Littérature scientifique sur le sujet « APPLE FOLIAR DISEASE »
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Articles de revues sur le sujet "APPLE FOLIAR DISEASE"
Sulaiman, Adel, Vatsala Anand, Sheifali Gupta, Hani Alshahrani, Mana Saleh Al Reshan, Adel Rajab, Asadullah Shaikh et Ahmad Taher Azar. « Sustainable Apple Disease Management Using an Intelligent Fine-Tuned Transfer Learning-Based Model ». Sustainability 15, no 17 (4 septembre 2023) : 13228. http://dx.doi.org/10.3390/su151713228.
Texte intégralCharan, Merugu Sai, Mohammed Abrar et Bejjam Vasundhara Devi. « Apple Leaf Diseases Classification Using CNN with Transfer Learning ». International Journal for Research in Applied Science and Engineering Technology 10, no 6 (30 juin 2022) : 1905–12. http://dx.doi.org/10.22214/ijraset.2022.44176.
Texte intégralAshritha, K., K. Sandhya, Y. Uday Kiran et V. N. L. N. Murthy. « Plant-Leaf Disease Prediction Using Deep Learning ». International Journal for Research in Applied Science and Engineering Technology 11, no 3 (31 mars 2023) : 121–28. http://dx.doi.org/10.22214/ijraset.2023.49338.
Texte intégralBlaedow, Ryan, William Chaney, Paul Pecknold et Harvey Holt. « Investigation of Fungicidal Properties of the Tree Growth Regulator Paclobutrazol to Control Apple Scab ». Arboriculture & ; Urban Forestry 32, no 2 (1 mars 2006) : 67–73. http://dx.doi.org/10.48044/jauf.2006.009.
Texte intégralHirakue, Ayumi, et Shuichi Sugiyama. « Relationship between foliar endophytes and apple cultivar disease resistance in an organic orchard ». Biological Control 127 (décembre 2018) : 139–44. http://dx.doi.org/10.1016/j.biocontrol.2018.09.007.
Texte intégralPeng, H. X., X. Y. Wei, Y. X. Xiao, Y. Sun, A. R. Biggs, M. L. Gleason, S. P. Shang, M. Q. Zhu, Y. Z. Guo et G. Y. Sun. « Management of Valsa Canker on Apple with Adjustments to Potassium Nutrition ». Plant Disease 100, no 5 (mai 2016) : 884–89. http://dx.doi.org/10.1094/pdis-09-15-0970-re.
Texte intégralProkopy, Ronald J. « A LOW-SPRAY APPLE-PEST-MANAGEMENT PROGRAM FOR SMALL ORCHARDS ». Canadian Entomologist 117, no 5 (mai 1985) : 581–85. http://dx.doi.org/10.4039/ent117581-5.
Texte intégralPercival, Glynn. « The Influence of Inducing Agents Applied by Soil Drenches on Disease Severity of Apple and Pear Scab ». Arboriculture & ; Urban Forestry 46, no 5 (1 septembre 2020) : 358–70. http://dx.doi.org/10.48044/jauf.2020.026.
Texte intégralRebel, P., C. Poblete-Echeverría, J. G. van Zyl, J. P. B. Wessels, C. Coetzer et A. McLeod. « Determining Mancozeb Deposition Benchmark Values on Apple Leaves for the Management of Venturia inaequalis ». Plant Disease 104, no 1 (janvier 2020) : 168–78. http://dx.doi.org/10.1094/pdis-04-19-0873-re.
Texte intégralProkopy, Ronald J., Daniel R. Cooley, Wesley R. Autio et William M. Coli. « Second-level integrated pest management in commercial apple orchards ». American Journal of Alternative Agriculture 9, no 4 (décembre 1994) : 148–56. http://dx.doi.org/10.1017/s0889189300005890.
Texte intégralThèses sur le sujet "APPLE FOLIAR DISEASE"
Furlan, Carla Regina Costa. « Avaliação da diversidade genética e da resistência a mancha foliar da gala em acessos de macieira do banco ativo de germoplasma da E.E.Epagri/Caçador ». Universidade do Estado de Santa Catarina, 2008. http://tede.udesc.br/handle/handle/1117.
