Training Machine Learning Algorithms Using Remote Sensing and Topographic Indices for Corn Yield Prediction

• Published in: Remote sensing (Basel, Switzerland) Vol. 14; no. 23; p. 6171
• Main Authors: Oliveira, Mailson Freire de, Ortiz, Brenda Valeska, Morata, Guilherme Trimer, Jiménez, Andrés-F, Rolim, Glauco de Souza, Silva, Rouverson Pereira da
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01-12-2022
• Subjects: Accuracy; Agricultural production; Algorithms; Artificial intelligence; auto-machine learning; Band spectra; Biomass; Climate change; Corn; Crop yield; digital agriculture; Flowering; Forecasting; Growth models; Growth stage; Harvesting; Hydrology; Learning algorithms; Machine learning; Mathematical models; Methods; Moisture content; Neural networks; Precipitation; predictive models; Productivity; Radiation; Remote sensing; site-specific model; Software; Spectral bands; Support vector machines; Topography; Vegetables; Zea mays L
• ISSN: 2072-4292; 2072-4292
• DOI: 10.3390/rs14236171

Methods using remote sensing associated with artificial intelligence to forecast corn yield at the management zone level can help farmers understand the spatial variability of yield before harvesting. Here, spectral bands, topographic wetness index, and topographic position index were integrated to predict corn yield at the management zone using machine learning approaches (e.g., extremely randomized trees, gradient boosting machine, XGBoost algorithms, and stacked ensemble models). We tested four approaches: only spectral bands, spectral bands + topographic position index, spectral bands + topographic wetness index, and spectral bands + topographic position index + topographic wetness index. We also explored two approaches for model calibration: the whole-field approach and the site-specific model at the management zone level. The model’s performance was evaluated in terms of accuracy (mean absolute error) and tendency (estimated mean error). The results showed that it is possible to predict corn yield with reasonable accuracy using spectral crop information associated with the topographic wetness index and topographic position index during the flowering growth stage. Site-specific models increase the accuracy and reduce the tendency of corn yield forecasting on management zones with high, low, and intermediate yields.