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Image-Based Wheat Fungi Diseases Identification by Deep plants Article Image-Based Wheat Fungi Diseases Identification by Deep Learning Mikhail A. Genaev 1,2,3, Ekaterina S. Skolotneva 1,2, Elena I. Gultyaeva 4 , Elena A. Orlova 5, Nina P. Bechtold 5 and Dmitry A. Afonnikov 1,2,3,* 1 Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia; [email protected] (M.A.G.); [email protected] (E.S.S.) 2 Faculty of Natural Sciences, Novosibirsk State University, 630090 Novosibirsk, Russia 3 Kurchatov Genomics Center, Institute of Cytology and Genetics, Siberian Branch of the Russian Academy of Sciences, 630090 Novosibirsk, Russia 4 All Russian Institute of Plant Protection, Pushkin, 196608 St. Petersburg, Russia; [email protected] 5 Siberian Research Institute of Plant Production and Breeding, Branch of the Institute of Cytology and Genetics, Siberian Branch of Russian Academy of Sciences, 630501 Krasnoobsk, Russia; [email protected] (E.A.O.); [email protected] (N.P.B.) * Correspondence: [email protected]; Tel.: +7-(383)-363-49-63 Abstract: Diseases of cereals caused by pathogenic fungi can significantly reduce crop yields. Many cultures are exposed to them. The disease is difficult to control on a large scale; thus, one of the relevant approaches is the crop field monitoring, which helps to identify the disease at an early stage and take measures to prevent its spread. One of the effective control methods is disease identification based on the analysis of digital images, with the possibility of obtaining them in field conditions, Citation: Genaev, M.A.; Skolotneva, using mobile devices. In this work, we propose a method for the recognition of five fungal diseases E.S.; Gultyaeva, E.I.; Orlova, E.A.; of wheat shoots (leaf rust, stem rust, yellow rust, powdery mildew, and septoria), both separately Bechtold, N.P.; Afonnikov, D.A. and in case of multiple diseases, with the possibility of identifying the stage of plant development. A Image-Based Wheat Fungi Diseases set of 2414 images of wheat fungi diseases (WFD2020) was generated, for which expert labeling was Identification by Deep Learning. performed by the type of disease. More than 80% of the images in the dataset correspond to single Plants 2021, 10, 1500. https:// disease labels (including seedlings), more than 12% are represented by healthy plants, and 6% of the doi.org/10.3390/plants10081500 images labeled are represented by multiple diseases. In the process of creating this set, a method was applied to reduce the degeneracy of the training data based on the image hashing algorithm. The Academic Editors: Pierre Bonnet, disease-recognition algorithm is based on the convolutional neural network with the EfficientNet Alexis Joly, Susan J Mazer and Nicolas Delhomme architecture. The best accuracy (0.942) was shown by a network with a training strategy based on augmentation and transfer of image styles. The recognition method was implemented as a bot on the Received: 20 April 2021 Telegram platform, which allows users to assess plants by lesions in the field conditions. Accepted: 15 July 2021 Published: 21 July 2021 Keywords: wheat; leaf rust; powdery mildew; septoria; stem rust; yellow rust; image recognition; deep learning; convolutional neural network; phenotyping Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. 1. Introduction Wheat is one of the world’s main crops and food sources for human consumption [1]. Wheat accounts for the largest planting area, and the food security of the population of most countries of the world depends on its yield. One of the main factors affecting Copyright: © 2021 by the authors. wheat yield is fungi diseases: rust, septoria of leaves and ears, and powdery mildew [2] Licensee MDPI, Basel, Switzerland. (Figure1). This article is an open access article Leaf rust, powdery mildew, and septoria (pathogens Puccinia triticina Erikss., Blumeria distributed under the terms and graminis (DC.) Speer, Zymoseptoria tritici Rob., and Parastaganospora nodorum Berk.) are conditions of the Creative Commons cosmopolitan pathogens. They are observed in the phytopathogenic complex on grain Attribution (CC BY) license (https:// crops widely. However, the total damage caused by the causative agents of the above-listed creativecommons.org/licenses/by/ diseases does not exceed 20% and is controlled by the timely use of fungicides [3]. The 4.0/). Plants 2021, 10, 1500. https://doi.org/10.3390/plants10081500 https://www.mdpi.com/journal/plants Plants 2021, 10, 1500 2 of 21 consequences of the epiphytoties of yellow and stem rust, leading to grain shortages of more than 30%, are of serious economic importance. In general, grain yield losses from these diseases, depending on the region and season conditions, can vary from 15 to 30% or more [4,5]. The most effective way to combat these diseases is their prevention and timely implementation of protective actions [4,6,7]. However, such a strategy is impossible without a timely and correct diagnosis of pathogens. In this case, it is important to identify diseases at the stage of seedlings, since at later