DOI: 10.5281/zenodo.17970103
title: AGIcam Dataset: In-Field IoT Sensor Data for Wheat Phenotyping and Yield Prediction

description: Description:

AGIcam is an affordable, solar-powered Raspberry Pi-based IoT sensor system designed for high-frequency, plot-level data acquisition. Data from the AGIcam system were used to develop time-series models of wheat yield using machine learning (Random Forest) and deep learning (Long Short-Term Memory, LSTM). This dataset provides the raw and preprocessed inputs used in those analyses.

The dataset accompanies the manuscript “AGIcam: An Open-Source IoT-Based Camera System for Automated In-Field Phenotyping and Yield Prediction.” The dataset includes synchronized vegetation index (VI) metrics, weather data, and related agronomic trait data, along with sample images collected with the AGIcam system during spring and winter wheat field trials in the 2022 growing season. 

Dataset Content:



Vegetation Index, Weather, and Agronomic Trait Data (Spring_wheat_data.csv and Winter_wheat_data.csv)The AGIcam dataset includes synchronized vegetation indices, weather measurements, and agronomic trait data. Each row in the dataset represents a single VI measurement extracted from AGIcam images, paired with weather parameters recorded at the corresponding time point. Plot-level heading date and grain yield (kg/ha) are included for downstream phenotyping and modeling applications.



Information on column names in the CSV files



date: Date of data acquisition

timepoint: Hour of image capture

wheat: Wheat type (spring/winter)

sensor: AGIcam sensor ID

variety: Genotype identifier

rep_var: Variety replicate number

rep_pic: Image replicate number

vi: Vegetation index name

max: Maximum VI value

mean: Mean VI value

median: Median VI value

std: Standard deviation of VI values

p95 / p90 / p85: 95th, 90th, and 85th percentile VI values

avg_air_temp: Average air temperature (°C)

humidity: Relative humidity (%)

avg_soil_temp_8_in: Soil temperature at 8-inch depth (°C)

precip: Precipitation (mm)

solar_rad: Solar radiation (W/m²)

heading_date: Date of heading stage

yield_kg/ha: Grain yield (kg per hectare)





Sample Images (AGIcam8.zip and AGIcam17.zip)Selected RGB and NoIR images captured from AGIcam sensors to demonstrate image quality and time resolution.


Data Collection and Processing:



Sensors: AGIcam units (RGB and NoIR cameras)

Weather: ATMOS 41 sensor station 

Location: Spring and winter wheat field trials in Pullman, Washington State.

Image Frequency: 3 captures per day per sensor.


Use Case: This dataset can be used to:



High-frequency VI and weather data modeling

Time-series analysis of crop growth

Machine/deep learning yield prediction

Sensor fusion in breeding programs


Acknowledgments:This study was funded by the United States Department of Agriculture (USDA) - National Institute for Food and Agriculture (NIFA) competitive project (accession number 1028108), hatch project (accession number 1014919), and Washington State University’s College of Agricultural, Human, and Natural Resource Sciences’ Emerging Research Issues competitive grant opportunity (ERI-20-04). The authors would like to thank Dr. Milton Valencia Ortiz and Kingsley Charles Umani for their support during the field camera installation

Contact Information:For further inquiries regarding this dataset, please contact Sindhuja Sankaran (s.sankaran@wsu.edu).