{"id":2539,"date":"2021-11-10T18:59:44","date_gmt":"2021-11-10T17:59:44","guid":{"rendered":"https:\/\/dev.littlebigcode.fr\/cas-client\/rendement-des-champs-agricoles\/"},"modified":"2021-11-17T09:48:35","modified_gmt":"2021-11-17T08:48:35","slug":"yield-agricultural-fields","status":"publish","type":"cas-client","link":"https:\/\/dev.littlebigcode.fr\/en\/cas-client\/yield-agricultural-fields\/","title":{"rendered":"Yield of agricultural fields"},"content":{"rendered":"<style>\nh3 {color : #1CACE4}<br \/><\/style>\n<h3><strong>Contexte and problematic<\/strong><\/h3>\n<p><\/p>\n<p>Axa Climate is launching a parametric insurance offer which includes drought insurance dedicated to European farmers. In order to provide this insurance, Axa needs to predict field rates of return primarily from soil moisture levels. We enrich this data with multiple Open Data sources: Raster, shapefiles, era5, copernicus, etc..<\/p>\n<p><\/p>\n<h3><strong>Goals<\/strong><\/h3>\n<p><\/p>\n<p>In the area of Germany :<br \/>\nEnrich internal data with Open Data<br \/>\nPr\u00e9edict the yield throught the soil moisture rate by region and by crop (regression problem)<br \/>\nDectection of drought years by region (classification problem)<\/p>\n<h3><\/h3>\n<p><\/p>\n<h3><strong>Our intervention<\/strong><\/h3>\n<p><\/p>\n<p>2 Data Scientist, 1 Lead Data Scientist<\/p>\n<ul>\n<li>Data collection from internal sources (Axa business partners)<\/li>\n<li>Open Data collection: shapefiles, era5 copernicus, etc.<\/li>\n<li>Preparation and transformation of multi-source aggregated data<\/li>\n<li>Dataset rebalancing<\/li>\n<li>Modeling and benchmarking of neural networks, LSTM, Random Forest, Gradient Boosting, LGBM, etc.<\/li>\n<li>Selection and deployment of the model with the best performance: Random Forest<\/li>\n<li>Interoperability of the model through heatmaps on geographic maps<\/li>\n<\/ul>\n<p><\/p>\n<h3><strong>R\u00e9sults<\/strong><\/h3>\n<p><\/p>\n<p>Enrichment of data by variables such as soles (raster), delimitation of regions (shapefile) and meteorological variables (era5)<br \/>\nRegression problem: RMSE ~ 30 for values of around 400 (tons \/hectare) (tonnes\/hectare)<br \/>\nClassification problem: accuracy = 0.88 \/ precision = 0.74 \/ recall = 0.61<\/p>\n<p><\/p>\n<h3><strong>Technical environment<\/strong><\/h3>\n<p><\/p>\n<p>Python &#8211; geoPandas &#8211; RasterIO &#8211; Shapely Jupyter &#8211; Pyzo<br \/>\nSk-learn &#8211; Tensorflow &#8211; Keras<br \/>\nAPI Rest<br \/>\nGCP \u2013 AWS<\/p>\n","protected":false},"excerpt":{"rendered":"<p>Contexte and problematic Axa Climate is launching a parametric insurance offer which includes drought insurance dedicated to European farmers. In order to provide this insurance, Axa needs to predict field rates of return primarily from soil moisture levels. We enrich this data with multiple Open Data sources: Raster, shapefiles, era5, copernicus, etc.. Goals In the [&hellip;]<\/p>\n","protected":false},"author":2,"featured_media":1623,"comment_status":"closed","ping_status":"open","template":"","meta":{"_et_pb_use_builder":"","_et_pb_old_content":"","_et_gb_content_width":""},"acf":[],"yoast_head":"<!-- This site is optimized with the Yoast SEO plugin v19.7.1 - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Yield of agricultural fields | LittleBigCode.fr<\/title>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/dev.littlebigcode.fr\/en\/cas-client\/yield-agricultural-fields\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Yield of agricultural fields | LittleBigCode.fr\" \/>\n<meta property=\"og:description\" content=\"Contexte and problematic Axa Climate is launching a parametric insurance offer which includes drought insurance dedicated to European farmers. In order to provide this insurance, Axa needs to predict field rates of return primarily from soil moisture levels. We enrich this data with multiple Open Data sources: Raster, shapefiles, era5, copernicus, etc.. 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In order to provide this insurance, Axa needs to predict field rates of return primarily from soil moisture levels. We enrich this data with multiple Open Data sources: Raster, shapefiles, era5, copernicus, etc.. 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