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ارزیابی جمعیت گونه های رایج آفات انباری در میوه خرمای رقم زاهدی بر اساس روش طیف سنجی | ||
| آفات و بیماری های گیاهی | ||
| مقاله 9، دوره 87، شماره 2 - شماره پیاپی 109، اسفند 1398، صفحه 253-263 اصل مقاله (806.39 K) | ||
| نوع مقاله: حشره شناسی کشاورزی | ||
| شناسه دیجیتال (DOI): 10.22092/jaep.2020.127812.1303 | ||
| نویسنده | ||
| مسعود لطیفیان* | ||
| عضو هیات علمی/ پژوهشکده خرما و میوه های گرمسیری | ||
| چکیده | ||
| این پژوهش برای تدوین برنامه نمونهبرداری آفات انباری خرما شامل: شپشه دندانهدار Oryzaephilus surinamensis (Linnaeus)، شب پره آرد Ephestia kueheniella (Zeller) و شب پره هندی interpunctella (Hübner) Plodiaدر خرمای رقم زاهدی با استفاده از روش طیف سنجی نوری انجام شد. آزمایش در قالب طرح کاملاً تصادفی و بهصورت فاکتوریل انجام شد. فاکتور اول شامل مراحل تخم، لارو، شفیره و حشره کامل و فاکتور دوم ده تراکم 5، 10، 15، 20، 25، 30، 35، 40، 45 و 50 عدد از هر مرحله بود. نتایج نشان داد که طول موج حداکثر جذب برای تخم، لارو، شفیره و حشره کامل شپشه دندانهدار بهترتیب معادل 1220، 1240، 1280، 1300، شب پره آرد 1210 1270، 1320، 1360 و شب پره هندی 1310، 1320، 1380، 1400 نانومتر بود. تعداد نمونه لازم (هر نمونه 110 گرم میوه) برای ارزیابی صحیح تخم، لارو، شفیره و حشره کامل بهترتیب برای شپشه دندانهدار معادل 1، 2،1 ،3 ، شب پره آرد 1، 1، 3، 3 و شبپره خشکبار 1، 1، 2 و 3 نمونه بود. از دو مؤلفه تغییرات نسبی (RV) و دقت نسبی شبکه (RNP) برای ارزیابی کارایی استفاده شد. مقدار شاخص RV برای آفات در مراحل رشدی بهترتیب برای شپشه دندانهدار 23/2، 26/3، 15/3، 52/2، شب پره آرد 42/1، 64/1، 78/1، 71/3 و شب پره هندی 23/2، 27/3، 15/3 و 52/3 بود. در تمام موارد، خطای نمونهبرداری کمتر از 10 درصد بود. مقدار شاخصRNP در چهار مرحله رشدی بهترتیب برای شپشه دندانهدار 79/39، 06/18، 39/32، 37/22 شبپره آرد 54/22، 58/27، 15/3، 33/10 و شبپره هندی 51/36، 71/23، 58/15 و 99/10 بود. با توجه بهنتایج، روش طیف سنجی، توانایی تشخیص مراحل پنهان (تخم و شفیره) آفات را با حداکثر دقت و حداقل هزینه دارد. | ||
| کلیدواژهها | ||
| آفات انباری؛ خرما؛ طیف سنجی نوری؛ نمونه برداری | ||
| عنوان مقاله [English] | ||
| Population Assessment of Common Stored Pest Species in Date Fruit Zahedi Cv. Based on Spectroscopic Method | ||
| نویسندگان [English] | ||
| Masoud Latifian | ||
| چکیده [English] | ||
| This study aimed to define sampling programs for pests, Oryzaephilus surinamensis (Linnaeus), Ephestia kueheniella (Zeller) and Plodia interpunctella (Hübner) using spectrophotometer in date Zahedi cultivar. The experiments were conducted in a completely randomized design with factorial arrangement. The first factor consisted of eggs, larvae, pupae and adult and the second including 5, 10, 15, 20, 25, 30, 35, 40, 45 and 50 densities of the above stages. Results showed that the maximum absorption wavelength for egg, larva, pupa and adult of O. surinamensis, was 1220, 1240, 1280, 1300 nm, for E. kuheniella 1210, 1270, 1320, 1360 nm and for P. interpunctella 1310, 1320, 1380, 1400 nm, respectively. The lowest number of sampling (each sample 110 g date fruits) for an accurate estimation of the egg, larva, pupa and adult of O. surinamensis were 1, 2, 1, 2, for E. kuheniella 1, 1, 3, 3 and for P. interpunctella 1, 1, 2 3, 2 samples, respectively, Relative Variation )RV( and Relative net precision )RNP( indices were used to validate the sampling accuracy. The RV for development stages of O. surinamensis