Agriculture: Crop Yield Prediction Workflow

PredictModel leverages the power of AWS to provide an advanced solution for predicting crop yields. This workflow helps farmers and agricultural businesses to optimize their operations, improve resource allocation, and maximize crop productivity.

Amazon S3
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AWS Glue
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Amazon Redshift
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Amazon SageMaker
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Amazon SNS

Workflow Step Explanation

  1. Data Storage: Amazon S3 stores historical weather data, soil data, and crop health imaging obtained via IoT sensors and drones.
  2. Data Processing: AWS Glue is utilized to perform ETL operations to clean, filter, and normalize the raw agricultural data.
  3. Data Warehousing: Amazon Redshift facilitates large-scale data warehousing, allowing comprehensive data analysis and complex queries.
  4. Machine Learning: Amazon SageMaker is used to develop, train, and deploy machine learning models to predict crop yields based on processed data.
  5. Notifications: Amazon SNS automates the dissemination of predictions and alerts to farmers and agricultural stakeholders through different channels.