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Remote Sens. 2018, 10(10), 1513; https://doi.org/10.3390/rs10101513

Evaluating Late Blight Severity in Potato Crops Using Unmanned Aerial Vehicles and Machine Learning Algorithms

1
Agrosavia, Corporación Colombiana de Investigación Agropecuaria—Agrosavia, C.I. Tibaitatá. Km 14, vía Mosquera-Bogotá, 250040 Cundinamarca, Colombia
2
Monsanto Group, 800 N. Lindbergh Boulevard, St. Louis, MO 63167, USA
*
Authors to whom correspondence should be addressed.
Received: 10 July 2018 / Revised: 12 September 2018 / Accepted: 17 September 2018 / Published: 21 September 2018
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Abstract

This work presents quantitative prediction of severity of the disease caused by Phytophthora infestans in potato crops using machine learning algorithms such as multilayer perceptron, deep learning convolutional neural networks, support vector regression, and random forests. The machine learning algorithms are trained using datasets extracted from multispectral data captured at the canopy level with an unmanned aerial vehicle, carrying an inexpensive digital camera. The results indicate that deep learning convolutional neural networks, random forests and multilayer perceptron using band differences can predict the level of Phytophthora infestans affectation on potato crops with acceptable accuracy. View Full-Text
Keywords: UAV; remote sensing; Phytophthora infestans; multispectral; neural networks; deep learning UAV; remote sensing; Phytophthora infestans; multispectral; neural networks; deep learning
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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
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Duarte-Carvajalino, J.M.; Alzate, D.F.; Ramirez, A.A.; Santa-Sepulveda, J.D.; Fajardo-Rojas, A.E.; Soto-Suárez, M. Evaluating Late Blight Severity in Potato Crops Using Unmanned Aerial Vehicles and Machine Learning Algorithms. Remote Sens. 2018, 10, 1513.

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