Convolutional neural network in the recognition of spatial images of sugarcane crops in the troncal region of the coast of Ecuador

This article deals with the agriculture, as part of the primary sector of the economy, includes the transformation of the natural environment through a set of actions and human intervention that allows satisfying the production mainly of food and derived raw materials. Therefore, with the high demand for products required for agricultural activity, it is necessary to implement new technologies to guarantee the quality and performance of production techniques and reduce environmental impact. In this context, precision agriculture has emerged to improve, evaluate, estimate and understand, based on the information obtained, the needs of crops. Taking into account the evolution of the methodology for processing of satellite images and the obtaining of indirect data, a classification of the characteristics of crop yield can be made. Definitely, different techniques have been applied to the processing of satellite images, but recently a current approach, both in effectiveness and speed in obtaining excellent results is the use of deep learning of the convolution of neural networks. The deep convolution of neural networks is used both in the recognition as well as in the classification of the satellite images of the sugarcane plantation in the Troncal region of the Coast of Ecuador. The experiment showed affirmative results of approximately 95% probability of recognition of crop status. © 2019 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ( Peer-review under responsibility of the scientific committee of the 13th International Symposium “Intelligent Systems” (INTELS'18).

Cobeña Cevallos J.P. , Atiencia Villagomez J.M. , Andryshchenko I.S. 1
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Elsevier B.V.
  • 1 Peoples' Friendship University of Russia, RUDN University, Mikluho-Maklaya str., 6, Moscow, 117198, Russian Federation
Ключевые слова
Classification; Convolutional Neural Network; Image analysis; Machine Learning; Precision agriculture
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