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Deep convolutional neural network for damaged vegetation segmentation from RGB images based on virtual NIR-channel estimation

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Artificial Intelligence in Agriculture 2022年第1期6卷 199-210页

作者： Artzai Picon Arantza Bereciartua-Perez Itziar Eguskiza Javier Romero-Rodriguez Carlos Javier Jimenez-Ruiz till eggers Christian Klukas Ramon Navarra-Mestre TECNALIA Basque Research and Technology Alliance(BRTA)Parque Tecnológico de BizkaiaC/Geldo.Edificio 700E-48160 Derio-BizkaiaSpain University of the Basque Country Plaza Torres Quevedo48013 BilbaoSpain BASF Espanola S.L.Carretera A376 41710 Utrera SevillaSpain BASF SE Speyererstrasse 267117 LimburgerhofGermany

Performing accurate and automated semantic segmentation of vegetation is a first algorithmic step towards more complex models that can extract accurate biological information on crop health,weed presence and phenological state,among others.Traditionally,models based on normalized difference vegetation index(NDVI),near infrared channel(NIR)or RGB have been a good indicator of vegetation presence.However,these methods are not suitable for accurately segmenting vegetation showing damage,which precludes their use for downstream phenotyping algorithms.In this paper,we propose a comprehensive method for robust vegetation segmentation in RGB images that can cope with damaged vegetation.The method consists of a first regression convolutional neural network to estimate a virtual NIR channel from an RGB image.Second,we compute two newly proposed vegetation indices from this estimated virtual NIR:the infrared-dark channel subtraction(IDCS)and infrared-dark channel ratio(IDCR)indices.Finally,both the RGB image and the estimated indices are fed into a semantic segmentation deep convolutional neural network to train a model to segment vegetation regardless of damage or condition.The model was tested on 84 plots containing thirteen vegetation species showing different degrees of damage and acquired over 28 days.The results show that the best segmentation is obtained when the input image is augmented with the proposed virtual NIR channel(F1=0:94)and with the proposed IDCR and IDCS vegetation indices(F1=0:95)derived from the estimated NIR channel,while the use of only the image or RGB indices lead to inferior performance(RGB(F1=0:90)NIR(F1=0:82)or NDVI(F1=0:89)channel).The proposed method provides an end-to-end land cover map segmentation method directly from simple RGB images and has been successfully validated in real field conditions.

关键词： Vegetation indices estimation Vegetation coverage map Near infrared estimation Convolutional neural network Deep learning

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