Journal of Phytopathology最新文献

{"title":"Impact of Planting Time on Severity of Alternaria Blight and Yield Losses in African Marigold (Tagetes erecta)","authors":"Rakesh Kumar,&nbsp;Ranjit Singh,&nbsp;Annie Khanna,&nbsp;Gaurav Singh,&nbsp;Pankaj Kumar","doi":"10.1111/jph.70101","DOIUrl":"https://doi.org/10.1111/jph.70101","url":null,"abstract":"<div>\u0000 \u0000 <p>Alternaria blight, caused by <i>Alternaria tagetica</i>, poses a significant threat to marigold production, leading to substantial losses in flower yield. The present study aimed to evaluate the impact of different transplanting months on Alternaria leaf blight severity, flower blight incidence and yield losses in marigold. Results indicated that transplanting the crop in October month resulted in the highest leaf blight severity (89.00% ± 1.87%), flower blight incidence (59.30% ± 1.84%) and disease progression (AUDPC = 4259.50 ± 53.37), while February and March transplanted crops had the lowest disease severity (9.13% ± 1.11% and 15.13% ± 1.89%, respectively). Marketable flower yield varied significantly with transplanting months and highest marketable flower yield was observed in August (207.21 ± 1.17 q/ha), whereas the lowest was in December transplanting (17.60 ± 1.17 q/ha). The application of carbendazim effectively reduced disease severity and increased the marketable floral yield in protected plots compared to unprotected ones. Avoidable floral yield losses due to <i>Alternaria</i> infection ranged from 11.23% to 58.90% across different transplanting months, being highest in October. Correlation analysis revealed that disease severity was positively correlated with morning relative humidity (<i>r</i> = 0.93) and evening relative humidity (<i>r</i> = 0.59) while it was negatively correlated with maximum temperature (<i>r</i> = −0.76), wind speed (<i>r</i> = −0.94) and sunshine hours (<i>r</i> = −0.84). Principal component analysis indicated that high morning humidity and wind speed are the main drivers of Alternaria blight severity. These findings demonstrated that months with the highest yield potential are also congenial for the pathogen to cause infection. Marigold growing farmers can minimise financial losses by regularly monitoring their crops and transplanting crops to less favourable climatic conditions for disease development.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 3","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339518","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Validation of a Standard Area Diagram Set for Assessment of Target Leaf Spot Severity on Handroanthus serratifolius","authors":"Ingryd Luzia de Farias Andrade,&nbsp;Ana Gabriele Gurgel Amaral,&nbsp;Ricardo Brainer Martins,&nbsp;Kamila Câmara Correia,&nbsp;Henrique da Silva Silveira Duarte,&nbsp;Humberson Rocha Silva,&nbsp;Marcos Paz Saraiva Câmara,&nbsp;Sami Jorge Michereff","doi":"10.1111/jph.70105","DOIUrl":"https://doi.org/10.1111/jph.70105","url":null,"abstract":"<p>Target leaf spot, caused by <i>Corynespora cassiicola</i>, is a common disease on <i>Handroanthus serratifolius</i> (yellow ipe) in Brazil. This study aimed to develop and validate a standard area diagram (SAD) set to estimate the severity of target spot on <i>H. serratifolius</i> leaves. For this purpose, a SAD set with 10 levels of severity (1%; 3%; 5%; 10%; 20%; 30%; 40%; 55%; 70% and 85%) was developed. The SAD set was validated by 20 raters with no experience in evaluating plant diseases. Both accuracy and precision improved when the proposed SAD set was employed. The statistical parameters evaluated were bias coefficient factor - <i>C</i>_<i>b</i> (no SAD set = 0.898, with SAD set = 0.994); correlation coefficient - <i>r</i> (no SAD set = 0.926, with SAD set = 0.979); and Lin's concordance correlation coefficient - <i>ρ</i>_<i>c</i> (no SAD set = 0.835, with SAD set = 0.974). In addition, estimates were more reliable as showed by the intra-class correlation coefficient - <i>ρ</i> (no SAD set = 0.792, with SAD set = 0.962). The SAD set proposed here is a useful tool for improving visual assessments of target spot severity on <i>H. serratifolius</i> leaves.