| تعداد نشریات | 286 |
| تعداد نشریات سازمان | 84 |
| تعداد شماره‌ها | 3,006 |
| تعداد مقالات | 33,642 |
| تعداد مشاهده مقاله | 44,578,563 |
| تعداد دریافت فایل اصل مقاله | 31,659,562 |
مقایسه پراکنش فضایی کرم میوه گوجه‌فرنگی،Helicoverpa armigera ، با روش‌های زمین آمار و شبکه فازی-عصبی | ||
| آفات و بیماری های گیاهی | ||
| دوره 92، شماره 1 - شماره پیاپی 118، 1403، صفحه 57-66 اصل مقاله (628.52 K) | ||
| نوع مقاله: حشره شناسی کشاورزی | ||
| شناسه دیجیتال (DOI): 10.22092/jaep.2024.365217.1512 | ||
| نویسندگان | ||
| روناک محمدی1؛ عباسعلی زمانی* 2؛ حسنعلی واحدی3؛ حمیدرضا پوریان3؛ بهرام تفقدی نیا4 | ||
| 1گروه گیاهپزشکی، دانشگاه رازی کرمانشاه | ||
| 21- گروه گیاهپزشکی، دانشکده کشاورزی، دانشگاه رازی، کرمانشاه | ||
| 3گروه گیاهپزشکی، دانشگاه رازی | ||
| 4سازمان پژوهش‌های علمی و صنعتی ایران | ||
| چکیده | ||
| با وجود روش‌های آماری قوی و پیدایش شبکه‌های فازی- عصبی، مدل‌های پیشبینی کننده پراکنش موجودات بهسرعت در علم اکولوژی توسعه پیدا کرده است. با توجه به دشواری نمونه برداری، معمولاً در این گونه مطالعات تعداد نمونه کافی وجود ندارد. لذا در این پژوهش به مقایسه روش زمین آمار و شبکه ی فازی-عصبی جهت تخمین پراکندگی کرم میوه گوجه فرنگی، Helicoverpa armigera (Hubner)(Lep., Noctuidae)، در مزرعه گوجه فرنگی شهر کرمانشاه پرداخته شد. بدین منظور، مختصات طول و عرض جغرافیایی نقاط نمونه برداری در سطح مزرعه مشخص و بهعنوان ورودی‌های هر دو روش تعریف شد. خروجی هر روش نیز، تعداد این آفت در آن نقاط بود. در بخش زمین‌آمار، از روش کریجینگ معمولی و در بخش شبکه فازی-عصبی مصنوعی، از تابع فعال سازی سیگموئیدی استفاده شد. مقایسه نتایج زمین‌آمار و شبکه فازی – عصبی، بیانگر توانایی بالای شبکه فازی - عصبی در مقایسه با روش زمین‌آمار بود، بهطوری که ضریب تبیین برای شبکه فازی - عصبی و زمین آمار بهترتیب 9/0 و 6/0 بهدست آمد. در مجموع میتوان نتیجه گرفت که روش شبکه فازی- عصبی با تلفیق دو عامل طول و عرض جغرافیایی و تراکم جمعیت آفت، قادر به پیشبینی تراکم کرم میوه گوجه فرنگی با دقت بسیار مناسب است. | ||
| کلیدواژه‌ها | ||
| شبکه فازی- عصبی (ANFIS)؛ کریجینگ؛ Helicoverpa armigera | ||
| عنوان مقاله [English] | ||
| Comparison of Spatial distribution of tomato fruit borer, Helicoverpa armigera using geostatistics and fuzzy-neural network methods | ||
| نویسندگان [English] | ||
| Ronak Mohammadi1؛ Abbas Ali Zamani2؛ Hassanali Vahedi3؛ Hamid-Reza Pourian3؛ Bahram Tafaghodinia4 | ||
| 1Dep. of Plant Protection, Razi University | ||
| 2Department of Plant Protection, Faculty of Agriculture, Razi University, Kermanshah | ||
| 3Department of Plant Protection, Razi University | ||
| 4Iranian Research Organization for Science and Technology | ||
| چکیده [English] | ||
| Despite the use of robust statistical methods and fuzzy-neural networks, models that predict the distribution of organisms have seen rapid development in the field of ecology. However, due to the challenges associated with sampling, these studies often lack sufficient samples. In this research, we compared geostatistics and fuzzy-neural networks to estimate the distribution of the tomato fruit worm in a tomato farm in Kermanshah city. For this purpose, the length and width coordinates of the sampling points at the field level were identified and used as inputs for both methods. The output of each method was the count of this pest at those locations. In the geostatistics approach, we employed the normal Kriging method, while in the fuzzy-artificial neural network approach, we used the sigmoid activation function. A comparison of the results from geostatistics and the fuzzy-neural network demonstrated the superior performance of the fuzzy-neural network. The coefficient of determination for the fuzzy-neural network and geostatistics was 0.9 and 0.6, respectively. In conclusion, the fuzzy-neural network method, by integrating latitude and longitude factors, was able to predict the density of the tomato fruit worm with high accuracy. | ||
