Oil Palm Yield Estimation Based on Vegetation and Humidity Indices Generated from Satellite Images and Machine Learning Techniques
نویسندگان
چکیده
Palm oil has become one of the most consumed vegetable oils in world, and it is a key element profitable global value chains. In Costa Rica, palm cultivation three crops with largest occupied agricultural area. The objective this study was to explain predict yield safe time lags for production management by using free-access satellite images. To end, machine learning methods were performed 20-year data set an plantation located Central Pacific Region Rica corresponding vegetation indices obtained from LANDSAT Since best correlations corresponded one-year lag, predictive models Random Forest (RF), Least Absolute Shrinkage Selection Operator (LASSO), Extreme Gradient Boosting (XGBoost), Recursive Partitioning Regression Trees (RPART), Neural Network (NN) built Time-lag 1. These applied all genetic material predominant variety (AVROS) separately. While NN showed performance multispecies information (r2 = 0.8139, NSE 0.8131, RMSE 0.3437, MAE 0.2605), RF better fit AVROS 0.8214, 0.8020, 0.3452, 0.2669). relevant (NDMI, MSI) are related water plant. also determined that distribution must be considered prediction evaluation area under study. estimation provide on identification important variables (NDMI) characterize yield. Additionally, generates scenario acceptable uncertainties forecast year advance. This direct interest industry.
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ژورنال
عنوان ژورنال: AgriEngineering
سال: 2022
ISSN: ['2624-7402']
DOI: https://doi.org/10.3390/agriengineering4010019