</p>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 3","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-06-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jph.70105","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144339519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"MSNet-LNet Architecture With Improved Deep Joint Segmentation for Tomato Plant Disease Classification With Set of Multi-Texton and Statistical Features","authors":"V. Jayanthi,&nbsp;M. Kanchana","doi":"10.1111/jph.70088","DOIUrl":"https://doi.org/10.1111/jph.70088","url":null,"abstract":"<div>\u0000 \u0000 <p>Tomato is a broadly consumed crop in every continent, though the number of tomatoes varies based on the method of fertilisation. The main factor that affects the crop yield quantity and quality is leaf disease. Therefore, it is essential to correctly diagnose and categorise these illnesses. Many creative approaches have been proposed to diagnose and categorise diseases using images. The major objective is to present a five-phase tomato plant disease classification (TPDC) model. Initially, Gaussian filtering is deployed as a pre-processing technique on the input image. To perform the segmentation procedure, an improved deep joint (IDJ) framework is suggested. Next, features are retrieved, such as statistical features, colour features and Improved MT (IMT) features. Data augmentation is then carried out. Based on the training feature information, a hybrid model integrating the modified Squeeze Net (MSNet) and LinkNet is presented to perform disease classification on tomato plants. Moreover, the proposed method is validated using two benchmark datasets, namely the PlantVillage dataset and the Taiwan tomato leaf images. The performance of the proposed work is evaluated over the conventional models in terms of different performance measures. As a result, the proposed IHM (MSNet+Linknet) model surpassed traditional methods, achieving an accuracy of 0.957, specificity of 0.975 and NPV of 0.973, consistently showcasing strong performance across both datasets. Therefore, the proposed approach shows exceptional performance in classifying tomato leaf diseases over the traditional methods.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 3","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-06-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144336065","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Review: Elucidating the Efficacy of Antagonistic Yeasts by Using Multiple Mechanisms in Controlling Fungal Phytopathogens","authors":"Rochelle C. Olana,&nbsp;Sukanya Haituk,&nbsp;Anuruddha Karunarathna,&nbsp;Christian Joseph R. Cumagun","doi":"10.1111/jph.70097","DOIUrl":"https://doi.org/10.1111/jph.70097","url":null,"abstract":"<div>\u0000 \u0000 <p>The widespread and indiscriminate application of synthetic fungicides against phytopathogens has led to ecological damage and health risks, highlighting the urgent need for eco-friendly alternatives. Antagonistic yeasts provide an environmentally sound alternative to chemical fungicides for crop productivity and environmental conservation, exhibiting direct and indirect control mechanisms against phytopathogens. These microorganisms possess simple nutritional requirements, compatibility with low doses of fungicides and promote public health safety by releasing non-hazardous secondary metabolites. Also, the commercialisation of yeast-based products shows significant potential for protection to a range of commodities, both pre- and postharvest. However, there was only a little literature on the complex control mechanisms working together by various yeast genera against phytopathogens, which were particularly stressed in the review. In addition, the different challenges of biocontrol yeast application and the standard commercialisation protocol were discussed. Future research should focus on optimising formulations and understanding environmental interactions to support biocontrol yeasts' commercialisation and widespread application for crop management.