Cloud-Based Precision Agriculture Platform
Leveraging Satellite and Drone Data for Precision Agriculture to Boost Field Productivity
problem
The client is a cloud-based precision agriculture platform service that leverages satellite data to provide intelligent analytical decision-support tools for monitoring, understanding, and optimizing field yield and crop performance. The modern agricultural industry demands increased land productivity while minimizing production costs, requiring reliable yet cost-effective data collection methods to enhance yields. To address these challenges, the client sought Interactivated team s expertise. The goal was to assist individual farmers and agricultural organizations in measuring agricultural growth performance at the field level using satellite and drone data to generate vegetation indices for field status analysis.
problem
The client is a cloud-based precision agriculture platform service that leverages satellite data to provide intelligent analytical decision-support tools for monitoring, understanding, and optimizing field yield and crop performance. The modern agricultural industry demands increased land productivity while minimizing production costs, requiring reliable yet cost-effective data collection methods to enhance yields. To address these challenges, the client sought Interactivated team s expertise. The goal was to assist individual farmers and agricultural organizations in measuring agricultural growth performance at the field level using satellite and drone data to generate vegetation indices for field status analysis.
solution
Our solution involved the development of two main components. First, we created a service hosted on Amazon EC2 that automatically identifies the latest datasets from the Landsat Sentinel satellite. This service extracts areas of interest, calculates the Normalized Difference Vegetation Index (NDVI), and generates rasters from the data. Second, we developed a web application that displays the collected statistics, integrating Google Maps with an analytics platform and geospatial measurement tools. Additionally, the service was connected to Google Earth Engine s statistical data for enhanced analytics. Technologically, the back-end was built using Python, with PostgreSQL as the database. AWS Lambda was used for data computing, and the service was deployed on AWS infrastructure, utilizing GDAL for geospatial data translation. Other technologies included MBTiles, JavaScript, and PyQt.
solution
Our solution involved the development of two main components. First, we created a service hosted on Amazon EC2 that automatically identifies the latest datasets from the Landsat Sentinel satellite. This service extracts areas of interest, calculates the Normalized Difference Vegetation Index (NDVI), and generates rasters from the data. Second, we developed a web application that displays the collected statistics, integrating Google Maps with an analytics platform and geospatial measurement tools. Additionally, the service was connected to Google Earth Engine s statistical data for enhanced analytics. Technologically, the back-end was built using Python, with PostgreSQL as the database. AWS Lambda was used for data computing, and the service was deployed on AWS infrastructure, utilizing GDAL for geospatial data translation. Other technologies included MBTiles, JavaScript, and PyQt.
results
We successfully developed a platform that addresses everyday agricultural challenges by analyzing satellite imagery. The platform processes satellite data suitable for various crop analytics, providing extensive coverage of the agricultural areas and handling anomalies such as nebulosity and overcast. Depending on the location, crop and field performance information is updated every 3-5 days, enabling farmers to analyze different indices to boost field productivity. As a result, the solution effectively assisted individual farmers and organizations in overcoming common agrarian challenges and evaluating yield performance using satellite and drone data. Furthermore, it helped our client navigate the barriers to entering a more intelligent business market and facilitated the scaling up of their business model.
results
We successfully developed a platform that addresses everyday agricultural challenges by analyzing satellite imagery. The platform processes satellite data suitable for various crop analytics, providing extensive coverage of the agricultural areas and handling anomalies such as nebulosity and overcast. Depending on the location, crop and field performance information is updated every 3-5 days, enabling farmers to analyze different indices to boost field productivity. As a result, the solution effectively assisted individual farmers and organizations in overcoming common agrarian challenges and evaluating yield performance using satellite and drone data. Furthermore, it helped our client navigate the barriers to entering a more intelligent business market and facilitated the scaling up of their business model.
tech stack
tech stack
