CropID: Crop Detection methodology

Crop Type Detection

Crop Identification

Regrow’s crop detection model detects and identifies commodity crops growing on a field, at scale. Crop detection models are regionally tuned, and support the common crops grown in each region.

Crop detection is the foundation of all Monitor’s practice detection insights. The crop and growing period are directly used to evaluate the impact of tillage activity and cover cropping practices on a field.

Predicting Crop Type with Machine Learning Models

Regrow’s CropID is a machine learning model trained to detect and identify common crops in a field. It analyzes multi-spectral Sentinel 2 satellite data throughout the growing season to determine crop type based on historical data & crop-specific spectral signatures.

The model is trained on industry-standard datasets, including government estimates of previous crops, farmer-reported data and survey data collected through government and academic institutions. By learning the reflectance values for each crop, Crop ID can predict crop types just one to two months after harvest, and in some cases, even during the growing season as the model improves.

Detecting Crop Planting and Harvest

In addition to identifying crop types, Monitor's CropID ML model also predicts the planting and harvest dates.

The model is trained on both satellite imagery and vegetation indices, such as NDVI and EVI which measure the chlorophyll content in crops, and region-specific crop calendars, which account for local farming practices. Satellite images help detect when crops emerge and begin to die (senescence) within the expected crop calendar, offering detailed timing. Using annotated NDVI time series data, the ML model can predict emergence and senescence of the crop.

It's important to note that while Monitor refers to these as planting and harvest dates for simplicity, it's really detecting the crop's emergence and senescence. So the actual planting date may differ from the detected emergence, and harvest timing may vary depending on how much green vegetation remains after harvesting.

Crop Detection Confidence

Monitor's ML Crop ID model provides a confidence score alongside each crop type prediction, indicating how likely the prediction is correct. Confidence is influenced by the quality of remote sensing imagery, which can be affected by factors like cloud cover.

Accuracy + Model Validation

The accuracy and validation of the Crop ID model are crucial for ensuring it can classify a wide range of field conditions, from fully productive to partially managed and newly converted croplands. The model must also account for crop diversity, especially in regions with varied crop rotations, while avoiding bias in areas dominated by a few crops.

Regrow's Data Science team uses established techniques to validate predictions against ground truth data, analyzing metrics like precision, recall, F1 scores, and confusion matrices. Multiple datasets, including government and farmer-reported data, are used to ensure unbiased validation.