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 3","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-06-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144315200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Impact of Potato Crop Rotation on Verticillium dahliae, Colletotrichum coccodes and Potato Early Dying","authors":"Julie Pedersen,&nbsp;Isaac K. Abuley,&nbsp;Jennie L. Brierley,&nbsp;Alison K. Lees,&nbsp;Sabine Ravnskov","doi":"10.1111/jph.70099","DOIUrl":"https://doi.org/10.1111/jph.70099","url":null,"abstract":"<p>The influence of different potato rotations on potato early dying (PED) and infections by the soilborne pathogen <i>Verticillium dahliae</i> was investigated. The study also considered the co-occurrence of the PED-associated fungus <i>Colletotrichum coccodes</i> in the fields. Furthermore, a pot experiment was carried out to explore the correlation between wilt progression and <i>V. dahliae</i> levels in plants, and to assess the effects of biofumigating cover crops in the potato rotations. In the fields studied, the density of <i>V. dahliae</i> observed in soil and plants was significantly higher in a 2-year potato rotation, compared to growing potatoes every 3–4 years. In addition, the study confirmed very low quantities of <i>V. dahliae</i> and <i>C. coccodes</i> in soils with an extended break (&gt; 15 years) from potatoes. In the pot experiment, there was no direct effect of the cover crops, yellow mustard and oats, on either incidence of <i>V. dahliae</i> infections, plant biomass, or premature wilting of potato plants. However, a strong positive correlation between <i>V. dahliae</i> and wilt severity was observed in potatoes without prior growing of oat or yellow mustard, which was not evident in potato plants grown after the incorporation of these cover crops. The findings demonstrate a clear association between potato crop rotation and the incidence of <i>V. dahliae</i> in the field; however, the inconclusive results of the effect of oat and yellow mustard cover crops on <i>V. dahliae</i> and PED require further investigation. This study suggests that the development of more strategic potato rotation management could reduce PED levels.</p>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 3","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1111/jph.70099","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308739","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficacy of a Beauveria bassiana-Based Biofungicide Against Strawberry Powdery Mildew Caused by Podosphaera aphanis Under Various Conditions and Its Potential Mode of Action","authors":"Shunsuke Asano,&nbsp;Yoshihiko Hirayama,&nbsp;Kandai Yoshida,&nbsp;Masahiro Katsuma,&nbsp;Kotaro Hori,&nbsp;Yuichiro Iida,&nbsp;Satoshi Yamanaka,&nbsp;Masaharu Kubota","doi":"10.1111/jph.70100","DOIUrl":"https://doi.org/10.1111/jph.70100","url":null,"abstract":"<div>\u0000 \u0000 <p>Powdery mildew, caused by <i>Podosphaera aphanis</i>, is a severe disease affecting strawberry production in Japan. The biofungicide BotaniGard ES, whose active ingredient is <i>Beauveria bassiana</i> strain GHA, was reported to be effective against <i>P. aphanis</i>. In the present study, the efficacy of BotaniGard was clarified under a range of conditions to evaluate its practicality in the greenhouse. In greenhouse efficacy trials, the control efficacy of BotaniGard was relatively high (70.4%–100%) against the disease under low disease pressure, but low (28.1%–53.4%) under high disease pressure. In addition, the application 6 h before inoculation was the most effective compared with application 7 days before or 7 days after inoculation. The application of BotaniGard at 1-week intervals (control efficacy of 66.5%) was more effective against powdery mildew on leaves than at 2- or 3-week intervals. In contrast, the application of BotaniGard was not effective against powdery mildew on fruits, regardless of application interval, although most of the chemical fungicides tested for comparison were effective. RNA-seq analysis indicated that the application of BotaniGard upregulated the defence response, systemic acquired resistance and chitinase activity in leaves. These results indicate that BotaniGard is effective in controlling strawberry powdery mildew on leaves under low disease pressure, but not on fruits, and the mode of action might be the activation of induced plant defences. This study encourages a detailed evaluation of the efficacy of biofungicides to promote their appropriate use in greenhouse production.