Field Boundary Data Collection

We have developed a data collection application that can be used to mark boundaries of crop regions. Using the manually marked series of points by field agents along with GPS of a mobile phone, we generate the geo-coordinates of field. The geo-location can be used to extract satellite images for the marked region using our mapping API.


  • Accurate boundary mapping using on ground field team and mobile application
  • Logging of field data
  • Extraction of corresponding satellite data

image processing
Example of marked boundary by our filed agent using our mobile application
Farmer name Last crop name Date
Wasim Sugarcane  Ratoon 18-5-2023
Sample of field data captured by our field team

Crop Classification

 Manually Identification of crop can be done for small regions however this is not scalable for large areas.  Sentinel2 satellite images provide access to large amounts of data covering nearly all parts of the Earth. Concave Analytics offers state-of-the-art crop classification solutions that enable farmers, agronomists, and agricultural organizations to gain valuable insights and make data-driven decisions. Crop Classification has following benefits:

Using cutting-edge algorithms, we can identify and classify different crop types with high accuracy. Our models leverage satellite imagery to provide detailed and up-to-date information about crop distributions within a given area.

With accurate crop classification, you can precisely allocate resources such as water, fertilizers, and pesticides. By understanding the spatial distribution of different crops, you can tailor your resource management strategies, reducing waste and minimizing environmental impact

At Concave Analytics, we understand that each agricultural operation is unique. Therefore, we tailor our crop classification solutions to meet your specific needs.

crop image processing
Given above is the ground truth and our prediction for Sentinel2 satellite data for the region of France

Crop Health Monitoring

Periodic monitoring of large crop lands is not possible manually. Monitoring the health of crops is essential for early detection of diseases, nutrient deficiencies, or pest infestations. Our crop classification solutions can assess the condition of crops, enabling timely interventions to prevent yield loss and optimize treatment strategies. At Concave Analytics, we specialize in crop health monitoring, leveraging advanced analytics and remote sensing technologies to provide actionable insights for optimizing crop performance.

Process Image

NDVI Based Image Processing

Process Image

Comparative Analysis of Crop Health


  • Early Detection of Crop Stress: By analyzing multi-spectral satellite imagery, drone data, and other remote sensing inputs, we can detect subtle changes in vegetation indices and assess crop health.
  • Nutrient Deficiency Identification: Our analytics platform can identify and quantify nutrient deficiencies by analyzing spectral signatures and vegetation indices.
  • Pest and Disease Monitoring: By analyzing satellite imagery, we can identify crop stress patterns associated with pest infestations or disease outbreaks. This allows for targeted interventions, such as localized pesticide application or disease-resistant crop varieties, minimizing the spread and impact of harmful agents.
  • Temporal Analysis and Trend Monitoring: Our crop health monitoring solutions include temporal analysis, allowing you to track changes in vegetation vigor, growth patterns, and stress levels over the growing season. By comparing historical data and identifying trends, you can make data-driven decisions for optimizing future planting, irrigation, and crop protection strategies.
benefits of image processing

Ready to unlock the potential of your visual data with scale & precision?

Contact us today to leverage advanced image processing for actionable insights to help you grow your business.