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Assessing the potential of UAV Spectral Data and Machine learning for Soil Organic Carbon Prediction in Sorghum Fields
  • +4
  • Boubacar Gano,
  • Jocelyn Saxton,
  • Marie De Gracia Coquerel,
  • Nathaniel Eck,
  • Jaccob Stanton,
  • Nurzaman Ahmed,
  • Nadia Shakoor
Boubacar Gano
Donald Danforth Plant Science Center

Corresponding Author:[email protected]

Author Profile
Jocelyn Saxton
Donald Danforth Plant Science Center
Marie De Gracia Coquerel
Donald Danforth Plant Science Center
Nathaniel Eck
Donald Danforth Plant Science Center
Jaccob Stanton
Donald Danforth Plant Science Center
Nurzaman Ahmed
Donald Danforth Plant Science Center
Nadia Shakoor
Donald Danforth Plant Science Center


Soil Organic Carbon (SOC) fluctuations in agricultural fields play a critical role in determining soil fertility and carbon sequestration. Efficient, non-destructive monitoring of SOC is essential. This study utilized Unmanned Aerial Vehicles (UAVs) and four Machine Learning (ML) algorithms-Multiple Linear Regression (MLR), Partial Least Squares Regression (PLSR), Random Forest (RF), and Xtreme Gradient Boosting (XGB)-to predict SOC levels.
Our research, set at the Danforth Center Field Research Site (FRS), comprised 35 sorghum genotypes in a complete block design. Using a GeoProbe drilling machine, we collected soil cores for compositional analysis through the Haney test. Concurrently, drone-captured multispectral images were processed to create orthomosaic maps and extract features. The ML algorithms effectively predicted SOC levels, with MLR models showing the highest accuracy (RMSE = 7.12 ppm; R = 0.74). We observed variation in SOC among genotypes, suggesting that genotype-specific traits could influence SOC estimation accuracy. Field plots planted with genotypes like SC1345, BTx623, and SAP-133 showed strong predictability with errors below 5%. In contrast, SC283 and SAP-154 had prediction errors exceeding 30%.
UAV-based remote sensing offers promising avenues for soil health assessment, contributing to precision agriculture and sustainable land management. Future studies could benefit from integrating hyperspectral sensors to fully harness this technology's potential.
30 Oct 2023Submitted to NAPPN 2024
30 Oct 2023Published in NAPPN 2024