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Agronomy
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Advanced Research on Diagnosis and Biological Control of Crop Diseases
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Advanced Research on Diagnosis and Biological Control of Crop Diseases

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Pest and Disease Management".

Deadline for manuscript submissions: 20 June 2025 | Viewed by 6848

Special Issue Editors

Dr. Yi Cheng

E-Mail Website
Guest Editor
Institute of Bast Fiber Crops and Center of Southern Economic Crops, Chinese Academy of Agricultural Sciences, Changsha 410205, China
Interests: plant disease diagnosis; molecular detection; biological control; disease management
Dr. Xiaofeng Su

E-Mail Website
Guest Editor
Biotechnology Research Institute, Chinese Academy of Agricultural Sciences, Beijing, China
Interests: plant resistant mechanism; fungal pathogenic mechanism; plant/microbe interaction; host-induced gene silencing; RNAi
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Crops are continuously confronted with a wide variety of plant pathogens, including fungi, nematodes, oomycetes, bacteria, and viruses, resulting in significant losses of crop yield and quality worldwide. Only after identifying the pathogen of crop disease can the corresponding control strategies be developed based on the biological characteristics and occurrence regularity of pathogen species. Therefore, a rapid and accurate diagnosis of crop diseases is essential for their effective control. At present, chemical control is still the main method used for controlling crop diseases. However, the increased use of chemical pesticides on agricultural crops has raised a great number of economic, ecological and health concerns. As an alternative, biological control is an effective and sustainable method, as it uses beneficial microorganisms or microbial metabolites to control crop diseases.

Reviews, original research articles, and communications are all welcome.

This Special Issue, entitled "Advanced Research on Diagnosis and Biological Control of Crop Diseases", aims to present the latest research findings on any aspect of disease diagnosis and biological control. Some of the main topics include, but are not limited to, the following:

  • New diagnostic tools for the detection of crop disease;
  • Research and application of novel biocontrol;
  • The relationships between microbial diversity and biocontrol;
  • The key mechanisms of biocontrol;

Development of more diverse and effective biocontrol for crop diseases

Dr. Yi Cheng
Dr. Xiaofeng Su
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • molecular identification
  • sustainable crop protection
  • diagnostic and detection
  • high-throughput identification
  • mechanisms
  • biological control
  • crop disease

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (5 papers)

