Journal of Phytopathology最新文献

{"title":"Characterisation of the Phenotypic Reaction of Brazilian Soybean Genotypes to Sclerotinia sclerotiorum Under Controlled Conditions","authors":"A. B. Borges,&nbsp;J. Huzar-Novakowiski,&nbsp;M. Pasquali,&nbsp;D. Baretta","doi":"10.1111/jph.13428","DOIUrl":"https://doi.org/10.1111/jph.13428","url":null,"abstract":"<div>\u0000 \u0000 <p>White mould is a disease caused by the fungus <i>Sclerotinia sclerotiorum</i>, and it is considered one of the most devastating diseases in soybean crops, causing huge losses in productivity. In this study, we aimed to contribute to the genetic control of this disease by characterising the phenotypic reaction of 67 Brazilian soybean genotypes to <i>S. sclerotiorum</i> under controlled conditions. Plants were artificially inoculated with mycelium discs when they reached the four-node phenological stage. Symptoms of white mould developed in all soybean genotypes. The resistance reaction was characterised by measuring the length of lesions on the main stem at 7 days after inoculation. Of the 67 genotypes evaluated, nine showed greater resistance to <i>S. sclerotiorum</i>, including 17S-01443-L8, 16S-00630-L4, GER_00003, 13S-00001-L2/FPS 2457 RR, BMX Torque I2X, P95Y02 IPRO, 17S-00842-L5, HO Pirapó IPRO and TMG 2359 IPRO. Twenty-three genotypes showed an intermediate response, while 35 genotypes showed greater susceptibility to <i>S. sclerotiorum</i>. Further studies should be conducted under field conditions with the soybean genotypes that showed greater resistance response to <i>S. sclerotiorum</i> under controlled conditions.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-11-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142574009","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":"Spark Architecture and Ensemble-Based Feature Selection With Hybrid Optimisation Enabled Deep Long Short-Term Memory for Crop Yield Prediction","authors":"Anitha Rajathi Surendran,&nbsp;Arun Sahayadhas","doi":"10.1111/jph.13408","DOIUrl":"https://doi.org/10.1111/jph.13408","url":null,"abstract":"<div>\u0000 \u0000 <p>Precise prediction of crop yield is crucial for addressing the economic resilience and food security of agricultural countries. Current models for crop yield prediction struggle to fully understand the long-term trends and seasonal variations. Here, the Fractional Rider-Based Water Cycle Algorithm-Based Deep Long Short-Term Memory (FRWCA-DLSTM) is devised for crop production forecasting and addresses these issues. Primarily, the simulation of the IoT is performed. Then, the selection of Cluster Head (CH) and routing are done with the Rider-Based Water Cycle Optimisation (RWCO). Then, the crop production data are accumulated at the Base Station (BS), where Spark architecture is used for crop prediction. Here, the data partitioning is done using Deep Fuzzy Clustering (DFC). Next, the technical indicators are extracted. Then, the ensemble-based Feature selection is accomplished. Here, the ranking techniques are combined by a fusion function. The weight parameters are tuned by Hunter-Sparrow Search Optimisation (HSSO). Finally, the crop yield prediction is performed by DLSTM, which is trained using FRWCA. The FRWCA is developed by merging Fractional Calculus (FC) with RWCO. The performance of FRWCA-DLSTM shows the minimum mean absolute percentage error (MAPE), mean square error (MSE) and root mean square error (RMSE) of 0.103, 0.081 and 0.284, respectively.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573947","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":"Pathogenic Variability and Race Structure of Colletotrichum lindemuthianum Isolates From Common Bean in Ethiopia","authors":"Tizazu Degu,&nbsp;Tesfaye Alemu,&nbsp;Asnake Desalegn,&nbsp;Alemayehu Assefa,&nbsp;Berhanu Amsalu Fenta","doi":"10.1111/jph.13421","DOIUrl":"https://doi.org/10.1111/jph.13421","url":null,"abstract":"<div>\u0000 \u0000 <p>Bean anthracnose, caused by <i>Colletothricum lindemuthianum</i>, poses a significant threat to common bean production in Ethiopia. The objective of this study was to determine the pathogenic variability and race structure and distribution of bean anthracnose in four selected zones (Metekel, Sidama, Wolaita and Halaba) of Ethiopia. Field surveys were conducted at 5–7 km intervals, focusing on diseased plant parts of common bean. The severity, incidence and prevalence of bean anthracnose were assessed and determined, and