Create a Unified Data Set with Local, Historical, and Third-Party Data
OUR OBJECTIVE

Take existing client collected local data and instrument to allow for advanced analytics 

Our client had significant amounts of locally collected data like local grove maps, site soil maps, and tree inventory, in addition to local weather, soil IoT, and spectral sensors. However, these individual data sources were not connected together in a meaningful way or down to the individual plant. They wanted to utilize their existing data to allow for advanced Plant Science analytics.


Client Provided Raw Data:

 

 

 

 

 

 

 

 

OUR APPROACH

Digitized, collected, and fused all data sets utilizing the AgLayers Data Science Cloud
Not only did we digitize our client’s existing historical data, we expanded with our own multispectral data collects and supplemented with local weather, soil IoT, and spectral sensors.

 

At this point, we established geo-rectified blocks to create a database of each individual tree asset and further analyzed by creating a foundation Morphology object. With the variety of independent data sets digitized and geo-rectified, we were able to fuse the data sets together to create meaningful views for further analysis.

 

OUR RESULTS

A unified data set that can be extended and improved with supplementary data

With this complete data set, our client’s Plant Scientists and study yield versus local farm management techniques and applied treatments to optimize performance.

Geo-Rectified Tree Asset Objects with Fused Datasets:

 

 

 

 

 

 

 

 

DIGITIZE
ANALYZE
COLLABORATE
1 – Digitize existing client provided historical data:

  • Local Grove Maps
  • Site Soil Maps
  • Tree Inventory
4 – Establish Geo-Rectified Blocks:

  • Established geo-rectified database of each tree asset
8 –Collaborate

  • Produce complete asset and tree inventory
  • Develop analytics models
2 – Insight-specific data collects:

  • VitalityTracker – Multispectral, (client collect)
5 – Produce Morphologies

  • Created foundation Morphology object
SYNTHESIZE
3 – Bring Your Own Data (BYOD):

  • Local Weather Sensors
  • Soil IoT Sensors
  • Spectral Sensors
6 – Fuse & geo-rectify data sets:

  • Fused local soil data
  • Fused by tree hand-collected yield data
  • Fused multispectral satellite data
  • Fused Soil Moisture IoT Data
Synthesize

  • Soil A has higher yield than Soil B
  • Yield by vegetative characteristic
  • Yield by soil moisture 
7 – Metrics by layer

  • Yield by Soil Type
  • Yield by vegetative characteristic
  • Yield by soil moisture