MonitorML overview

Learn more about Regrow’s in-house crop detection model

Regrow’s remotely sensed data is powered by MonitorML. MonitorML is Regrow’s in-house crop detection model, built on remote sensing technology. MonitorML is trained and validated using a combination of ground truth data collected from field surveys, grower reported data, and the USDA CDL. MonitorML also uses CDL in certain circumstances when satellite imagery is not applicable or available. Using this mix of public and high quality data for model training and evaluation, we are able to improve classification relative to CDL, especially in the crops most relevant to our customers. This provides better insights into practice adoption KPIs, such as cover crop and tillage. 

By leveraging machine learning for crop detection and advanced algorithms, Regrow can provide:

• Increased cropland coverage 
• Enhanced identification of fields with hay, improving programs involving livestock or silage 
• Enhanced detection of additional cover crops 
• High quality insights into field boundaries 
• Identification of non-agricultural land through Regrow’s Parcel ID model 

MonitorML has been trained to better delineate haylands from permanent grasslands and pasture , providing improved coverage in the Great Plains and the South, where these cropping patterns are prevalent. Unlike CDL, which often struggles to distinguish between haylands, temporary grasslands, and permanent grasslands— MonitorML is much more consistent in tracking these acres year over year, offering stable classifications for these land cover types. This stability enables more accurate tracking of grassland conversion into row crop production, which is vital for accurate land use monitoring and tracking changes in land use over time.

As we incorporate more data inputs, we continuously refine our ML models to provide targeted insights and emissions impact data, ensuring our customers receive the most relevant and up-to-date information.

How does the Sustainability Insights remote-sensed data compare to other data sets?

Like the USDA Ag Census, SI Monitor data reports on the adoption of regenerative practices across the United States. Outside of Sustainability Insights, the most comprehensive source of agricultural data across CONUS is the Census of Agriculture (Ag Census) conducted every five years by the USDA National Agricultural Statistics Service (NASS). The Ag Census attempts to survey all farmers, asking about their intention to implement regenerative practices within their operations. Regrow takes a different approach, using remote sensing models to identify fields that have successfully implemented cover cropping, reduced till, or no-till practices.

Many things can influence the success of a practice in a given year, including weather, pest and weed pressure, field workability, etc. Often a grower who intends to plant a cover crop may have limited success due to poor growing conditions post planting. In the Ag Census these acres would be counted as “cover crop”, while Regrow may not if the crop fails to thrive.

Due to this difference in the goals of each program, direct comparison of Ag Census data and Regrow data can be misleading. In general, we advise customers to use the data together, with the Ag Census and Regrow reporting on different elements of regenerative practice adoption:

•  The Ag Census provides a regular snapshot of grower intentions to implement cover cropping and conservation tillage. This is a leading indicator - as this number increases, we can infer growers are more comfortable with these practices as part of their operations, though they may still be learning how to successfully implement them. In general, it can take a grower five or more seasons to successfully integrate cover cropping and no till into their operation.
• Regrow provides an annual view of how successfully cover crops and conservation tillage have been implemented in a given year. This number will generally be smaller than the Ag Census (as not all intended practices are successfully implemented), and will naturally move up and down as conditions favorable to cover cropping and conservation tillage vary from year to year. Just as we see in commodity crop production, some years will have much higher or lower adoption rates due to variability in weather conditions and other factors.

Comparison of Ag Census 2022 and Regrow SI cover crop data

Comparison of Ag Census 2022 and Regrow SI Conservation Tillage data

Field Delineation

Delineated field boundaries that represent crop land are essential parameters for remote sensing, and serve as the basis for DNDC to quantify emissions of an area. 

Field Delineation: USDA CLUs vs. Regrow Generated Field Boundaries 

Regrow has developed an in-house field delineation model called Parcel ID, which generates precise field boundaries, crucial for evaluating key performance indicators, baseline emissions, and abatement potential in SI.

By using Regrow’s generated field boundaries, customers benefit from a higher level of precision compared to the less accurate legacy solutions provided by Common Land Units (CLUs), which lack comprehensive coverage, as detailed below. 

Benefits of using Regrow generated field boundaries:

Regrow's Parcel ID uses satellite imagery to accurately delineate agricultural field boundaries within a region. This is crucial for generating SI data, as it provides a detailed list of fields, which are processed through Regrow’s Monitor and Measure APIs. Parcel ID simplifies MRV farmer enrollment by pre-filling field boundaries, making it easier for farmers to select their fields. It also improves the accuracy of covered area identification and the exclusion of non-agricultural land. Unlike outdated CLUs, which can complicate farmer pre-fill and credit auditing in MRV programs, Regrow's Parcel ID offers precise and current field delineations, enabling more efficient credit auditing. 

In many regions outside the corn belt, we found CLUs often missed many agricultural fields, providing an incomplete picture of cropping practices. 

Comparison of CLUs (left) and Regrow parcels in Missouri (right)

Comparison of CLUs (left) and Regrow parcels in Wisconsin (right)

Comparison of CLUs (left) and Regrow parcels in Massachusetts (right)

What data validation framework does Regrow use for crop and practice determination models?

• Crop type models are trained on public data, validated on high quality data from growers, customers, and other 3rd party partners.
• Practice models are calibrated and tested at the field level using data from research partners & organizations, growers, and other 3rd party partners. We also compare our aggregate data to publicly reported data in EuroStat (eg NUTS 2 regional stats).