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.
This dataset is used for AI automated judgment of strawberry ripeness, promoting the development of intelligent picking and grading technology.
This dataset is used for the detection and localization of strawberry fruits, aiding the development of agricultural intelligence.
Includes images of farmland floods under extreme weather such as heavy rains and flash floods, for AI model research on the impact of climate change on agriculture.
This dataset is used for AI automatic recognition and claim assistance of flood disaster farmland images.
This dataset records crop growth anomalies in flood environments, aiming to support AI models in anomaly identification.
This dataset provides rich images and annotation data for farmland water detection, supporting the application of AI technology in agriculture.
This dataset is used for disaster level classification of crops with different levels of flooding, supporting AI training and evaluation.
This dataset is used to monitor the water accumulation situation in farmland at different stages after heavy rainfall, aiding agricultural risk management.
This dataset is used to provide precise segmentation annotations of farmland flood areas, supporting AI model training.
This dataset is used to assess the impact of floods on farmland, aiding smart agriculture management.
This dataset is used to detect the growth status of crops under waterlogged conditions, aiding intelligent agricultural management.
This dataset is used to identify the impact of floods on farmland, aiding in the intelligent management of agriculture.
This dataset records the ecological damage patterns of pests on crop leaves, aiming to support agricultural ecological research.
This dataset is used for detecting abnormal conditions of crop leaves to support intelligent agricultural monitoring.
This dataset is used for AI training in real-time farmland pest monitoring systems, combining individual pests and feeding traces.
This dataset is designed to support joint detection and recognition of pests and diseases in the agricultural field.
This dataset is used to evaluate pest severity through the annotation of the number and area of leaf perforations.
This dataset is used for farmland pest species recognition, containing a wealth of image samples and annotation information.
This dataset is used for AI classification of different types of crop pest and disease images.
This dataset provides precise pixel-level segmentation annotations for crop pest monitoring, aiding the development of AI models.
Lightning-Fast Response, 1:1 Technical Validation