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Research

15 pages, 2494 KiB  
Article
High-Throughput Field Screening of Cassava Brown Streak Disease Resistance for Efficient and Cost-Saving Breeding Selection
by Mouritala Sikirou, Najimu Adetoro, Samar Sheat, Eric Musungayi, Romain Mungangan, Miafuntila Pierre, Kayode Fowobaje, Ibnou Dieng, Zoumana Bamba, Ismail Rabbi, Hapson Mushoriwa and Stephan Winter
Agronomy 2025, 15(2), 425; https://doi.org/10.3390/agronomy15020425 - 8 Feb 2025
Viewed by 269
Abstract
Cassava brown streak disease (CBSD) remains the most severe threat to cassava production in the Great Lakes region and Southern Africa. Screening for virus resistance by subjecting cassava to high virus pressure in the epidemic zone (hotspots) is a common but lengthy process [...] Read more.
Cassava brown streak disease (CBSD) remains the most severe threat to cassava production in the Great Lakes region and Southern Africa. Screening for virus resistance by subjecting cassava to high virus pressure in the epidemic zone (hotspots) is a common but lengthy process because of unpredictable and erratic virus infections requiring multiple seasons for disease evaluation. This study investigated the feasibility of graft-infections to provide a highly controlled infection process that is robust and reproducible to select and eliminate susceptible cassava at the early stages and to predict the resistance of adapted and economically valuable varieties. To achieve this, a collection of cassava germplasm from the Democratic Republic of Congo and a different set of breeding trials comprising two seed nurseries and one preliminary yield trial were established. The cassava varieties OBAMA and NAROCASS 1 infected with CBSD were planted one month after establishment of the main trials in a 50 m2 plot to serve as the source of the infection and to provide scions to graft approximately 1 ha. Grafted plants were inspected for virus symptoms and additionally tested by RT-qPCR for sensitive detection of the viruses. The incidence and severity of CBSD and cassava mosaic disease (CMD) symptoms were scored at different stages of plant growth and fresh root yield determined at harvesting. The results from the field experiments proved that graft-infection with infected plants showed rapid symptom development in susceptible cassava plants allowing instant exclusion of those lines from the next breeding cycle. High heritability, with values ranging from 0.63 to 0.97, was further recorded for leaf and root symptoms, respectively. Indeed, only a few cassava progenies were selected while clones DSC260 and two species of M. glaziovii (Glaziovii20210005 and Glaziovii20210006) showed resistance to CBSD. Taken together, grafting scions from infected cassava is a highly efficient and cost-effective method to infect cassava with CBSD even under rugged field conditions. It replaces an erratic infection process with a controlled method to ensure precise screening and selection for virus resistance. The clones identified as resistant could serve as elite donors for introgression, facilitating the transfer of resistance to CBSD. Full article
(This article belongs to the Special Issue Advanced Research on Diagnosis and Biological Control of Crop Diseases)
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<p>Introducing virus infection: (<b>a</b>) by side-grafting of scions from infected source plants to healthy cassava rootstocks; (<b>b</b>) observing development of symptoms on newly developing leaves of sprouting buds 3 weeks after grafting.</p> Full article ">Figure 2
<p>Expression of cassava brown streak disease (CBSD) symptoms: leaves (<b>a</b>) and roots (<b>b</b>) of a susceptible variety.</p> Full article ">Figure 3
<p>CBSD symptom evaluation in the root in the seed nursery after grafting CIAT population (G: grafted plants and NG: non-grafted plants).</p> Full article ">Figure 4
<p>CBSD symptoms evaluation in the root in the Uganda SN after grafting at harvest (G: grafted plants and NG non-grafted plants).</p> Full article ">Figure 5
<p>Correlation between CMD, CBSD, and yield in the DRC germplasm collection (<b>above</b>) and PYT Nigeria (<b>below</b>). Values in the figure represent the correlation coefficient.</p> Full article ">
19 pages, 5373 KiB  
Article
Cladophialophora guangxiense sp. nov., a New Species of Dark Septate Endophyte, Mitigates Tomato Bacterial Wilt and Growth Promotion Activities
by Xihong Wei, Yanyan Long, Yanlu Chen, Stanley Nyenje Mataka, Xue Jiang, Yi Zhou, Zhengxiang Sun and Ling Xie
Agronomy 2024, 14(12), 2771; https://doi.org/10.3390/agronomy14122771 - 22 Nov 2024
Viewed by 831
Abstract
Bacterial wilt of tomatoes, caused by Ralstonia solanacearum, is a significant soilborne disease that often causes significant reductions in the yield of tomatoes. Dark septate endophytic fungi (DSE) represent potential biocontrol agents against plant pathogens that can also enhance plant growth. To collect [...] Read more.
Bacterial wilt of tomatoes, caused by Ralstonia solanacearum, is a significant soilborne disease that often causes significant reductions in the yield of tomatoes. Dark septate endophytic fungi (DSE) represent potential biocontrol agents against plant pathogens that can also enhance plant growth. To collect DSE fungi with potential for biocontrol, the fungus Cladophialophora guangxiense HX2 was isolated from the rhizosphere soil of sugarcane in Hengzhou Guangxi Province, China, and a novel species of Cladophialophora was identified based on morphological properties and DNA sequence analysis. C. guangxiense HX2 demonstrated a controlling effect of 76.7% on tomato bacterial wilt and promoted a 0.5-fold increase in tomato seedling height. It colonized tomato seedling roots, enhancing the activity of antioxidant and defensive enzyme systems. Transcriptomic and qPCR approaches were used to study the induction response of the strain HX2 infection by comparing the gene expression profiles. Gene ontology (GO) and Kyoto Encyclopedia of Gene and Genome (KEGG) pathway enrichment revealed that tomatoes can produce salicylic acid metabolism, ethylene-activated signaling, photosynthesis, and phenylpropanoid biosynthesis to the strain HX2 infection. The expression of IAA4 (3.5-fold change), ERF1 (3.5-fold change), and Hqt (1.5-fold change) was substantially enhanced and Hsc 70 (0.5-fold change) was significantly reduced in the treatment group. This study provides a theoretical foundation for further investigation into the potential of C. guangxiense HX2 as a biological agent for the prevention and control of tomato bacterial wilt. Full article
(This article belongs to the Special Issue Advanced Research on Diagnosis and Biological Control of Crop Diseases)
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Figure 1

