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 Cabbage Yield Prediction Dataset aims to improve the accuracy of agricultural yield predictions.
This dataset focuses on image classification of cabbage disease spots, providing high-quality image resources to support agricultural disease detection research.
This dataset provides image data of cabbage pests, aiding precision agriculture and intelligent agricultural monitoring.
The Cabbage Field Growth Monitoring Dataset is designed to support agricultural object detection tasks, providing rich image data to enhance crop monitoring efficiency.
This dataset provides image classification support for grape destemming task status in the agricultural field.
This dataset provides high-quality object detection data for grape sorting machinery monitoring in the agricultural field.
The Grape Destemming Recognition Dataset aims to advance agricultural automation and improve destemming efficiency.
This dataset aims to improve the efficiency and accuracy of carrot appearance inspection.
The Carrot Raw Material Recognition Dataset is a large-scale dataset focused on agricultural object detection.
This dataset provides high-quality carrot growth stage recognition data to support the development of smart agriculture.
This dataset is used for carrot quality grading, supporting target detection tasks.
The Carrot Harvest Detection Dataset provides precise target detection data for smart agriculture, helping to improve harvest efficiency.
The Orchard Land Use Monitoring Dataset provides high-quality image data for agricultural object detection, aiding intelligent agriculture management.
This dataset provides high-quality image data for orchard yield forecasting, supporting the development of agricultural intelligence.
This dataset is used for classifying the growth stages of fruit trees, aiding in precision agriculture management.
The fruit tree quantity detection dataset aims to improve the efficiency and accuracy of fruit tree management.
The Orchard Scene Recognition Dataset provides high-quality images and annotations for agricultural object detection, aiding in orchard management and monitoring.
This dataset provides high-quality beehive environment monitoring images for supporting object detection tasks.
The Beehive Detection and Recognition Dataset provides high-quality object detection data for agricultural intelligent monitoring, aiding in improving operational efficiency.
The Beehive Operation Behavior Recognition Dataset is used to support intelligent beekeeping and automatic detection of bee behaviors.
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