detect crop diseases and pests, and provide treatment recommendations to reduce yield loss.
Analyze images and sensor data to assess ripeness and defects, enabling automated grading and improving market competitiveness.
Combine satellite, weather, and sensor data with AI models to predict crop growth, drought, and flood conditions.
Beehive scene recognition dataset to support intelligent agricultural management.
The Tea Garden Landscape Recognition Dataset aims to enhance tea garden management efficiency through image classification technology.
The Tea Garden Ecological Monitoring Dataset is used to support target detection and ecological monitoring in the agricultural sector.
The Tea Garden Scene Recognition Dataset provides high-quality object detection data for the agriculture sector, aiding the development of intelligent technologies.
A multimodal dataset integrating vineyard images, sensor data, and management records for comprehensive analysis and decision support in smart agriculture systems.
This dataset combines visual data from vineyards with field environment data to build a yield prediction dataset for AI model training.
This dataset records the state of grapevines at different growth stages to aid precise agricultural management.
This dataset focuses on image detection of grape planting rows, supporting smart agriculture applications.
This dataset contains images of vineyard scenes under different seasons and lighting conditions, supporting AI model orchard recognition and analysis.
This dataset records images of papaya from seedling to fruiting stages, intended for AI models to recognize crop growth status and support precision agriculture management.
This dataset is used for the automatic detection and counting of papaya fruits, aiding the development of agricultural intelligence.
The Papaya Tree Recognition Dataset is used to support the automatic identification and classification of AI models, promoting intelligent agricultural management.
This dataset is used for the recognition of asparagus packaging labels, supporting supply chain traceability and anti-counterfeiting applications.
Collection of asparagus images under different brands and packaging methods for packaging detection and automatic recognition in retail scenarios.
This dataset is used for asparagus quality grading to support agricultural automation detection.
This dataset is used for training models to automatically recognize asparagus, containing images under different varieties and lighting conditions.
This dataset is used to analyze the correlation between the color characteristics and maturity of strawberries to enhance agricultural intelligence.
The Strawberry Harvest Traceability Dataset provides comprehensive support for agricultural intelligent traceability.
The Strawberry Defect Detection Dataset is used for AI models to automatically identify abnormal fruit features on strawberries.
Strawberry quality grading dataset to enable intelligent grading and inspection.
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