were 2.23, 3.26, 3.15, 2.52, for E. kuheniella 2.52, 1.42, 1.64, 1.78, 3.71 and for P. interpunctella 2.23, 3.27, 3.15, 3.52 respectively. The accuracy level of samplings was lower than 10 in all cases, The RNP values for O. surinamensis were 39.79, 18.06, 32.29, 22.37, for E. kuheniella 22.54, 27.58, 13.15, 10.33 and for P.interpunctella 36.51, 23.71, 15.58 10.99 respectively. Based on the results, the spectrophotometer could detect the hidden pest stages (egg and pupa) with maximum accuracy and minimum cost. | ||
| کلیدواژهها [English] | ||
| Date palm, sampling, spectrophotometer, storage pests | ||
| مراجع | ||
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ARBOGAST, R. T. and MAKIN, R. W. 1999. The utility of spatial analysis in management of storage pests. In ‘‘Stored Product Protection: Proceedings of the 7th International Working Conference on Stored-product Protection’’ (J. Xuxun, L. Quan, L. Yongsheng, T. Xianchang, and G. Lianghua, eds), pp. 1519-1527. Sichuan Publishing House of Science and Technology, Chengdu, China. BOWDITCH, T. G. and MADDEN, J. L. 1996. Spatial and temporal distribution of Ephestia cautella (Walker) (Lepidoptera: Pyralidae) in a confectionary factory: Causal factors and management implications.Journal of Stored Products Research. 32, 123-130. BURKS, C. S., DOWELL, F. E., and XIE, F. 2000. Measuring fig quality using near-infrared spectroscopy. Journal of Stored Products Research. 36, 289-296. CHAMBERS, J. and RIDGWAY, C. 1996. Rapid detection of contaminants in cereals. In ‘‘Near Infrared Spectroscopy. The Future Waves’’ (A.M.C. Davies and P. Williams, eds), pp. 484-489. NIR Publications, Chichester, UK. CHAMBERS, J. and RIDGWAY, C. C., DAVIES, E.R., MASON, D. R., and BATEMAN,M. W. 1998. Rapid Automated Detection of Insects and Certain Other Contaminants in Cereals. Home-Grown Cereals Authority Project report no. 152. London, UK. CHAMBERS, J. and RIDGWAY, C. and DAVIES, E. R. 2001. Design of an Integrated Machine Vision System Capable of Detecting Hidden Infestation in Wheat Grains. Home-Grown Cereals Authority Project report no. 262. London, UK. DOWELL, F. E., THRONE, J. E., WANG, D., and BAKER, J. E. 1998. Automated nondestructive detection of internal insect infestation of wheat kernels by using near-infrared reflectance spectroscopy. Journal of Economic Entomology,91, 899-904. DUDHE P.B., CHAVARE P. D., and SHELKE P. S. 2017. Spectrophotometric Determination of Febuxostat from Bulk and Tablet Dosage Form by Area Under Curve Method, International Journal of ChemTech Research, 10 (6): 183-189. HEIDARY, N. LATIFIAN, M., and FALLAHZADEH, M. 2014. The spectrophotometry method to evaluating in the infestation level of developmental stages of Ephestia kuheniella in type date palm Kabkab. Plant Protection Journal. 6(1): 17-28. HUANG, J. DONG, M. LU, S. LI, W. Lu, J. LIU, C. YOO, J.H. 2018. Estimation of the mechanical properties of steel via LIBS combined with canonical correlation analysis (CCA) and support vector regression (SVR). JAAS, 33, 720-729. JINENDRA, B., TAMAKI, K., KUROKI, S., VASSILEVA, M., YOSHIDA, S. and TSENKOVA, R. 2010. Near infrared spectroscopy and aquaphotomics: Novel approach for rapid in vivo diagnosis of virus infected soybean. Biochemical and Biophysical Research Communications, 397: 685-90. LATIFIAN, M. RAD, B. and