Abstract
A first step in the process of automating weed removal in precision agriculture is the semantic segmentation of crops, weeds and soil. Deep learning techniques based on convolutional neural networks are successfully applied today and one of the most popular network architectures in semantic segmentation problems is U-Net. In this article, the variants in the U-Net architecture were evaluated based on the aggregation of residual and recurring blocks to improve their performance. For training and testing, a set of data available on the Internet was used, consisting of 60 multispectral images with unbalanced pixels, so techniques were applied to increase and balance the data. Experimental results show a slight increase in quality metrics compared to the classic U-Net architecture.
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Change history
25 March 2020
In the originally published version of the paper on p. 39 the authorship information was incorrect. The names and sequence of the authors have been corrected as “Pablo Torres-Carrión, Ruth Reátegui, Priscila Valdiviezo, Byron Bustamante and Silvia Vaca”.
In the originally published version of the paper on p. 473, the author’s full name was incorrect. The author’s name has been changed to “Ramon Alcarria”.
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Acknowledgments
This research was supported by National Agriculture Innovation Program (PNIA) of Peru and the Institute of Scientific Research (IDIC) of the University of Lima.
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Chicchón Apaza, M.Á., Monzón, H.M.B., Alcarria, R. (2020). Semantic Segmentation of Weeds and Crops in Multispectral Images by Using a Convolutional Neural Networks Based on U-Net. In: Botto-Tobar, M., Zambrano Vizuete, M., Torres-Carrión, P., Montes León, S., Pizarro Vásquez, G., Durakovic, B. (eds) Applied Technologies. ICAT 2019. Communications in Computer and Information Science, vol 1194. Springer, Cham. https://doi.org/10.1007/978-3-030-42520-3_38
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