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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 (field, microplots) in order to compute further analytics.

1. Required Inputs

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

ANALYTICINPUTS
VECTORIZATIONSMicroplot Vectorization1. Base Scouting Maps2. Experimental design plan
Field Vectorization2. Rough field boundaries to localize field in orthomap
Row Vectorization2. Field boundaries3. Row spacing
STATISTICS BY PLOTS
2. Microplots boundaries
GAPS & COUNT
2. Field Boundaries3. Rows for row crops, orchards and vineyards planted in rows, with interplant spacing and canopy diameter
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

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 - EXAMPLE

2.1.2. Outputs

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

2.1.3. Deliverables

The output format is geojson vector format. It contains :

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 panel, select Microplot layer from SURVEY DATA section :

2.2. Field Vectorization

This Analytic serves to digitalize the exact contour of cropped area within a field to know precisely the geolocation of field boundaries and its cropped surface.

2.2.1. Inputs

  • Scouting Maps shall be computed first
  • Field boundaries need to be distinguishable from the sky (tracks, roads, alleys, bare soil). Input to provide are the rough boundaries of the field, to know where is the field in the orthomosaic.

2.2.2. Output File Formats

  • .geojson
  • .shp
  • .csv

2.2.3. Deliverables

The field boundaries with the field area as an attribute.

2.2.4. Display

Enable Field Boundaries from the Layers panel.

2.2. Row Vectorization

This analytic automatically identifies rows within fields in order to assist with field counting or help guide farming equipment.

2.2.1. Inputs

This analytic is available for crops planted in row and where bare soil is visible in between the rows. The base scouting maps have to be computed first, theoretical boundaries of field 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 then Row vectorization subgroups of layers 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 around scouting maps and traits at the microplot or plot level (min, max, average, standard deviation, variance).

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 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.

4.1. Inputs

  • 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 (ie: plant width seen from above) 
  • For orchard / vineyard: plant must be planted in row. if canopy is closed, only gaps will be detected. Height of the plant must be > 50cm
  • For palm trees, plant must be > 50 cm

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

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 :

5. Traits

5.1. Plant Height

This analytic automatically determines plant height for phenotyping or volume estimation in forestry.

5.1.1. Inputs

  • Basic scouting maps
  • If no bare soil survey is already available, an area of bare soil must be visible in the survey (such as alleys or tracks).
  • Plants should be taller than 50 cm
  • Overlap and sidelap shall be of mininum 80%

5.1.2. Output File Formats

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

5.1.3. Deliverables

  • Vegetation height map
  • Extrapolated DTM if no bare soil option selected
  • Statistics at field / microplots level if option selected

5.1.4. Display

Open Inventory then Plant height subgroups of layers belonging to SURVEY DATA section on left panel. Enable Vegetation height layer, click it to open its information panel on right side to read distibutionor pick a value in any point of the map :

5.2. Emergence Characterization

This analytic automatically calculates the percentage of green (or leaves) to characterize seedling vigor — suitable for early stage crops.

5.2.1. Inputs

5.2.2. Output File Formats

  • .tif
  • .geojson
  • .shp
  • .csv

5.2.3. Deliverables

  • Emergence mask
  • Emergence % per microplot

5.2.4. Display

Open Inventory then Emergence characterization subgroups of layers belonging to SURVEY DATA section on left panel. Emergence % per microplot is displayed under Emergence ratio layer, click it to open its information panel on right side to read value chart :

The Emergence mask belongs to the same layers subgroup :

5.3. Flowering Characterization

This analytic automatically calculates the percentage of flowering vs. no-flowering — currently suitable for row crops with yellow flowers such as canola and sunflowers.

5.3.1. Flowering Characterization Inputs

5.3.2. Output File Formats

  • .tif
  • .geojson
  • .shp
  • .csv

5.3.3. Deliverables

  • Flowering mask
  • Flowering % per microplot

5.3.4. Display

Open Inventory then Flowering characterization subgroups of layers belonging to SURVEY DATA section on left panel. Flowering % per microplot is displayed under Flowering % layer, click it to open its information panel on right side to read value chart :

Flowering mask is under the same layer subgroup ; Value at point can notably be used to read value in any point of the map.

5.4. Stay Green

This analytic provides an automatic assessment of the ability for the crop to remain as a green growing plant late in the season.

5.4.1. Inputs

5.4.2. Output File Formats

  • .tif
  • .geojson
  • .shp
  • .csv

5.4.3. Deliverables

  • Stay green mask
  • Stay green % per microplot

5.4.4. Display

Open Inventory then Stay green subgroups of layers belonging to SURVEY DATA section on left panel. Stay green % per microplot is displayed under Stay green % layer, click it to open its information panel on right side to read value chart :

Stay green mask is under the same layer subgroup ; Value at point can notably be used to read value in any point of the map.

5.5. FCOVER

FCOVER, standing for Fraction of green vegetation COVER, is a measurement of the fraction of ground covered by green vegetation. It therefore expresses the ratio between biomass and soil, between these two extrema :

0 = only soil

1 = we only see biomass

Practically, it quantifies the spatial extent of the vegetation.

5.5.1. Inputs

5.5.2. Output File Formats

  • .tif
  • .geojson
  • .shp
  • .csv

5.5.3. Deliverables

  • FCOVER per microplot (Fcover %)
  • Biomass mask (Fcover mask)

5.5.4. Display

Open Inventory then Fraction of vegetation cover subgroups of layers belonging to SURVEY DATA section on left panel. FCOVER per microplot is displayed under Fcover % layer, click it to open its information panel on right side to read value chart :

Biomass mask is under the same layer subgroup ; Value at point can notably be used to read value in any point of the map.

Learn more with Weed Management Analytics.

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