Sensor-Based Bermudagrass Yield Prediction Models Using Random Forest Algorithm in Oklahoma
Gabriel Camargo de Campos Jezus | Lucas Freires Abreu | Daryl Brian Arnall | Lucas Martins Stolerman | Alexandre Caldeira Rocateli
The current available direct and indirect forage biomass estimation methods are prohibitive for producers because they are labor-intensive and time-consuming. Current literature states that (i) machine learning algorithms are promising in agriculture, and (ii) proximity and multispectral sensors can be employed to predict biomass. This research aimed to develop bermudagrass [<i>Cynodon dactylon</i> (L.) Pers.] biomass prediction models using the Random Forest regressor with laser, ultrasonic, multispectral sensors, precipitation, and N fertilization as input features. The prediction models—cultivar-specific and non-cultivar-specific—were developed using six bermudagrass cultivars, managed with four N rates, at four different locations, collecting data at 2, 4, and 6 weeks of bermudagrass regrowth (WOR) at two consecutive growing seasons (2018 and 2019). The 4 WOR, all-features, all-cultivars model had the highest performance when evaluating the model using ten-fold cross-validation (R<sup>2</sup> = 0.75, MAPE = 26.79%, RMSE = 1.0 Mg ha<sup>−1</sup>), with the laser having the highest feature importance score (65.5%). However, the Greenfield cultivar-specific model benefited from removing the laser and ultrasonic readings from the training dataset, achieving R<sup>2</sup> = 0.68, MAPE = 29.95%, RMSE = 0.82 Mg ha<sup>−1</sup>. Overall, the Random Forest regressor, proximity, and multispectral sensors proved to be efficient tools for developing effortless and efficient models to accurately predict bermudagrass biomass yield in Oklahoma.
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