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<p>Tomato–HX2 (<span class="html-italic">C. guangxiense</span>) symbiont. (<b>a</b>): Co-culture results of strain HX2 and tomato seedlings. (<b>b</b>): Tomato seedling dry weight. CK: Tomato cultured on PDA; HX2: Tomato treated with strain HX2. Bars indicate the standard error of the mean. Columns marked with the same letter are not significantly different according to Duncan’s Multiple Range Test at <span class="html-italic">p</span> ≤ 0.05.</p> Full article ">Figure 2
<p>Morphology of endophytic fungi <span class="html-italic">Cladophialophora guangxiense</span> HX2. (<b>a</b>): Colony on CMMY after 2 weeks at 28 °C. (<b>b</b>,<b>c</b>): Lateral conidiogenous cells and oval conidial chains on OMA after 3 weeks at 28 °C. (<b>d</b>–<b>f</b>): Curled string of sausage-shaped conidial chains on OMA after 3 weeks at 28 °C. (<b>g</b>): Cylindrical to sub-cylindrical conidial chains on OMA after 3 weeks at 28 °C. (<b>h</b>): Solitary conidiophores and oval conidial chains on OMA after 3 weeks at 28 °C. (<b>i</b>): Budding cells on OMA after 3 weeks at 28 °C. Scale bars 20 μm.</p> Full article ">Figure 3
<p>NJ phylogenetic tree based on the combined sequences ITS+LSU+SSU of <span class="html-italic">Cladophialophora</span> species. Bootstrap values &gt; 50% are shown at nodes. <span class="html-italic">Cyphellophora reptans</span> CBS 113.85 was used as an outgroup. T: type strain. The isolated strain of this study is indicated in bold. The bar indicates 0.02 nucleotide substitutions per site.</p> Full article ">Figure 4
<p>The effect of spore suspension from strain HX2 at different concentrations on tomato growth parameters. This figure illustrates the differences in root length plant height and stem diameter between the treated and control groups. C1: 1 × 10<sup>8</sup> spores/ mL spore suspension of strain HX2 C2: 1 × 10<sup>6</sup> spores/mL spore suspension of strain HX2 C3: 1 × 10<sup>4</sup> spores/mL spore suspension of strain HX2 ck: H<sub>2</sub>O. (<b>A</b>): Potted Plant Experiment. (<b>B</b>): Root length. (<b>C</b>): Plant height. (<b>D</b>): Stem diameter. Bars indicate the standard error of the mean. Columns marked with the same letter are not significantly different according to Duncan’s Multiple Range Test at <span class="html-italic">p</span> ≤ 0.05.</p> Full article ">Figure 5
<p>Effect of HX2 on the control of <span class="html-italic">R. solanacearum</span> in tomatoes.</p> Full article ">Figure 6
<p><span class="html-italic">C. guangxiense</span> HX2 colonized in the roots of tomato seedlings. (<b>a</b>,<b>b</b>): the colonization of endophytic fungal within the tomato root tissue was observed using an Olympus BX53 microscope, following staining with lactic acid cotton blue. Intracellular (black arrows) and intercellular (red arrows) colonization of hyphae.</p> Full article ">Figure 7
<p>Effect of strain HX2 on activities of antioxidant and defense−related enzymes in leaves of tomato plants. Antioxidant enzymes including phenylalanine ammonia−lyase (PAL) (<b>A</b>), peroxidase (POD) (<b>B</b>), and superoxide dismutase (SOD) (<b>C</b>). Defense−related enzymes including polyphenol oxidase (PPO) (<b>D</b>) and catalase (CAT) (<b>E</b>). CK: sterile water. T1: HX2; T2: HX2+ <span class="html-italic">R. solanacearum</span>; T3: Thiediazole copper + <span class="html-italic">R. solanacearum</span>; T4: <span class="html-italic">R. solanacearum.</span> Bars indicate the standard error of the mean.</p> Full article ">Figure 8
<p>DEG analyses of tomatoes with HX2 vs. PDA inoculation. (<b>a</b>): The module clusters and their relationships. (<b>b</b>): Volcano plots (the abscissa indicates the multiple changes of gene expression in different samples (log2FoldChange), and the ordinate indicates the significant level of expression difference (−log10padj); upregulated genes are represented by red dots and downregulated genes by blue dots). (<b>c</b>): Gene Ontology (GO) annotation category statistics. (<b>d</b>): Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway classification statistic. ((<b>c</b>,<b>d</b>): The abscissa is the Term, and the ordinate is the number of genes annotated to the Term).</p> Full article ">Figure 8 Cont.