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 3","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-06-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144308748","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Crop Leaf Segmentation and Disease Detection Based on Shepherd Wide Residual Network","authors":"Reshma Nazirkar Atole,&nbsp;Navnath B Pokale,&nbsp;Anjali Devi Patil","doi":"10.1111/jph.70080","DOIUrl":"https://doi.org/10.1111/jph.70080","url":null,"abstract":"<div>\u0000 \u0000 <p>The rapid rise in population necessitates a significant increase in agricultural productivity to satisfy the increasing demand for food. Timely identification of crop diseases is essential to ensure food security. Timely identification of crop disorders is vital for effective disease management and mitigating declines in crop yields. However, manually monitoring leaf diseases is labour-intensive and requires extensive knowledge of plant pathogens and considerable time and effort. The primary objective of this study is to develop an efficient and accurate deep learning-based approach named Shepherd Wide Residual Network (ShWRes-Net) for the automated detection and classification of crop leaf diseases, thereby reducing reliance on manual diagnosis and improving disease management in agriculture. The process begins with collecting crop leaf images from various datasets, then subjecting them to pre-processing leveraging a Wiener filter to mitigate noise. Leaf segmentation is then performed utilising the Dual-Branch U-Net model. Additionally, feature extraction is performed using a Complete Local Binary Pattern and Pyramid Histogram of Oriented Gradients. Finally, the identification of crop diseases is accomplished through the introduction of the ShWRes-Net model, which combines the Shepard Convolutional Neural Network with the Wide Residual Network. The ShWRes-Net method achieved a True Negative Rate of 90.877%, a True Positive Rate of 94.876% and an accuracy of 92.986%.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 3","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144299877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Molecular Characterisation of Diverse Wheat Germplasm for Enhanced Resistance to Puccinia triticina","authors":"Farhan Ullah,&nbsp;Liaqat Shah,&nbsp;Muhammad Khalid,&nbsp;Akhlaq Ahmad,&nbsp;Chen Can,&nbsp;Si Hongqi,&nbsp;Ma Chuanxi","doi":"10.1111/jph.70095","DOIUrl":"https://doi.org/10.1111/jph.70095","url":null,"abstract":"<div>\u0000 \u0000 <p>Leaf rust (LR) epidemics present a persistent threat to global wheat production, despite the presence of resistance (Lr) genes in wheat. The evolving pathogen <i>Puccinia triticina</i> continually challenges these resistance mechanisms. This study assessed 10 wheat lines for relative resistance index (RRI) and screened them for <i>Lr</i> genes or quantitative trait loci (QTLs) using microsatellite markers. The lines were classified into three groups: Ssusceptible (&lt; 5; 4.32 ± 0.68), moderate (5–7; 6.05 ± 0.67) and resistant (&gt; 7; 8.50 ± 0.22) (<i>p</i> &lt; 0.001). Genetic analysis with 12 polymorphic markers revealed 186 alleles with varying allelic diversity. Markers <i>Xbarc124</i> and <i>Xgwm512</i> showed greater diversity, and resistance-related alleles were linked to markers <i>Xgwm512</i> and <i>Xgwm493</i>, associated with the <i>Lr34</i> gene. Moderate associations were found with <i>Lr37</i> (<i>Xbarc1138</i> and <i>Xgwm400</i>) and <i>Lr24</i> (<i>Xgwm273</i>), while <i>Lr26</i> (<i>Xwmc407</i>) was linked to susceptibility. Parental line crosses resulted in higher RRI, indicating beneficial recombination. Structure analysis revealed genetic diversity among resistance groups, with susceptible groups showing distinct clustering. Lines AN179 and PR127 clustered together, showing key resistance alleles, particularly in crosses with resistant PR123. The findings highlight novel pathogen races contributing to resistance breakdown and suggest combining all-stage resistance genes (<i>Lr9</i>, <i>Lr24</i>, <i>Lr37</i>) with adult plant resistance (APR) genes (<i>Lr48</i>, <i>Lr22a</i>, <i>Lr34</i>, <i>Lr46</i>) for durable LR resistance. The identified alleles offer valuable insights for marker-assisted breeding to enhance wheat resistance to LR.