a total of 74 bean anthracnose isolates were obtained for further analysis. The isolates were tested on the 12 standard differential cultivars. The results revealed a wide range of pathogenic variability, with severity ranging from 0% to 24.1%, incidence from 0% to 87% and prevalence from 0% to 100%. The 74 isolates were classified into 32 distinct pathogenic races with 20 of them being newly identified races specific to Ethiopia. Dibate district had the highest number of races (12), whereas Sodo_Zuria and Borecha district had only one. Race 128 was widely distributed, and race 3770 was the most virulent, infecting 8 of the 12 differential cultivars. Around 14% of the isolates were virulent to one differential cultivar, whereas 4.1% to eight cultivars. These findings have important implications for developing resistant cultivars by deploying resistance genes into an improved cultivar and ensuring sustainable common bean production in Ethiopia and other related countries.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142573948","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 QoI-Resistant Isolates of the Banana Pathogen Pseudocercospora fijiensis in Mexico","authors":"Gilberto Manzo-Sánchez,&nbsp;Luis Carlos Salazar-Licea,&nbsp;Marco Tulio Buenrostro-Nava,&nbsp;Carlos L. Leopardi-Verde,&nbsp;Luciano Martínez-Bolaños,&nbsp;Ignacio Islas-Flores,&nbsp;Mario Orozco-Santos,&nbsp;Blondy Canto-Canché","doi":"10.1111/jph.13427","DOIUrl":"https://doi.org/10.1111/jph.13427","url":null,"abstract":"<div>\u0000 \u0000 <p>Black Sigatoka disease is a significant threat to banana (<i>Musa</i> spp.) fruit yield and quality. For the control of the fungal pathogen, <i>Pseudocercospora fijiensis</i>, producers currently rely on fungicides such as Quinone outside Inhibitor (QoI). In this study, we examined the resistance status of <i>P. fijiensis</i> to QoI fungicides using 80 isolates from 24 localities in the main banana-producing areas of Mexico (Colima, Michoacán, Tabasco and Chiapas). Resistance was evaluated using the RFLP-PCR mutation assay on Cytochrome b gen (Cyt<i>b</i>). The results showed the G143A mutation in three isolates from Chiapas, indicating a relatively low mutation frequency in the sampled areas, where additionally, a microplate bioassay confirmed the resistance to fungicides. We also evaluated the genetic structure and differentiation among the sampled populations, detecting differences between populations within each region and among all populations. Furthermore, our analysis revealed shared haplotypes between resistant populations in Chiapas and nonresistant populations in Michoacán. These findings provide valuable insight into the resistance status of <i>P. fijiensis</i> to QoI fungicides in Mexico and serve as foundation for the development of strategies to manage strobilurin resistance in the country. Overall, this study highlights the importance of monitoring and implementing effective management practices to mitigate the spread of resistant strains.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-11-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142574065","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":"In Situ Diagnosis and Digital Cataloguing of Plant Pathogenic Fungi Through Mobile-Based Foldscope Microscopy","authors":"Palanisamy Mooventhan,&nbsp;Palaiyur Nanjappan Sivalingam,&nbsp;Harvinder Kumar Singh,&nbsp;Manoj Kumar Sahu,&nbsp;Yogita Dhimar,&nbsp;Uttam Singh,&nbsp;Pankaj Kaushal,&nbsp;Probir Kumar Ghosh","doi":"10.1111/jph.13422","DOIUrl":"https://doi.org/10.1111/jph.13422","url":null,"abstract":"<div>\u0000 \u0000 <p>Agriculture confronts multifaceted challenges across the spectrum of crop production, with pest and disease management being a prominent concern. Timely diagnosis of crop diseases is imperative for mitigating production costs and curbing the adverse environmental impacts of chemical pesticides. In the present investigation, mobile phone-based foldscope microscopy (MBFM) was used to diagnose various field samples infected with fungal diseases of field and horticultural crops, and the same was validated with the normal microscope pictures and field symptoms. The MBFM was also used to diagnose seed-borne microflora associated with wheat and spores of commercial formulation of bioagents and validated. The MBFM utilises both symptoms and morphological structures of pathogen for in situ field diagnosis and hence advantages over the symptom-based mobile Apps. This study underscores the utility of foldscope microscope as a potent technique for plant pathologists and extension workers to enable real-time and in situ identification of diseases caused by fungal pathogens in various agricultural crops.