<p>DEG analyses of tomatoes with HX2 vs. PDA inoculation. (<b>a</b>): The module clusters and their relationships. (<b>b</b>): Volcano plots (the abscissa indicates the multiple changes of gene expression in different samples (log2FoldChange), and the ordinate indicates the significant level of expression difference (−log10padj); upregulated genes are represented by red dots and downregulated genes by blue dots). (<b>c</b>): Gene Ontology (GO) annotation category statistics. (<b>d</b>): Kyoto Encyclopedia of Genes and Genomes (KEGG) pathway classification statistic. ((<b>c</b>,<b>d</b>): The abscissa is the Term, and the ordinate is the number of genes annotated to the Term).</p> Full article ">Figure 9
<p>Enrichment analysis of tomatoes with HX2 vs. PDA inoculation. (<b>a</b>): Gene Ontology (GO) enrichment analysis. (<b>b</b>): Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analysis. Circle size represents the number of enriched genes. The X axis displays the enrichment factor.</p> Full article ">Figure 10
<p>The effect of strain HX2 on the expression level of disease-resistant and pathogenic genes by quantitative reverse-transcription PCR analysis. Relative expression levels of (<b>a</b>) <span class="html-italic">IAA</span>4, (<b>b</b>) <span class="html-italic">ERF</span>1, (<b>c</b>) <span class="html-italic">Hqt</span>, and (<b>d</b>) <span class="html-italic">Hsc</span>70 in the root of tomatoes through treatment with strain HX2 and sterile water (CK). Error bars represent mean standard deviation of triplicate experiments. ** <span class="html-italic">p</span> &lt; 0.05 *** <span class="html-italic">p</span> &lt; 0.001.</p> Full article ">
13 pages, 2968 KiB  
Article
High-Quality Complete Genome Resource for Dickeya dadantii Type Strain DSM 18020 via PacBio Sequencing
by Yi Cheng, Jianping Xu, Zhiqiang Song, Wenting Li, Jiayang Li, Zhecheng Xu, Fengming Chen, Huajiao Qiu and Tuhong Wang
Agronomy 2024, 14(7), 1342; https://doi.org/10.3390/agronomy14071342 - 21 Jun 2024
Viewed by 1036
Abstract
Dickeya dadantii is a common pathogen of bacterial soft rot on a wide range of plants, including several crops. In this study, we present the complete genome sequence of the D. dadantii type strain DSM18020T. The genome was assembled using PacBio [...] Read more.
Dickeya dadantii is a common pathogen of bacterial soft rot on a wide range of plants, including several crops. In this study, we present the complete genome sequence of the D. dadantii type strain DSM18020T. The genome was assembled using PacBio technology, resulting in a 4,997,541 bp circular chromosome with a G+C content of 56.5%. Our sequence analyses predicted 4277 protein-encoding genes, including several associated with known bacterial virulence factors and secondary metabolites. Comparative genomics analysis between Dickeya revealed that the category of ‘metabolism’ is the most important in both the core and accessory genomes, while the category of ‘information storage and processing’ is the most dominant in unique genomes. These findings will not only help us to understand the pathogenic mechanisms of D. dadantii DSM18020T, but also provide us with useful information for new control strategies against this phytopathogen. Full article
(This article belongs to the Special Issue Advanced Research on Diagnosis and Biological Control of Crop Diseases)
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<p>Circular chromosome of <span class="html-italic">D. dadantii</span> DSM18020<sup>T</sup> genome generated by Circos v0.64 (<a href="http://circos.ca/" target="_blank">http://circos.ca/</a>, accessed on 5 June 2018). Circles display from outside to inside: (1) genome position in Mb; (2,3) coding genes on + and-stands, correspondingly; (4) rRNA and tRNA; (5) GC content. Above the average is red and below is blue; (6) GC skew.</p> Full article ">Figure 2
<p>Phylogeny of <span class="html-italic">Dickeya</span> strains constructed on REALPHY using PhyML 3.1 based on complete sequence.</p> Full article ">Figure 3
