Monitor's Practice Detection Accuracy

To ensure reliable crop and practice detection, the accuracy of Monitor models is validated using a standardized process focused on the most commonly grown crops in each region. This targeted approach allows the model to be optimized for crops that have the greatest impact on agricultural planning and decision-making.

Accuracy is established by comparing the model’s predictions against ground truth data—verified information about which crops are actually growing in specific fields at a given time. This ground truth data typically comes from field surveys, farmer reports, or government records.

Rather than attempting to validate every field across a region, we focus on a representative subset of fields where reliable and accurate ground truth data is available. These selected fields serve as benchmarks for evaluating model performance. By assessing how often the model correctly identifies crop types in these known areas, we can quantify accuracy using standard metrics such as precision, recall, and F1 score, which give us an overall measure of accuracy.

This validation process is repeated in all regions where Monitor is available.

North America

Coming soon!

Europe

France

Crop detection

Crop type Crop detection accuracy Common Confusions*
Canola +95%  
Sugar beets +90%  
Winter wheat +95% Fallow, Triticale
Rice +95%  
Barley (winter) +90%  
Barley (spring) +80%  
Peas +90%  
Lentils +90%  
Potatoes +85%  
Sunflowers +90%  
Oats (spring) +85%  
Oats (winter) +80%  
Corn silage +85% Grain corn
Corn (grain) +85% Corn silage
Sorghum +85%  
Rye +80%  
*Common confusions refer to instances where the model frequently misclassifies one crop as another - often due to similar growth patterns, or comparable spectral signatures in the satellite imagery.

Germany, Belgium, Netherlands

Crop detection

Crop type Crop detection accuracy Common Confusions*
Canola +95%  
Sugar beets +95%  
Winter wheat +90% Triticale, Rye
Barley (winter) +90% Winter wheat
Barley (spring) +80% Oats
Peas +90%  
Potatoes +90%  
Oats (spring) +85% Barley, Spring wheat
Corn silage +85% Grain corn
Rye +85%  
Triticale +75% Rye
Soybean +70%  
*Common confusions refer to instances where the model frequently misclassifies one crop as another - often due to similar growth patterns, or comparable spectral signatures in the satellite imagery.

 

Australia 

Coming soon!