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142555496","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":"Remora-CNN: A Novel and Effective Method for Rice Leaf Disease Detection and Classification","authors":"Devchand J. Chaudhari,&nbsp;Malathi Karunakaran","doi":"10.1111/jph.13411","DOIUrl":"https://doi.org/10.1111/jph.13411","url":null,"abstract":"<div>\u0000 \u0000 <p>For millions of people worldwide, rice is one of the main food crops. Nevertheless, while being grown, rice is susceptible to many diseases. Most rice plant diseases are influenced by biotic and abiotic factors, including nematodes, viroids, fungus, viruses, bacteria, and other microorganisms, as well as temperature and other environmental factors. Thus, an automatic early classification of leaf disease is necessary to improve the rice yield. In this paper, for identifying and categorizing the rice leaf disease, a convolutional neural network (CNN) model is used, and the CNN is trained using the Remora Optimization Algorithm (ROA). A better classification outcome is attained by performing the segmentation process using <i>K</i>-means with the Fractional Tangential-Spherical Kernel (FTSK) algorithm. Furthermore, the developed Remora Optimization- Convolutional Neural Network (Remora-CNN) method achieved the optimal performance based on the testing accuracy, sensitivity and specificity of 0.925, 0.931, and 0.941 using the Rice Leaf Disease Image Samples Dataset.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525306","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 Rust Resistance Genes in Wheat (Triticum aestivum L.) Using Molecular Markers and Host–Pathogen Interaction Tests","authors":"Dharam Pal,&nbsp;Subodh Kumar,&nbsp;Subhash Chander Bhardwaj,&nbsp;Om Prakash Gangwar,&nbsp;Anjali Pal,&nbsp;Madhu Patial,&nbsp;Santosh Watpade,&nbsp; Harikrishna,&nbsp;Niharika Mallick,&nbsp;Vikas Fandade,&nbsp;J. K. Roy","doi":"10.1111/jph.13417","DOIUrl":"https://doi.org/10.1111/jph.13417","url":null,"abstract":"<div>\u0000 \u0000 <p>The leaf rust (<i>Puccinia triticina</i> f. sp. <i>tritici</i>), stripe rust (<i>Puccinia striiformis</i> f. sp. <i>tritici</i>), and stem rust (<i>Puccinia graminis</i> f. sp. <i>tritici</i>) are major fungal constraints affecting wheat production worldwide. Identifying and deploying wheat varieties with diverse resistance are the best ways to manage all the rusts. Therefore, a continuous search goes on to identify diverse germplasm with effective rust resistance that expresses at different stages of plant growth (seedling and adult plant). A set of 22 rust resistant wheat genotypes and 4 checks (controls), viz., Avocet-Yr10, Avocet -Yr15, Agra Local, and respective positive checks were studied for characterising rust resistance genes using host–pathogen interactions complemented by molecular markers. Among 22 elite genotypes, 05 genotypes amplified 191 bp fragment with marker <i>PSY1E1</i>, confirmed the presence of gene <i>Lr19</i>/<i>Sr25</i>. These genotypes also expressed resistance to most virulent leaf rust pathotypes, 77-5 and 77-9 in host–pathogen interaction test (HPI). Seven genotypes showed the presence of <i>Lr34/Yr18/Sr57/Pm38/Ltn1</i> in homozygous state, whereas G4 showed its presence in heterozygous condition. Among 22 genotypes, 16 genotypes possessed <i>Yr10</i>. Five genotypes (22.7%) exhibited two gene combinations, <i>Lr19/Sr25</i>, and <i>Yr10</i> as revealed through the detection of 191 bp fragment with marker <i>PSY1E1</i> and 260 bp fragment with co-dominantly inherited microsatellite marker <i>Xpsp3000</i>, respectively. All five genotypes (G2, G3, G8, G9, and G18) also expressed brown glumes controlled by the gene <i>Rg1</i> tightly linked to <i>Yr10</i> on the 1BS chromosome. Broad spectrum rust resistance present in these lines in good agronomic backgrounds could be used as potent genetic donors for diverse and durable rust resistance breeding programmes in wheat.