GHAMARI, M. 2013. Determination the population density of different development stags of Mediterranean meal moth Ephestia kuheniella Zell. in Date fruit Sayer cultivar based on spectrophotometry. Journal of Plant Protection, 27(4), 491-510. (in Persian with English summary) LATIFIAN, M. and RAD, B. 2015. Determination of the population densities of different development stags of Sawtoothed beetle Oryzaephilus surinamensis L. in Date fruit) Sayer cultivar bausing spectrophotometry method. Journal of Etomology Research. 6(4): 353-365. (in Persian with English summary) LU, J., ZHOU, M., GAO, Y., and JIANG, H. 2017. Using hyperspectral imaging to discriminate yellow leaf curl disease in tomato leaves. Precision Agriculture, 1-16. MAHLEIN, A. K. 2016. Plant disease detection by imaging sensors parallels and specific demand for precision agriculture and plant phenotyping. Plant disease, 100: 241-251. MORRISON, W. R. BRUCE, A. WILKINS, R. V. and ALBIN, C. E. 2019. Influences of Stored Product Insect Movements on Integrated Pest Management Decisions. Insects. 77(10): 1-20. NAIDU, R. A., PERRY, E. M., PIERCE, F. J. and MEKURIA, T. 2009. The potential of spectral reflectance technique for detecting Grapevine leafroll-associated virus-3 in two red-berried wine grape cultivars. Computers and Electronics in Agriculture, 66: 38-45. PEREZ-MENDOZA, J., THRONE, J. E., DOWELL, F. E., and BAKER, J. E. 2003. Detection of insect fragments in wheat flour by near-infrared spectroscopy. Journal of Stored Products Research. 39, 305-312. RIDGWAY, C., DAVIES, R., and CHAMBERS, J. 2001. Imaging for the high-speed detection of pest insects and other contaminants in cereal grain in transit. Paper presented at the 2001 ASAE Annual International Meeting, California, July 30-Aug. 1, 2001. SANKARAN, S., MISHRA, A., EHSANI, R. and DAVIS, C. 2010. A review of advanced techniques for detecting plant diseases. Computers and Electronics in Agriculture, 72: 1-13. SOLA, M. 2016. Detection, identification, and quantification of insect infestation in stored products using PCR approaches. 10.1603/ICE.2016.111999. THRONE, J. E., DOWELL, F. E., PEREZ-MENDOZA, J., and BAKER, J. E. 2003. Entomological applications of near-infrared spectroscopy. In ‘‘Proceedings of the 8th International Working Conference on Stored-product Protection’’ (P. F. Credland, D. M. Armitage, C. H. Bell, P. M. Cogan, and E. Highley, eds), pp. 131-134. CAB International, Wallingford, UK. WILKIN, D. R., COWE, I. A., THIND, B. B., MCNICOL, J. W., and CUTHBERTSON, D. C. 1986. The detection and measurement of mite infestation in animal feed using near infra-red reflectance. Journal of Agricultural Science,107, 439-448. WILKIN, D. R. and FLEURAT-LESSARD, F. 1991. The detection of insects in grain using conventional sampling spears. In ‘‘Proceedings of the 5th International Working Conference on Storedproduct Protection’’ (F. Fleurat-Lessard and P. Ducom, eds), pp. 1445-1453. Bordeaux, France. WILLIAMS, P. and NORRIS, K. 2001. ‘‘Near-infrared Technology in the Agricultural and Food Industries’’. American Association of Cereal Chemists, St. Paul, MN. ZAYAS, I. Y. and FLINN, P. W. 1998. Detection of insects in bulk wheat samples with machine vision. Transactions of the ASAE, 41, 883-888. | ||
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