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 3","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144299878","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Identification of Fusarium Species Associated With Dry Rot of Potato Tubers in Sinaloa, Mexico","authors":"Jesús Christopher Castillo-Batista,&nbsp;Francisco Roberto Quiroz-Figueroa,&nbsp;Edgar Antonio Rodríguez-Negrete,&nbsp;Genaro Diarte-Plata,&nbsp;Ruben Felix-Gastelum,&nbsp;Norma Elena Leyva-López,&nbsp;María Elena Santos-Cervantes","doi":"10.1111/jph.70102","DOIUrl":"https://doi.org/10.1111/jph.70102","url":null,"abstract":"<div>\u0000 \u0000 <p>Northern Sinaloa is one of the most important potato-producing regions in Mexico. Potato dry rot caused by species of <i>Fusarium</i> is one of the major threats to potato production. The aim of this study was to identify and characterize <i>Fusarium</i> species associated with potato dry rot. A total of 146 <i>Fusarium</i> isolates were obtained from potato tubers with dry rot symptoms from fields in different geographic regions in northern Sinaloa, Mexico, in 2018–2019. A subgroup of 39 representative isolates was selected for further morphological and molecular characterization as well as pathogenicity tests. According to morphological features and phylogenetic analyses of the transcription elongation factor 1-α (EF1-α) gene, the isolates were identified as <i>F. oxysporum</i> clade 3, <i>F. falciforme</i> (FSSC 3 + 4) and <i>F. nygamai</i>. All representative <i>Fusarium</i> species isolates were pathogenic to potato tubers. Furthermore, a significant difference in virulence between species was observed. Future research should focus on understanding the genetic diversity and population structure of these pathogens in the field.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 3","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144292738","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Novel Feature Extraction and Siamese Zeiler and Fergus Forward Taylor Network-Based Rice Plant Leaf Disease Detection","authors":"Karthick Muthusamy,&nbsp;Ramprasath Jayaprakash,&nbsp;Vivek Duraivelu,&nbsp;Satheesh Kumar Sabapathy","doi":"10.1111/jph.70074","DOIUrl":"https://doi.org/10.1111/jph.70074","url":null,"abstract":"<div>\u0000 \u0000 <p>Rice leaf disease affects the leaves of the rice plant that are caused by fungi, bacteria or viruses. Leaf disease leads to yellowing, wilting or lesions on the leaves, which affects photosynthesis and minimises crop production. General rice leaf diseases include rice blast, bacterial blight, and leaf smut, which reduce food production and the economic stability of farmers. Hence, rice plant leaf disease detection is an important aspect, which ensures healthy crop yields. Many methods have been proposed for rice plant leaf disease detection, but they did not fully handle the variability in disease symptoms. Therefore, Siamese Zeiler and Fergus Forward Taylor Network (S-ZFFTNet) is developed for rice plant leaf disease detection. First, leaf disease images are collected from the rice leaf bacterial and fungal disease dataset and denoised by anisotropic diffusion. The plant leaf is segmented by conditional Generative Adversarial Network (cGAN). Then, the segmented image is augmented by rotation, colour change, and scaling factor. Then, Fuzzy Local Binary Patterns (FLBP) with wavelet transform features are excerpted from an augmented image. In the rice plant leaf disease detection phase, a new hybrid S-ZFFTNet is utilised, which is the unification of the Siamese Convolutional Neural Network (SCNN), Zeiler and Fergus Network (ZF-Net), and Taylor's series. The results acquired by S-ZFFT-Net are 92.654% of accuracy, 94.654% True Positive Rate (TPR), 91.757% True Negative Rate (TNR), 90.866% precision, and 92.721% F1-score for k fold value 8.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":"173 3","pages":""},"PeriodicalIF":1.1,"publicationDate":"2025-06-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144299778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}