Reduction of incidence and severity of Septoria lycopersici leaf spot of tomato with bacteria and yeasts

Blum, Luiz Eduardo Bassay

doi:10.1590/s0103-84782000000500003

Cited by 9 publications

(4 citation statements)

References 17 publications

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“…The raw tomato leaf dataset utilized in this work comes from an open access repository of images, which focus on plant health [43]. Health and other 8 diseases categories are included (see Table 1, Figure 2), i.e., early blight (pathogen: Alternaria solani) [1], yellow leaf curl disease (pathogen: Tomato Yellow Leaf Curl Virus (Tylcv), Family Geminiviridae, Genus Begomovirus) [2], corynespora leaf spot disease (pathogen: Corynespora cassiicola) [3], leaf mold disease (pathogen: Fulvia fulva) [4], virus disease (pathogen: Tomato Mosaic Virus) [5], late blight (pathogen: Phytophthora Infestans) [6], septoria leaf spot (pathogen: Septoria lycopersici) [7], and two-spotted spider mite (pathogen: Tetranychus urticae) [8]. The total dataset is 5550.…”

Section: Raw Datasetmentioning

confidence: 99%

“…Given the importance of tomato in the economic context, it is necessary to maximize productivity and product quality by using techniques. Corynespora leaf spot disease, early blight, late blight, leaf mold disease, septoria leaf spot, two-spotted spider mite, virus disease, and yellow leaf curl disease are 8 common diseases in tomato [1][2][3][4][5][6][7][8]; thus, a real time and precise recognition technology is essential.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Can Deep Learning Identify Tomato Leaf Disease?

Zhang

Liu

et al. 2018

Advances in Multimedia

147

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This paper applies deep convolutional neural network (CNN) to identify tomato leaf disease by transfer learning. AlexNet, GoogLeNet, and ResNet were used as backbone of the CNN. The best combined model was utilized to change the structure, aiming at exploring the performance of full training and fine-tuning of CNN. The highest accuracy of 97.28% for identifying tomato leaf disease is achieved by the optimal model ResNet with stochastic gradient descent (SGD), the number of batch size of 16, the number of iterations of 4992, and the training layers from the 37 layer to the fully connected layer (denote as “fc”). The experimental results show that the proposed technique is effective in identifying tomato leaf disease and could be generalized to identify other plant diseases.

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Section: Raw Datasetmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Can Deep Learning Identify Tomato Leaf Disease?

Zhang

Liu

et al. 2018

Advances in Multimedia

147

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“…diseases [8]. Diseases and insect pests are the key factors that affect the growth of tomato, so it is particularly significant to study the identification of crop diseases [9].…”

Section: Introductionmentioning

confidence: 99%

DCGAN-Based Data Augmentation for Tomato Leaf Disease Identification

2020

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Tomato leaf disease seriously affects the yield of tomato. It is extremely vital for agricultural economy to identify agricultural diseases. The traditional data augmentation methods, such as rotation, flip and translation, are severely limited, which cannot achieve good generalization results. To improve the recognition accuracy of tomato leaf diseases, a new method of data augmentation by generative adversarial networks (GANs) is proposed for leaf disease recognition in this work. Generated images augmented by deep convolutional generative adversarial networks (DCGAN) and original images as the input of GoogLeNet, this model can achieve a top-1 average identification accuracy of 94.33%. By adjusting the hyper-parameters, modifying the architecture of the convolutional neural networks, and selecting different generative adversarial networks, an improved model for training and testing 5 classes of tomato leaf images was obtained. Meanwhile, images generated by DCGAN not only enlarge the size of the data set, but also have the characteristics of diversity, which makes the model have a good generalization effect. We have also visually confirmed that the images generated by DCGAN have much better quality and are more convincing through the t-Distributed Stochastic Neighbor Embedding (t-SNE) and Visual Turing Test. Experiments with tomato leaf disease identification show that DCGAN can generate data that approximate to real images, which can be used to (1) provide a larger data set for the training of large neural networks, and improve the performance of the recognition model through highly discriminating image generation technology; (2) reduce the cost of data collection; (3) enhance the diversity of data and the generalization ability of the recognition models.

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“…Among them collar rot or Sclerotium wilt; Sclerotium rolfsii (Mahato et al 2017) [22] , damping off; Fusarium spp., Pythium spp., Rhizoctonia solani, Sclerotium rolfsii (Prasad et al 2017) [33] , early blight; Alternaria solani (Roopa, 2012) [39] , Fusarium wilt; Fusarium oxysporum f.sp. lycopersici (Manikandan and Raguchander, 2014) [23] , late blight (Olanya et al 2015) [30] , Septoria leaf spot (Parker et al 1997 andBlum, 2000) [31,11] , bacterial fruit canker; Clavibacter michiganensis subsp. michiganensis (Ftayeh et al 2010 andSharabani et al 2013) [18,41] , bacterial wilt; Ralstonia solanacearum (Ayana and Fininsa, 2016) [9] , bud blight; Groundnut bud necrosis virus (Manjunatha et al 2010) [24] , leaf curl; Tomato leaf curl virus (Shelat et al 2014 andYadav et al 2014) [42,48] , tomato mosaic; Tomato mosaic virus (Alishiri et al 2013) [4] , tomato spotted wilt; Tomato spotted wilt virus (Sevik and Arli-Sokmen, 2012) [40] and Root knot; Meloidogyne spp (Naz et al 2012) [28] are major diseases of tomato (Mahato et al 2017) [22] .…”

Section: Introductionmentioning

confidence: 99%