Texte intégralIn this work were used 3 isolates of C. gloeosporioides from different apple producer places, and 245 access belong to the Active Germoplasm Banc Apple Tree Estação Experimental da EPAGRI de Caçador, Santa Catarina . Among the tested access, 185 were resistant and 32 susceptible. From those susceptible, there was not difference in the severity degree of the MFG attack. The data obtained in this study can help in the choice of resistant access, what could be used like parentage in the genetic breeding programs of the apple tree, in order to obtain the resistance to this pathogen.The genetic diversity characterization of the access by means of molecular marker studies, has been used like an important tool to maximize the maintenance work of the germoplasm banks. In this way, the objective of this study was characterize genetically the active germoplasm bank of the apple tree making use of 12 primers of SSR. The analysis of genetic diversity was realized through the DNA extraction of the 169 access, using young leafs. The extraction protocol used was the CTAB, and the estimation of the DNA concentration was done in agarose gel 0,8% coloured with bromide ethidium (0,3 μg/ml), with DNA Fago Lambda (20, 50, 100 e 200 ng/μl) like pattern. A total of 197 alleles were encountered, and the medium number of alleles by locus of SSR was 16,4. The medium heterozigosity expectation was 84%, and the revealed polimorphism by number of the alleles by locus had a high percentage (82%). The genetic similarity analysis showed two distinct groups. The gotten results had demonstrated the existence of high genetic variability in the bank of germoplasma of apple trees, salient for the raised number of alelos for I lease and high level of heterozigosidade. This knowledge will contribute with the improvement in the efficiency of the identification of combinations that will serve of base for the genetic improvement of the apple tree
No presente trabalho foram utilizados 245 genótipos pertencentes ao Banco de Germoplasma de macieira BGM, da Epagri / Estação Experimental de Caçador - EECd, SC. Para identificar os genótipos portadores de resistência à mancha foliar de glomerela - MFG, doença essa causada principalmente pelo fungo Colletotrichum gloeosporioides. Os genótipos foram inoculados com 3 isolados de C. gloeosporioides provenientes de diferentes regiões produtoras de maçã. Entre os genótipos testados, 187 (76,3%) manifestaram resistência e 58 (23,7%) manifestaram suscetibilidade à MFG. Entre as cultivares suscetíveis, não houve diferença no grau de severidade de ataque da MFG. Os resultados obtidos neste estudo têm, dentre outras, a finalidade de subsidiar os programas de melhoramento genético da macieira na seleção de parentais, objetivando a incorporação de resistência genética à MFG nas futuras novas cultivares. Para caracterizar os genótipos do Banco de Germoplasma de macieiras foram utilizados 12 primers de SSR. A análise de diversidade genética foi realizada através da extração do DNA de 169 genótipos, utilizando-se folhas jovens. O método de extração utilizado foi CTAB e a estimativa da concentração de DNA extraído foi feita em gel de agarose 0,8% com brometo de etídeo (0,3 μg/ml), tendo como padrão DNA Fago Lambda (20, 50, 100 e 200 ng/μl). Foi obtido um total de 197 alelos, sendo que o número médio de alelos por loco SSR foi de 16,4. A expectativa média de heterozigosidade foi de 84%, o polimorfismo revelado pelo número de alelos por loco teve uma percentagem alta de 82%. Pela análise de similaridade genética foram observados dois grupos. Os resultados obtidos demonstraram a existência de alta variabilidade genética no banco de germoplasma de macieiras, ressaltado pelo elevado número de alelos por loco e alto nível de heterozigosidade. Esse conhecimento contribuirá com a melhora na eficiência da identificação de combinações que servirão de base para o melhoramento genético da macieira
DAS, SARADINDU. « APPLE FOLIAR DISEASE DETECTION USING CONVOLUTIONAL NEURAL NETWORK BASED APPROACH ». Thesis, 2023. http://dspace.dtu.ac.in:8080/jspui/handle/repository/19825.
Texte intégralActes de conférences sur le sujet "APPLE FOLIAR DISEASE"
Dhivya Praba., R., R. Vennila., G. Rohini., S. Mithila. et K. Kavitha. « Foliar Disease Classification in Apple Trees ». Dans 2021 International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation (ICAECA). IEEE, 2021. http://dx.doi.org/10.1109/icaeca52838.2021.9675529.
Texte intégralBiswas, Barsha, et Rajesh Kumar Yadav. « Multilayer Convolutional Neural Network Based Approach to Detect Apple Foliar Disease ». Dans 2023 2nd International Conference for Innovation in Technology (INOCON). IEEE, 2023. http://dx.doi.org/10.1109/inocon57975.2023.10101125.
Texte intégralHaruna, Ahmed Abba, Ibrahim Ahmed Badi, L. J. Muhammad, Albaraa Abuobieda et Abdulaziz Altamimi. « CNN-LSTM Learning Approach for Classification of Foliar Disease of Apple ». Dans 2023 1st International Conference on Advanced Innovations in Smart Cities (ICAISC). IEEE, 2023. http://dx.doi.org/10.1109/icaisc56366.2023.10085039.
Texte intégralAhmed, Md Rayhan, Adnan Ferdous Ashrafi, Raihan Uddin Ahmed et Tanveer Ahmed. « MCFFA-Net : Multi-Contextual Feature Fusion and Attention Guided Network for Apple Foliar Disease Classification ». Dans 2022 25th International Conference on Computer and Information Technology (ICCIT). IEEE, 2022. http://dx.doi.org/10.1109/iccit57492.2022.10055790.
Texte intégralSujatha, Kamepalli, Ketepalli Gayatri, M. Srikanth Yadav, N. Chandra Sekhara Rao et Bandaru Srinivasa Rao. « Customized Deep CNN for Foliar Disease Prediction Based on Features Extracted from Apple Tree Leaves Images ». Dans 2022 International Interdisciplinary Humanitarian Conference for Sustainability (IIHC). IEEE, 2022. http://dx.doi.org/10.1109/iihc55949.2022.10060555.
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