Explore field scanalyzer multimodal phenomic data!
Introduction
The field scanalyzer at the University of Arizona Maricopa Agricultural Center is a multimodal phenotyping platform that travels along rails and captures images and point clouds of thousands of plants. These data are processed using PhytoOracle distributed processing pipelines. Given the size of raw data, all field scanalyzer data types are processed on the University of Arizona high performance computer cluster.
Figure 1. The field scanalyzer is an outdoor plant phenotyping platform at the University of Arizona Maricopa Agricultural Center.
Sensors enclosed within the sensor box include stereo RGB and thermal cameras, a PSII chlorophyll fluorescence imager, and a pair of 3D laser line scanners. All sensors collect data at the full field scale, except PSII chlorophyll fluorescence which collects data at the center of each agricultural plot.
Figure 2. (A) The field scanalyzer covers a 1 hectare field. (B) The platform collects RGB, thermal, PSII chlorophyll fluorescence, and 3D laser scanner data. (C) The raw data is sensor dependent, ranging from 5-350 GBs. All sensor data is captured at the full field scale, except for PSII chlorophyll fluorescence which captures data from the center of each agricultural plot. (D) Raw sensor data is temporarily stored on a cache server, where it is programmatically compressed and uploaded onto CyVerse. Compressed data is downloaded, processed, and outputs transferred on the UA high performance clusters.
Irrigation treatment & weather data
Figure 3. Volumetric water content (%) over the course of the growing period. For each collection, measurements were taken at depths 10, 30, 50, 70, and 90 cm. Each point represents the mean value of two measurements.Figure 4. Weather data throughout the growing period collected by the Arizona Meteorological Network (AZMET).
Test dataset
To download our numerical, tabular test dataset, click here. This dataset contains RGB, thermal, PSII chlorophyll fluorescence, and 3D line scanner phenotypic trait data. For a full description of the dataset, click here. The figures below show only those lettuce types included in the test dataset, although you can click on other lettuce types to see their trends by clicking on each figure’s legend.
To download our point cloud test dataset in an archived, compressed “tar.gz” format , click here. To access the same data in an uncompressed Google Drive folder, click here.
Mophological phenotypes
RGB
Figure 5. Bounding area time series showing plant development over the growing period. Errors bars represent the 95% CI around the mean. Means represent the phenotypic average of a lettuce type, including all genotypes and their respective replicates within a treatment.
3D laser scanner
Figure 6. Height time series showing plant development over the growing period. Errors bars represent the 95% CI around the mean. Means represent the phenotypic average of a lettuce type, including all genotypes and their respective replicates within a treatment.
Physiological phenotypes
Thermal
Figure 7. Canopy temperature over the growing period. Errors bars represent the 95% CI around the mean. Means represent the phenotypic average of a lettuce type, including all genotypes and their respective replicates within a treatment.
PSII chlorophyll fluorescence
Figure 8. Maximum quantum effiiency of PSII (FV/FM) over the growing period. Errors bars represent the 95% CI around the mean. Means represent the phenotypic average of a lettuce type, including all genotypes and their respective replicates within a treatment.
As sensor technology improves, data volumes grow. We now live in a sea of data collected by our phones, smartwatches, and home assistants like Alexa. Science is not any different, new sensors are enabling the collection of large datasets that can be mined for new scientific discoveries. In plant science, sensor technology is being applied to study how plants grow under drought conditions.
As sensor technology improves, data volumes grow. We now live in a sea of data collected by our phones, smartwatches, and home assistants like Alexa. Science is not any different, new sensors are enabling the collection of large datasets that can be mined for new scientific discoveries. In plant science, sensor technology is being applied to study how plants grow under drought conditions.
