Cover Crop Monitoring with RGB-Based Indices: A Low-Cost Solution for Farmers

Cover Crop Monitoring with RGB-Based Indices: A Low-Cost Solution for Farmers

Cover crops provide many benefits such as improving soil health, sequestering carbon, and potentially providing nitrogen credits. Accurately measuring these benefits has traditionally required labor-intensive sampling, expensive instrumentation, and technical expertise.

Recent research in the Runck Lab by Rosen et al. (2024) investigates how consumer-grade cameras and RGB (Red-Green-Blue) imaging can offer a low-cost and scalable alternative for estimating cover crop biomass and biochemical composition. The findings suggest that common digital and smartphone cameras can provide good estimates of vegetative ground cover, nitrogen content, and carbon-to-nitrogen (C:N) ratios.

Using RGB Indices to Monitor Cover Crops

In this study, different RGB color indices were tested using off-the-shelf cameras on medium red clover (Trifolium pratense L.), a common cover crop, to classify vegetation pixels and estimate biomass The four indices included were Excess Green (ExG), Excess Green minus Red (ExGR), Green Leaf Index (GLI), and Visible Atmospherically Resistant Index (VARI). The ExGR index with a preset threshold of zero was the most effective at correctly identifying plant pixels from the background 86.25% of the time. The research findings also included strong correlations between plant canopy coverage and biomass (R² = 0.554, RMSE = 219.29 kg ha⁻¹), as well as between vegetation index values and nitrogen content (R² = 0.573, RMSE = 3.5 g kg⁻¹) and C:N ratio (R² = 0.574, RMSE = 1.29 g g⁻¹). This method remained stable across varying lighting conditions, making it practical for field applications.

Practical Applications and Future Potential

This study highlights the potential of RGB-based sensing to provide accurate estimates of biomass and nitrogen content. By integrating these indices into digital agriculture platforms or mobile applications, farmers and researchers could better manage soil health, use less fertilizers, and scale up research efforts with less specialized equipment.
While further validation across different crops and environments is needed, this approach represents a promising addition to the growing suite of precision agriculture tools. By using low-cost and widely available technology RGB-based indices have the potential to make data-driven farming more accessible and sustainable.

Acknowledgments

This work was funded by the United States Department of Agriculture, GEMS Informatics Center’s Real-time Geoinformation Systems Lab, and the University of Minnesota MnDRIVE Global Food Ventures Faculty Scholars program.

Photo Credit: Wikimedia Commons