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525224","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":"First Report of the Root-Knot Nematode Meloidogyne incognita Infecting Ficus tikoua in China","authors":"Yu Gan,&nbsp;Ju-Kui Ma,&nbsp;Li-Jun Liao,&nbsp;Xiang-Rui Meng,&nbsp;Yan Chen","doi":"10.1111/jph.13419","DOIUrl":"https://doi.org/10.1111/jph.13419","url":null,"abstract":"<div>\u0000 \u0000 <p><i>Ficus tikoua</i> can be used as resources for fruit and herbal medicine inspiring large-scale planting in China. Nodular galls were found on the roots of the planted <i>F. tikoua</i> individuals in 2022. The mature females and egg masses of nematodes were extracted from the nodular galls, and microscopic examination suggested <i>Meloidogyne</i> infection. Molecular identification based on ITS, 18S rRNA sequences assigned the infecting species to <i>Meloidogyne incognita</i>, which was further demonstrated by the successful application of specific SCAR marker <i>M. incognita</i> in all samples. The pathogenicity of <i>M. incognita</i> was conducted on <i>F. tikoua</i> cutting seedlings based on Koch's postulates. This is the first report of <i>M. incognita</i> infection in <i>F. tikoua</i> in China.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524961","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":"Phomopsis Head Rot caused by Diaporthe helianthi: A New Disease on Sunflower in Argentina","authors":"D. N. Colombo,&nbsp;A. Figueruelo,&nbsp;N. B. Paniego,&nbsp;R. M. Comerio,&nbsp;A. Corró Molas","doi":"10.1111/jph.13416","DOIUrl":"https://doi.org/10.1111/jph.13416","url":null,"abstract":"<div>\u0000 \u0000 <p>During a survey conducted in the 2021, 2022 and 2023 seasons, sunflower heads showing symptoms of dry rot were collected from 80 fields in the semi-arid Pampa region of Argentina. A total of 72% of the fields examined in 2021, 71% in 2022 and 63% in 2023 showed the presence of the disease. The highest incidence was recorded as 70% in 2021. Phomopsis head rot began as dry, brown spots on the back of the head, progressing to necrosis and twisting of adjacent leaves. Infected head tissue samples were surface sterilised, cultured on potato dextrose agar (PDA) and incubated at 25°C for 14 days. <i>Diaporthe helianthi</i> was identified based on cultural and morphological characteristics as well as molecular data. A phylogenetic analysis was performed. Internal transcribed spacer (ITS), translation elongation factor 1-α (<i>ef1-α</i>) and β-tubulin sequences were deposited in GenBank, showing the identity with the ex-type <i>D. helianthi</i> strain CBS 592.81. Pathogenicity experiments confirmed the presence of similar disease symptoms in inoculated sunflower heads, and <i>D. helianthi</i> was consistently reisolated from these organs. Our Koch's postulates testing results on heads constitute the first confirmed report that <i>D. helianthi</i> is the cause of Phomopsis head rot on sunflower in Argentina.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524960","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":"Occurrence, Complete Genome Sequencing, and Development of Diagnostics for Black Pepper Virus F Infecting Black Pepper in India","authors":"P. Malavika,&nbsp;M. Greeshma,&nbsp;A. I. Bhat","doi":"10.1111/jph.13418","DOIUrl":"https://doi.org/10.1111/jph.13418","url":null,"abstract":"<div>\u0000 \u0000 <p>The occurrence of black pepper virus F (BPVF) was identified for the first time from India, and its complete genome sequence was determined using overlapping fragments obtained through reverse transcription polymerase chain reaction (RT-PCR). The RNA 1 and RNA 2 of the Indian isolate of BPVF (BPVF-IND) contained 6376 and 3340 nucleotides potentially coding for proteins of 230.7 kDa and 114 kDa respectively. Comparison of the RNA 1 sequence of BPVF-IND with that of BPVF from Brazil (BPVF- BR-PA) and China (BPVF- ZYP-1) revealed an identity of 95% and 90%, respectively, while RNA 2 showed an identity of 96% and 90%. The phylogenetic analysis of the Pro-Pol region of RNA 1 and coat protein region of RNA 2 revealed close clustering of all three BPVF isolates well separated from other species of the genus, <i>Fabavirus</i>. Diagnostics assays based on the RT-PCR and RT-recombinase polymerase amplification (RT-RPA) were developed for the sensitive detection of the virus that will help in the identification and propagation of virus-free black pepper plants.</p>\u0000 </div>","PeriodicalId":16843,"journal":{"name":"Journal of Phytopathology","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2024-10-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142524917","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}