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Carrot Yield Mapping: A Precision Agriculture Approach Based on Machine Learning

Department of Biosystems Engineering, “Luiz de Queiroz” College of Agriculture (ESALQ), University of Sao Paulo (USP), 11 Padua Dias Avenue, Piracicaba 13418-900, Brazil
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AI 2020, 1(2), 229-241; https://doi.org/10.3390/ai1020015
Received: 30 March 2020 / Revised: 14 May 2020 / Accepted: 22 May 2020 / Published: 23 May 2020
(This article belongs to the Special Issue Artificial Intelligence in Agriculture)
Carrot yield maps are an essential tool in supporting decision makers in improving their agricultural practices, but they are unconventional and not easy to obtain. The objective was to develop a method to generate a carrot yield map applying a random forest (RF) regression algorithm on a database composed of satellite spectral data and carrot ground-truth yield sampling. Georeferenced carrot yield sampling was carried out and satellite imagery was obtained during crop development. The entire dataset was split into training and test sets. The Gini index was used to find the five most important predictor variables of the model. Statistical parameters used to evaluate model performance were the root mean squared error (RMSE), coefficient of determination (R2) and mean absolute error (MAE). The five most important predictor variables were the near-infrared spectral band at 92 and 79 days after sowing (DAS), green spectral band at 50 DAS and blue spectral band at 92 and 81 DAS. The RF algorithm applied to the entire dataset presented R2, RMSE and MAE values of 0.82, 2.64 Mg ha−1 and 1.74 Mg ha−1, respectively. The method based on RF regression applied to a database composed of spectral bands proved to be accurate and suitable to predict carrot yield. View Full-Text
Keywords: horticultural crops; random forest regression; remote sensing; satellite imagery; spectral bands; yield estimation; yield forecast horticultural crops; random forest regression; remote sensing; satellite imagery; spectral bands; yield estimation; yield forecast
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Wei, M.C.F.; Maldaner, L.F.; Ottoni, P.M.N.; Molin, J.P. Carrot Yield Mapping: A Precision Agriculture Approach Based on Machine Learning. AI 2020, 1, 229-241.

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