<p>The evolution of pan-, core, and singleton genomes depends on the number of selected <span class="html-italic">Dickeya</span> strains.</p> Full article ">Figure 4
<p>COGs in the core, accessory, and unique genomes and KEGG analysis of 14 <span class="html-italic">Dickeya</span> strains: (<b>A</b>) COGs’ distribution; (<b>B</b>) KEGG distribution; (<b>C</b>) COGs’ functions; (<b>D</b>) KEGG pathways and their functions.</p> Full article ">
12 pages, 3309 KiB  
Article
Pan-Genome Analysis and Secondary Metabolic Pathway Mining of Biocontrol Bacterium Brevibacillus brevis
by Jie Du, Binbin Huang, Jun Huang, Qingshan Long, Cuiyang Zhang, Zhaohui Guo, Yunsheng Wang, Wu Chen, Shiyong Tan and Qingshu Liu
Agronomy 2024, 14(5), 1024; https://doi.org/10.3390/agronomy14051024 - 11 May 2024
Viewed by 1720
Abstract
Brevibacillus brevis is one of the most common biocontrol strains with broad applications in the prevention and control of plant diseases and insect pests. In order to deepen our understanding of B. brevis genomes, describe their characteristics comprehensively, and mine secondary metabolites, we [...] Read more.
Brevibacillus brevis is one of the most common biocontrol strains with broad applications in the prevention and control of plant diseases and insect pests. In order to deepen our understanding of B. brevis genomes, describe their characteristics comprehensively, and mine secondary metabolites, we retrieved the genomic sequences of nine B. brevis strains that had been assembled into complete genomes from the NCBI database. These genomic sequences were analyzed using phylogenetic analysis software, pan-genome analysis software, and secondary metabolite mining software. Results revealed that the genome size of B. brevis strains ranged from 6.16 to 6.73 Mb, with GC content ranging from 47.0% to 54.0%. Phylogenetic analysis classified the nine B. brevis strains into three branches. The analyses of ANI and dDDH showed that B. brevis NEB573 had the potential to become a new species of Brevibacillus and needed further research in the future. The pan-genome analysis identified 10032 gene families, including 3257 core gene families, 3112 accessory gene families, and 3663 unique gene families. In addition, 123 secondary metabolite biosynthetic gene clusters of 20 classes were identified in the genomes of nine B. brevis strains. The major types of biosynthetic gene clusters were non-ribosomal peptide synthase (NRPS) and transAT polyketide synthase (transAT-PKS). Furthermore, a large number of untapped secondary metabolites were identified in B. brevis. In summary, this study elucidated the pan-genome characteristics of the biocontrol bacterium B. brevis and identified its secondary metabolites, providing valuable insights for its further development and utilization. Full article
(This article belongs to the Special Issue Advanced Research on Diagnosis and Biological Control of Crop Diseases)
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Figure 1
<p>ANI (<b>A</b>) and dDDH (<b>B</b>) analyses of the nine <span class="html-italic">B. brevis</span> strains.</p> Full article ">Figure 2
<p>Phylogenetic tree of nine <span class="html-italic">B. brevis</span> strains based on 120 bacterial single-copy marker genes.</p> Full article ">Figure 3
<p>Pan-genome analysis of the nine <span class="html-italic">B. brevis</span> genomes. (<b>A</b>) Distribution of gene families. (<b>B</b>) Curve development of pan (blue color) and core (pink color) genomes.</p> Full article ">Figure 4
<p>COG functional analysis of pan-genome. The graph shows the predicted function of proteins encoded by core, accessory, and unique gene families of the pan-genome.</p> Full article ">Figure 5
<p>Occurrence frequency of secondary metabolite synthesis gene clusters.</p> Full article ">
20 pages, 6367 KiB  
Article
An Advancing GCT-Inception-ResNet-V3 Model for Arboreal Pest Identification
by Cheng Li, Yunxiang Tian, Xiaolin Tian, Yikui Zhai, Hanwen Cui and Mengjie Song
Agronomy 2024, 14(4), 864; https://doi.org/10.3390/agronomy14040864 - 20 Apr 2024
Cited by 2 | Viewed by 1733
Abstract
