An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning - Filippi, Patrick; Jones, Edward J.; Wimalathunge, Niranjan S.; Somarathna, Pallegedara D. S. N.; Pozza, Liana E.; Ugbaje, Sabastine U.; Jephcott, Thomas G.; Paterson, Stacey E.; Whelan, Brett M.; Bishop, Thomas F. A. (Precision agriculture, 2019)
Keywords: Agricultural practices; Agricultural production; Agricultural research; Agriculture; Artificial intelligence; Atmospheric Sciences; Barley; Biomedical and Life Sciences; canola; case studies; Cereal crops; Chemistry and Earth Sciences; Computer Science; Correlation coefficient; Correlation coefficients; Crop yield; Crops; data collection; Data processing; Datasets; Decision trees; Electrical conductivity; Electrical resistivity; farmers; Farming systems; Farms; Fertilizer application; Grain; Grain crops; grain yield; large farms; Learning algorithms; Life Sciences; Machine learning; moderate resolution imaging spectroradiometer; Multilayers; Physics; Precision agriculture; prediction; Prediction models; rain; Rainfall; Remote Sensing/Photogrammetry; Seasons; soil; Soil Science & Conservation; Soil testing; space and time; Spatial resolution; Statistics for Engineering; surveys; Time series; time series analysis; Western Australia; Wheat; Western Australia; Precision agriculture; Yield forecast; Empirical yield prediction; Machine learning; Feature extraction; Random forest; Remote sensing