Application of intelligent spore capture system in prediction of cucumber downy mildew and cucumber powdery mildew
Vol 2, Issue 1, 2019
VIEWS - 786 (Abstract) 593 (PDF)
Abstract
In order to explore the application of the new integrated intelligent spore capture system developed in China in the prediction of cucumber downy mildew and cucumber powdery mildew, the main working parameters of the integrated intelligent spore capture system, such as the presence or absence of air cutting head, the height of air collection port and the time of air collection, were optimized by identifying the morphology of captured spores in the case of natural disease in the field. The relationship between the disease index of cucumber downy mildew and cucumber powdery mildew in greenhouse and the amount of spores captured was analyzed through the dynamic monitoring of disease and spores. The results show that when the air cutting head is not installed, the height of the air collection port is 70 cm, and the period of 10: 00–10: 30 was beneficial to the capture of spores. The disease index of cucumber downy mildew and cucumber powdery mildew had a strong positive correlation with the total amount of spores captured for 7 consecutive days. Continuous monitoring of cucumber downy mildew sporangia and rapid increase in the number is a predictor of the occurrence or rapid increase of cucumber downy mildew. The conidia of cucumber powdery mildew were not detected before the disease onset, and the number of conidia captured was still small at the peak of the disease. The research shows that the integrated intelligent spore capture system is suitable for the prediction of cucumber downy mildew, but there are still some problems in the prediction of cucumber powdery mildew.
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1. Liu Y, Zhang Y, Cai N, et al. Advances in pathogen and resistance of Pseudoperonospora cubensis. Journal of Northeast Agricultural University 2009; 40(4): 127–131.
2. Li D, Cui H, Liu F, et al. Evaluation and analysis of resistance to cucumber downy mildew under low light stress. Journal of Plant Protection 2016; 43(4): 621–626.
3. Xiong Y, Wang H, Xiang H, et al. 2016. Research progress of cucumber powdery mildew. Chinese Agricultural Science Bulletin 2016; 32(1): 130–135.
4. Yue H, Wu X, Hao J, et al. Status and prospects in molecular breeding of powdery mildew resistance of cucumber. Journal of Plant Genetic Resources 2014; 15 (1): 120–128.
5. Ren L, Zhao B, Han J, et al. Sensitivity of Podosphaera xanthii to thiophanate-methyl and characteristics of resistant mutants. Journal of Plant Protection 2015; 42(2): 176–181.
6. Qu L, Qin Z. Advance on cucumber resistance to powdery mildew. Journal of Northeast Agricultural University 2007; 38(6): 835–841.
7. Kong X, Zhang X, Zhang J, et al. Study on decision-making system for forecasting cucumber downy mildew in the field and its application. Chinese Agricultural Science Bulletin 1997; 13(6): 29–30.
8. He Z, Yu H, Zhu T, et al. The prediction model for the epidemiological rate of cucumber downy mildew in Guangzhou region. Plant Protection 2001; 27(5): 10–12.
9. Qian S, Huo Z, Ye C. Long-term meteorological prediction research on epidemic of wheat powdery mildew in China. Journal of Natural Disasters 2005; 14(4): 56–63.
10. Jiang Y, Luo J, Luo D, et al. Monitoring effect of remote-controlled spore trap on wheat aero-borne diseases. Plant Protection 2015; 41(6): 163–168.
11. Cao X, Zhou Y. 2016. Progress in monitoring and forecasting of plant disease. Plant Protection 42(3): 1–7.
12. Neufeld KN, Isard SA, Ojiambo PS. Relationship between disease severity and escape of Pseudoperonospora cubensis sporangia from a cucumber canopy during downy mildew epidemics. Plant Pathology 2013; 62(6): 1366–1377.
13. Granke LL, Morrice JJ, Hausbeck MK. Relationships between airborne Pseudoperonospora cubensis sporangia, environmental conditions, and cucumber downy mildew severity. Plant Disease 2014; 98(5): 674–681.
14. Zhou Y, Duan X, Cheng D. Estimation of disease severity of wheat powdery mildew by using data of pathogen spore trap. Acta Phytopathologica Sinica 2007; 37(3): 307–309.
15. Yao D. Application of remote sensing and pathogen conidia trap for monitoring of wheat powdery mildew [Master’s thesis]. Hefei: Anhui Agricultural University; 2013.
16. Choudhury RA, Koike ST, Fox AD, et al. Season-long dynamics of spinach downy mildew determined by spore trapping and disease incidence. Phytopathology 2016; 106(11): 1311–1318.
17. Zhao Y, Liu Y. Studies on the predictive model for disease development period and seed yield of ginseng Alternaria blight. Acta Phytopathologica Sinica 1991; 21(3): 211–215.
18. Liu W, Yao DM, Fan JR, et al. Dynamic monitoring of aerial conidia of Blumeria graminis f. sp. tritici in wheat fields. Acta Phytopathologica Sinica 2016; 46(1): 112–118.
DOI: https://doi.org/10.24294/th.v2i1.1778
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