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Field Inventory Analytics

Delair.ai provides analytics to manage inventories in crops, plantations and forestry plots. Some features are related to the vectorization of asset (microplots, rows) in order to compute further analytics. This tutorial details how to run these analytics and read their results.


1. Required Inputs

This table shows what analytics shall be preliminarily calculated, plus field design information and other user input, that shall be provided to vectorize the agriculture assets, run inventories and calculate traits :

FIELD INVENTORY ANALYTICINPUTS
VECTORIZATIONSMicroplot Vectorization1. Base Scouting Maps2. Experimental design plan
Row Vectorization2. Field boundaries3. Row spacing
STATISTICS BY PLOTS
2. Microplots boundaries
GAPS & COUNT
2. Field Boundaries3. Interplant spacing and canopy diameter
NB: Row Vectorization is required and generated when running Gap & Count
TRAITS
Plant Height2. DTM (bare soil)3. Optional Field boundaries
Emergence Characterization2. Field or microplots boundaries3. Gaps & Count
Flowering Characterization2. Field or microplots boundaries
Stay Green
FCOVER

Read about currently supported agriculture assets :


2. Vectorizations

2.1. Microplot Vectorization

This analytic digitalizes and georeferences microplot boundaries, to delineate microplots to then measure, benchmark and monitor traits of varieties, evaluate trial responses at microplot level. Microplot customer id is linked to each microplot to enable seamless data analysis. It serves as the reference file for traits and trial responses data storage.

2.1.1. Inputs

  • Scouting Maps shall be computed first
  • Additional Input required from the customer is the experimental design plan (.xlsx format).

All microplots must have same design. The experimental design plan shall provide the trial breakdown :

INPUTS OF EXPERIMENTAL PLANDESCRIPTION
block_row_idcustomer row id of the microplot, within a block or a field
block_col_idcustomer column id of the microplot, within a block or a field
block_plot_idcustomer id of the microplot
block_idcustomer block or field name (optional)
number of rows per microplots
number of rows of analysis
plant spacing
row spacing
row length
alley size


EXPERIMENTAL DESIGN PLAN : AN EXAMPLE


IMPORTANT - EXPERIMENTAL DESIGN PLAN : MARKING THE FIELD TRIAL START

You have to indicate in your file the location of the field trial start. In the process to run the analytic (steps detailed at 2.1.6), you will have at some point to create an annotation on the RGB map that will correspond to this field trial start position :


2.1.2. Output File Formats

The microplot customer id is linked to each microplot to enable seamless data analysis ; linkage of traits and outcomes. Output formats are :

  • .geojson
  • .shp

2.1.3. Deliverables

The output contains these fields :

FIELDDESCRIPTION
parent_idblock or field Id
block_idcustomer -block or field name
block_row_idcustomer -row id of the microplot, within a block or a field
block_col_idcustomer column id of the microplot, within a block or a field
block_plot_idcustomer id of the microplot

2.1.4. Display

From layers left panel, select Microplot layer from SURVEY DATA section, inside Contours subgroup :

Outputs (id's) can be browsed from the information panel on right side :

2.1.5. How to run the analytic ?

Microplot vectorization can be run using either :

  • A field trial experimental plan from an excel file (standard workflow)
  • or a shapefile containing the microplots (in this alternative, it is highly recommended to also upload an experimental plan if any, as some adjustments may be required)

2.1.5.1. Standard Workflow

Step 1/9 - Ensure to have the base scouting maps processing completed in your site.

Step 2/9 - Open your site and start to upload of your experimental plan from UPLOAD FILES button on top bar :

Step 3/9 - Select to attach the experimental plan to All surveys of this site ; this will mount the experimental plan as a reference file :

Step 4/9 - Drag-and-drop or browse your experimental plan file :

Step 5/9 - Edit file Name if necessary, select Type : File and Category : Microplots / Plots. Then press UPLOAD :

Your experimental plan will be stored and made available under Downloads section :

Step 6/9 - Display the RGB layer from the layers panel at BASE LAYERS section. Create on this RGB map a point annotation to mark the position of the field trial start. Reminder : the field trial start should also have been marked in your experimental plan excel file (cf. 2.1.2.). You can optionally modify the annotation icon from the STYLE section :

Step 7/9 - Rename this point annotation to "Start of field trial" :

Step 8/9 - Go to Analytics section from the left panel, scroll to find Microplot Vectorization section, and press Run :

Step 9/9 - Select your experimental plan among the listed reference files then press Launch microplot vectorization button :

Once process is complete vectorization results becomes viewable as shown at section 2.1.5.

2.1.5.2. Alternative Workflow using a shapefile

This section will be documented soon.

2.2. Row Vectorization

This analytic automatically identifies rows within fields in order to assist with field counting or help guide farming equipment. It detects, digitizes and measures rows.

2.2.1. Inputs

This analytic is available for crops planted in row and where bare soil is visible in between the rows. Base scouting maps have to be computed first, theoretical field boundaries and row spacing must be provided.

2.2.2. Outputs File Formats

  • .geojson
  • .shp
  • .csv

2.2.3. Deliverables

Rows digitization with the following attributes :

  • row id
  • parent id (microplots) and vegetation length in case of microplots

2.2.4. Display

Open Contours belonging to SURVEY DATA section on left panel. Click any row on map to open its information panel on right side and read row id and parent id :


3. Statistics by plots

This analytic automatically extracts statistics metrics of scouting maps and traits at the microplot or plot level.

3.1. Inputs

The basic scouting maps have to been computed first, plus the microplots boundaries defined. Quantitative scouting maps, as CIR and RGB are out of scope.

3.2. Output File Formats

  • .geojson
  • .shp
  • .mbtiles
  • .csv

3.3. Deliverables

Statistics per microplots or plots are delivered for each selected scouting maps with the following attributes :

  • min
  • max
  • average
  • standard deviation 
  • variance

3.4. Display

Enable the statistic to be displayed from the Layers panel, and open right panel displaying properties.


4. Gaps & Count

This analytic automatically determines plant count and gaps through detection.

4.1. Inputs

  • Base scouting maps
  • Field or microplots boundaries
  • Rows for row crops and orchard and vineyards planted in rows
  • Interplant spacing
  • Canopy diameter (from above) 

It is mandatory to see bare ground in between two plants on the map, otherwise only visible gaps will be detected.

For row crops, with 6cm of GSD and a multispectral camera, optimal corn stage will be 4 leaves, and starbud for sunflower.

With enhanced resolution, earlier stages can be targeted. The basic rule is : size of GSD <= half canopy size (from above) 

For orchards and vineyards plants must be planted in rows. If canopy is closed, only gaps will be detected. Height of the plant must be > 50cm.

For palm trees, plant must be > 50 cm as well.

4.2. Output File Formats

  • .geojson
  • .shp
  • .csv

4.3. Deliverables

Gaps are only for plants in rows and are delivered with the following attributes : 

  • Gap length
  • Position of gaps at line end or not

Plant count are delivered with the following attributes:

  • Number of plant per microplot or per field
  • Number of missing plant per microplot or per field

4.4. Display

From layers panel, select Inventory layer from SURVEY DATA section , and select which count or gaps layers to display :

Opening the layer information panel on right side enables to modifiy the view for certain attributes. In example below we discriminate gaps that are inside the rows or located at their ends :


Continue here to Traits Analytics.

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