Longitudinal Aerial Imagery for Nutrient Deficiency Stress in 386 Farm Parcels

The Longitudinal Nutrient Deficiency dataset is associated with a 2021 AAAI paper on detection and prediction of nutrient deficiency stress. It contains three image sequences of aerial imagery from 386 farm parcels, which have been annotated for nutrient deficiency stress. The data is hosted on AWS Open Data and was contributed by Intelinair, Inc.

Use Cases

• Train computer vision models to detect nutrient deficiency stress based on annotated aerial imagery.
• Analyze temporal patterns of crop health based on longitudinal image sequences.
• Develop predictive models for nutrient deficiency based on historical aerial imagery.
• Benchmark agricultural image analysis algorithms using a dataset with expert annotations.

Strengths

• Contains annotations for nutrient deficiency stress, providing a labeled dataset for supervised learning.
• Includes longitudinal imagery from 386 distinct farm parcels, offering multiple observations per location.
• Data is associated with a peer-reviewed AAAI 2021 paper, suggesting a research-grade origin.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count, file size, and last update date are unknown, which may limit suitability assessment.
• Data may reflect geographic or temporal bias inherent to the specific 386 farm parcels.

Provenance

Source

Intelinair, Inc.

Collection Method

Aerial imagery collection and expert annotation for nutrient deficiency stress.

Time Range

Time range is unknown.

Freshness

Last update date is unknown; freshness unverified.

Geography

Geography is unknown, but data is from farm parcels.