The significance of environmental considerations has been highlighted by the substantial impact of plant pests on ecosystems. Addressing the urgent demand for sophisticated pest management solutions in arboreal environments, this study leverages advanced deep learning technologies to accurately detect and classify common tree [...] Read more.
The significance of environmental considerations has been highlighted by the substantial impact of plant pests on ecosystems. Addressing the urgent demand for sophisticated pest management solutions in arboreal environments, this study leverages advanced deep learning technologies to accurately detect and classify common tree pests, such as “mole cricket”, “aphids”, and “Therioaphis maculata (Buckton)”. Through comparative analysis with the baseline model ResNet-18 model, this research not only enhances the SE-RegNetY and SE-RegNet models but also introduces innovative frameworks, including GCT-Inception-ResNet-V3, SE-Inception-ResNet-V3, and SE-Inception-RegNetY-V3 models. Notably, the GCT-Inception-ResNet-V3 model demonstrates exceptional performance, achieving a remarkable average overall accuracy of 94.59%, average kappa coefficient of 91.90%, average mAcc of 94.60%, and average mIoU of 89.80%. These results signify substantial progress over conventional methods, outperforming the baseline model’s results by margins of 9.1%, nearly 13.7%, 9.1%, and almost 15% in overall accuracy, kappa coefficient, mAcc, and mIoU, respectively. This study signifies a considerable step forward in blending sustainable agricultural practices with environmental conservation, setting new benchmarks in agricultural pest management. By enhancing the accuracy of pest identification and classification in agriculture, it lays the groundwork for more sustainable and eco-friendly pest control approaches, offering valuable contributions to the future of agricultural protection. Full article
(This article belongs to the Special Issue Advanced Research on Diagnosis and Biological Control of Crop Diseases)
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<p>Pest dataset samples: (<b>a</b>) mole cricket; (<b>b</b>) aphids; (<b>c</b>) <span class="html-italic">Therioaphis maculata</span> (Buckton).</p> Full article ">Figure 2
<p>Technical route.</p> Full article ">Figure 3
<p>The architecture of the ResNet-18 Model.</p> Full article ">Figure 4
<p>The architecture of the SE-RegNet Model.</p> Full article ">Figure 5
<p>The architecture of the SE-RegNetY model.</p> Full article ">Figure 6
<p>The architecture of the SE-Inception-ResNet-V3 model.</p> Full article ">Figure 7
<p>The architecture of the SE-Inception-RegNetY-V3 model.</p> Full article ">Figure 8
<p>The architecture of the SE-Inception-RegNetY-V3 model.</p> Full article ">Figure 9
<p>Model performance comparison.</p> Full article ">Figure 10
<p>Normalized Matrix: (<b>a</b>) Normalized Matrix ofResNet-18; (<b>b</b>) Normalized Matrix of SE-RegNet; (<b>c</b>) Normalized Matrix of SE-RegNetY; (<b>d</b>) Normalized Matrix of SE-Inception-ResNet-V3; (<b>e</b>) Normalized Matrix of SE-Inception-RegNetY-V3; (<b>f</b>) Normalized Matrix of GCT-Inception-ResNet-V3.</p> Full article ">Figure 10 Cont.
<p>Normalized Matrix: (<b>a</b>) Normalized Matrix ofResNet-18; (<b>b</b>) Normalized Matrix of SE-RegNet; (<b>c</b>) Normalized Matrix of SE-RegNetY; (<b>d</b>) Normalized Matrix of SE-Inception-ResNet-V3; (<b>e</b>) Normalized Matrix of SE-Inception-RegNetY-V3; (<b>f</b>) Normalized Matrix of GCT-Inception-ResNet-V3.</p> Full article ">Figure 11
<p>Loss and accuracy variation curve: (<b>a</b>) curve of ResNet-18; (<b>b</b>) curve of SE-RegNet; (<b>c</b>) curve of SE-RegNetY; (<b>d</b>) curve of SE-Inception-ResNet-V3; (<b>e</b>) curve of SE-Inception-RegNetY-V3; (<b>f</b>) curve of GCT-Inception-ResNet-V3.</p> Full article ">Figure 11 Cont.
<p>Loss and accuracy variation curve: (<b>a</b>) curve of ResNet-18; (<b>b</b>) curve of SE-RegNet; (<b>c</b>) curve of SE-RegNetY; (<b>d</b>) curve of SE-Inception-ResNet-V3; (<b>e</b>) curve of SE-Inception-RegNetY-V3; (<b>f</b>) curve of GCT-Inception-ResNet-V3.</p> Full article ">Figure 12
<p>An exemplary result of the CNN visualization heat map.</p> Full article ">
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