MANTIS Ph.D. student Benjamin Ghansah has recently published an article in Computers and Electronics in Agriculture titled “Satellite vs Uncrewed Aircraft Systems (UAS): Combining High-Resolution SkySat and UAS images for Cotton Yield Estimation.” The manuscript explores how imagery from a high-resolution satellite system (SkySat) compares with imagery captured by UAS platforms for the purpose of estimating cotton yield. The dataset was collected over cotton fields in Texas during the 2023 growing season. A range of vegetation indices (including NDVI, GCI, MSR) were derived from both satellite and UAS images and used as inputs to a multilayer perceptron (MLP) deep-learning model, with observed yield data (lint + seed) serving as the response variable.
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The abstract reads as follows:
Uncrewed Aircraft Systems (UAS) are widely used for crop growth monitoring and yield estimation in Precision Agriculture (PA). However, UAS are limited by their relatively small area coverage, high cost, and high data processing needs. High resolution satellites (such as SkySat) are valuable alternatives to UAS in PA. Nonetheless, persistent cloud cover, especially in regions like the South of Texas, limits their utility. This study compared and explored the integration of satellite and UAS imagery for cotton yield estimation. The rationale was to determine the best performing platform among the two, as well as leverage their synergy to mitigate data gaps caused by persistent cloud cover. Using deep learning model, vegetation indices derived from SkySat and P4M (Phantom 4 Multispectral) images were correlated with crop yield data collected during the 2023 season. Results demonstrated that SkySat slightly outperformed P4M in yield estimation, with median accuracies of R2 = 0.81 and RMSE = 0.20 ton/ha for SkySat, compared to R2 = 0.80 and RMSE = 0.21 ton/ha for P4M. More importantly, when all the SkySat and P4M datasets were combined, accuracy improved by 3% compared to SkySat-only data. In addition, data collected between 74 and 114 days after planting contributed most significantly to yield prediction. The fusion approach used in this study allows for better spatial and temporal coverage, ultimately enhancing yield prediction reliability in PA. Future research should explore the inclusion of additional sensors such as Synthetic Aperture Radar (SAR) and thermal imagery, which could further improve yield prediction accuracy, especially in cloud-prone regions.
