Crop Type Classification Using Satellite Imagery

We detect crop types historically back to 2016 as well as in the growth season, approx. 31-37 on Zadoks scale depending on the region and crop-type. In a Norwegian context we start run the model in the middle of July and achieve our best results in mid-August.

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Empowering Innovative Agriculture Companies

Crop Type Classification Using Satellite Imagery

Request Free Dataset

Empowering Innovative Agriculture Companies

Crop Type Classification Using Satellite Imagery

Request Free Dataset

Empowering Innovative Agriculture Companies

Global Coverage

We’re always working hard to bring new regions like France, Spain, Brazil, US, Australia, Germany and 30+ more.

Predictable Price

We've prepared a convenient system of discounts for increasing the volume of use. More requests - less price.

API-Ready

We provide full access to all our documentation and simple API / Add-ons endpoints for easy integration.

How do we classify all crops?

Crop Classification Model built on the baseline of crop specific yield-data. The alpha version we released in 2019 achieved a 83-87% accuracy based on single-pixel algorithm (Sentinel-1), while our latest model released we reached an accuracy of 92% based on object-based Sentinel-2 algorithm.

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Different Crops

Barley, rye, oats and wheat - the more diversity in crop specific data will increase overall accuracy.

Crop Training Data

Built on validated ground truth data from over 100k field level crop seeded data over a 5-year period.

High Accuracy

Our latest model reached 92% due to the increased accuracy of field delineation model.

Project Based

We deliver crop detection in the form of projects which fall under specificity difficulty levels.

Full Control of Data Delivery

Currently our crop classification product is produced in a project-basis, so it will be pre-processed for the area of interest and then made available through API queries.

Project types according to your request:

Cropped / Non Cropped Land. Based on extensive ground-data, as well as in the growth season;

Specific Crop Categories. Food crops, feed crops, fiber crops, oil crops, and industrial crops;

Specific Crops. Corn, wheat, barley, rice, rye, maize and many more.

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Our Success Projects

Challenge:

Crop Classification Model in Western Australia based on Wheat, Barley, Oats and Canola.

Result:

A prepared dataset with a trained model for selected crops, which revealed additional information for agricultural development.

Challenge:

Large-scale Crop Classification Model in India (grapes, onion and sugar cane).

Result:

A prepared dataset with a trained model for selected crops, which revealed additional information for agricultural development.

Challenge:

Small-scale Crop Classification Model in Thailand on sugar cane and rice paddies.

Result:

A prepared dataset with a trained model for selected crops, which revealed additional information for agricultural development.

Automatic crop classification contributes to sustainable agriculture practices and enhance food security.

Harvesting Optimization

Automatic crop detection can assist in optimizing the harvesting process by accurately determining the ripeness of crops. This ensures optimal timing for harvesting, reducing post-harvest losses and improving the quality of harvested produce.

Crop Insurance

Crop insurance companies can benefit from automatic crop detection technology as it provides accurate and objective data for assessing crop health and yield. This can help ensure accurate claims settlement and fair compensation.

Precision Agriculture

Optimize agricultural practices by providing valuable insights into crop health, growth, and yield estimation -> informed decisions regarding irrigation, fertilizer application, and pest control, leading to increased efficiency and reduced costs.

Crop Monitoring and Management

Crop detection technology can continuously monitor fields, detecting changes in crop health, growth, or pest infestations in real-time. This allows for timely intervention, preventing potential crop damage and ensuring maximum productivity.