From e8cf37eb2668c664ad44ef96b6bfb1049faf0ae4 Mon Sep 17 00:00:00 2001 From: "Renato L. de F. Cunha" Date: Thu, 26 Oct 2023 15:48:00 -0300 Subject: [PATCH] Fix description in workflow list --- docs/source/docfiles/markdown/WORKFLOW_LIST.md | 18 ------------------ 1 file changed, 18 deletions(-) diff --git a/docs/source/docfiles/markdown/WORKFLOW_LIST.md b/docs/source/docfiles/markdown/WORKFLOW_LIST.md index 3e3e2e75..bd8a2b26 100644 --- a/docs/source/docfiles/markdown/WORKFLOW_LIST.md +++ b/docs/source/docfiles/markdown/WORKFLOW_LIST.md @@ -111,24 +111,6 @@ Below is a list of all available workflows within the FarmVibes.AI platform. For 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. - [`linear_trend/chunked_linear_trend` 📄](workflow_yaml/data_processing/linear_trend/chunked_linear_trend.md): Computes the pixel-wise linear trend of a list of rasters (e.g. NDVI).