stages of plant development, resistance to Plants 2021, 10, x FOR PEER REVIEW 2 of 21 the pathogen is higher [8]. In turn, the effectiveness of such diagnostics largely depends on how accurate, labor-intensive, and resource-intensive they are. FigureFigure 1. Examples of of digital images showing manifestations of of wheat wheat plan plantt disease: ( (aa)) leaf leaf rust rust,, ((b)) powdery powdery mildew mildew,, (c) septoria,septoria, (d) stem rust,rust, ((e)) yellowyellow rust,rust, andand ((ff)) multiplemultiple diseases.diseases. LeafVisual rust, assessment powdery of mildew, the diseases and septoria has been (pathogens the main diagnostic Puccinia methodtriticina throughoutErikss., Blume- the riahistory graminis of wheat (DC.) cultivation. Speer, Zymoseptoria It allows tritici to identify Rob., plaque,and Parastaganospora pustules, spots, nodorum or necrosis Berk.) [9 ,are10]. cosmopolitanIt requires the pathogens. training of They specialists are observed in phytopathology in the phytopathogenic and a lot of complex routine work,on grain in cropsaddition widely. to the However, observations the (keepingtotal damage a record caused book, by statistical the causative processing agents of of observations, the above- listedetc.). Indiseases recent does decades, not exceed molecular, 20% spectral and is control methods,led andby the methods timely baseduse of onfungicides the analysis [3]. Theof digital consequences images have of the appeared epiphytoties and haveof yellow become and widespread stem rust, leading [11,12]. to These grain approaches shortages ofdiffer more in than the labor30%, are intensity, of serious as well economic as in itsimportance. cost and accuracy.In general, The grain methods, yield losses using from the theseanalysis diseases, of digital depending RGB images, on the are reg basedion and on determining season conditions, changes can in thevary color, from texture, 15 to 30% and orshape more of [4,5]. plant The organs most that effective arise as way a result to combat of changes these in theirdiseases pigment is their composition prevention under and timelythe influence implement of theation vital of activity protective of pathogensactions [4,6, [137].– 17However,]. The advantages such a strategy of such is methodsimpossi- are the low cost of monitoring equipment (it is sufficient to use a digital camera or a mobile ble without a timely and correct diagnosis of pathogens. In this case, it is important to phone) and the high monitoring speed. The disadvantages include low sensitivity (in identify diseases at the stage of seedlings, since at later stages of plant development, re- comparison, for example, with spectral methods; see References [12,18]). sistance to the pathogen is higher [8]. In turn, the effectiveness of such diagnostics largely Recently the technologies for plant-disease monitoring based on digital RGB images depends on how accurate, labor-intensive, and resource-intensive they are. have received a powerful impulse due to the improvement of machine learning methods Visual assessment of the diseases has been the main diagnostic method throughout based on the use of neural network algorithms. A feature of deep learning neural networks the history of wheat cultivation. It allows to identify plaque, pustules, spots, or necrosis in comparison with other methods is the multilayer architecture of neurons, in which the [9,10]. It requires the training of specialists in phytopathology and a lot of routine work, next layer uses the output of the previous layer as input data to derive ideas regarding in addition to the observations (keeping a record book, statistical processing of observa- the analyzed objects [19,20]. For example, for such an important task as image labeling, tions, etc.). In recent decades, molecular, spectral methods, and methods based on the some of the most successful methods are convolutional neural networks (CNNs), for analysis of digital images have appeared and have become widespread [11,12]. These ap- which several architecture options are used. Among the first types of CNN architecture proacheswere AlexNet differ [in21 ]the and labor VGG intensity, [22]. Further as well development as in its cost ofand these accuracy. approaches The methods, made it usingpossible the to analysis improve of thedigital convergence RGB images, of the are proposed based on algorithmsdetermining (ResNet changes network) in the color, [23], texture,reduce theand number shape of of plant parameters organs due that to arise deep as convolution a result of changes (MobileNet in their network) pigment [24 ],com- and positionimprove under model the training influence results of duethe vital to adaptive activity recalibrationof pathogens of [13 responses–17]. The across advantages channels of such(SENet methods network) are [ 25the]. low These cost advances of monitoring have expanded equipment the (it applicability is sufficient of to deep use a learning digital cameraneural networks.or a mobile Moreover, phone) and they the have high also monitoring demonstrated speed.
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