Sync with prod branch

Co-authored-by: Bruno Silva <brunosilva@microsoft.com>
Co-authored-by: Eduardo Rodrigues <edrodrigues@microsoft.com>
Co-authored-by: Jessica Wolk <Jessica.Wolk@microsoft.com>
Co-authored-by: Naga Bilwanth Gangarapu <Naga@zensa.co>
Co-authored-by: Rafael Padilha <rpadilha@microsoft.com>
Co-authored-by: Renato Luiz de Freitas Cunha <renato.cunha@microsoft.com>
Co-authored-by: Rick Gutierrez <ricardgu@microsoft.com>

docs/source/docfiles/markdown/WORKFLOW_LIST.md

@@ -124,23 +124,6 @@ Below is a list of all available workflows within the FarmVibes.AI platform. For
 - [`heatmap/classification` 📄](workflow_yaml/data_processing/heatmap/classification.md): Utilizes input Sentinel-2 satellite imagery & the sensor samples as labeled data that contain nutrient information (Nitrogen, Carbon, pH, Phosphorus) to train a model using Random Forest classifier. The inference operation predicts nutrients in soil for the chosen farm boundary.
 
 
-The workflow generates a heatmap for selected nutrient. It relies on sample soil data that contain information of nutrients.
-The quantity of samples define the accuracy of the heat map generation. During the research performed testing with
-samples spaced at 200 feet, 100 feet and 50 feet. The 50 feet sample spaced distance provided results matching to the
-ground truth. Generating heatmap with this approach reduce the number of samples.
-It utilizes the logic below behind the scenes to generate heatmap.
-  - Read the sentinel raster provided.
-  - Sensor samples needs to be uploaded into prescriptions entity in Azure data manager for Agriculture (ADMAg). ADMAg is having hierarchy
-  to hold information of Farmer, Field, Seasons, Crop, Boundary etc. Prior to uploading prescriptions, it is required to build hierarchy and
-  a prescription_map_id. All prescriptions uploaded to ADMAg are related to farm hierarchy through prescription_map_id. Please refer to
-  https://learn.microsoft.com/en-us/rest/api/data-manager-for-agri/ for more information on ADMAg.
-  - Compute indices using the spyndex python package.
-  - Clip the satellite imagery & sensor samples using farm boundary.
-  - Perform spatial interpolation to find raster pixels within the offset distance from sample location and assign the value of nutrients to group of pixels.
-  - Classify the data based on number of bins.
-  - Train the model using Random Forest classifier.
-  - Predict the nutrients using the satellite imagery.
-  - Generate a shape file using the predicted outputs.
 
 - [`index/index` 📄](workflow_yaml/data_processing/index/index.md): Computes an index from the bands of an input raster.