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.
The Grassland Degradation Monitoring Dataset aims to provide accurate target detection data support for the agricultural sector.
The weed detection dataset provides high-quality object detection data for the agriculture field, aiding crop health management.
This dataset provides high-quality pasture coverage segmentation data to aid in agricultural intelligence monitoring.
The Grass Seed Recognition Classification Dataset provides a wealth of grass seed images, supporting the development of smart agriculture.
This dataset provides high-quality image data for farmland soil moisture estimation, aiding the development of precision agriculture.
This dataset provides a wealth of soil moisture cycle change image data, supporting agricultural monitoring and research.
This dataset enhances the accuracy of soil classification by using soil samples as reference data for remote sensing images.
This dataset focuses on the identification of soil and organic matter content, aiding the development of precision agriculture.
This dataset focuses on semantic segmentation of soil texture and particles, aiming to improve the efficiency and accuracy of agricultural monitoring.
Soil pollution detection dataset, dedicated to improving the efficiency and accuracy of agricultural soil monitoring.
This dataset provides high-quality image data for soil fertility evaluation, supporting research and applications in precision agriculture.
This dataset aims to help the agricultural sector quickly identify soil types through image recognition technology.
This dataset focuses on the identification and monitoring of seedling health status, supporting object detection tasks.
The Field Seedling Localization Dataset focuses on providing high-quality target detection data for precision agriculture.
This dataset focuses on the relationship between soil fertility and seedling growth, supporting in-depth research and applications in the agricultural field.
This dataset is used for automatic detection and statistics of seedling quantity, supporting precision agriculture.
The Plant Growth Stage Classification Dataset is dedicated to promoting the development of intelligent agriculture.
This dataset focuses on the semantic segmentation of seedlings and soil, aiding the development of precision agriculture.
This dataset is designed for target detection of organic fertilizers and soil environments, supporting agricultural monitoring and evaluation.
This dataset focuses on the automatic detection of plant seedlings, aiding the development of smart agriculture.
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