| کلیدواژه‌ها [English] | ||
| Fuzzy-Neural Network (ANFIS), Kriging, Helicoverpa armigera | ||
| مراجع | ||
|
AMINI, M., AFYUNI, M., FATHIANPOUR, N., KHADEMI, H AND FLUHLER, H. 2005. Continuous soil pollution mapping using fuzzy logic and spatial interpolation. Geoderma, 124(11): 223- 233. https://doi.org/10.1016/j.geoderma.2004.05.009 AFROUS, A., HOSSEINI, S.M. AND GOUDARZI, SH. (2007) Assesment of the Ordinary Kriging and NeuroFuzzy appraoches in interpolation of the groundwater level. Journal of Groundwater,13(2): 978-984.https://doi.org/10.22034/IWRR.2023.181576 AGBOKA, K. M., TONNANG, H. E., ABDEL-RAHMAN, E. M., ODINDI, J., MUTANGA, O., AND NIASSY, S. 2024. Leveraging computational intelligence to identify and map suitable sites for scaling up augmentative biological control of cereals crop pests. Biological Control, 105459. doi.org/10.1016/j.biocontrol.2024.105459 BANDERI, A., GHARINEH, M.H., KARDONI, F., BAHAMIN, S. 2011. The need for accurate agricultural application in sustainable development. 1th National Conference on Strategies for Achieving Sustainable Agriculture, May 2627, Ahvaz, Iran. P. 8. DIONISIO, L.F.S., LIMA, A.C.S., IZIDÓRIO, R.M., SANTOS, A.V., ORRILLO, H.M., LIMA, G.L. 2016. Monitoramento de insetos e distribuição espacial de Sitophilus spp. (Coleoptera: Curculionidae) em unidade de beneficiamento e armazenamento degrãos. Revista Agroambiente On-line, doi.org/10.18227/1982-8470ragro.v10i3.3273 FITE, T., AND TEFERA, T. 2022. The cotton bollworm (Helicoverpa armigera) and Azuki bean beetle (Callosobruchus chinensis): major chickpea (Cicer arietinum L.) production challenges on smallholder farmers in Ethiopia. The Journal of Basic and Applied Zoology, 83(11): 1-12. doi.org/10.1186/s41936-022-00275-w GARZIA, T. G., SISCARO, G., BIONDI, A. AND ZAPPALA, L. 2011. Distribution and damage of Tuta absoluta, an exotic invasive pest from South America. In: International symposium on management of Tuta absoluta (Tomato borer) Proceeding. Agadir, Morocco, November, 16-18. GRESSIE, N. 1993. Statistics for spatial data. 430PP. John Wiley and Sons, New York. doi.org/10.1002/9781119115151 GUO, J., FU, X., ZHAO, S., SHEN, X., WYCKHUYS, K. A. G. AND WU, K. 2020. Long-term shifts in abundance of (migratory) crop-feeding and beneficial insect species in northeastern Asia. Journal of Pest Science, 25(11): 35-37. doi.org/10.1007/s10340-019-01191-9 HABASHI, H., M. HOSSEINI, J. MOHAMMADI and R. RAHMANI, 2007. Geostatistic applied in forest soil studing process. Journal of Agricultural Science and natural Resources, 14(1): 1-10. [In Persian with English summary]. HASSANI-PAK, A. A. 2007. Geostatistics. 314pp. University of Tehran Press. Tehran, Iran. (In Persian). IRMAK, A., J. W. JONES, W. D. BATCHELOR, S. IRMAK, K. J. BOOTE and J. PAZ, 2006. Artificial neural network model as a data analysis tool in precision farming. Transactions of the American Society of Agricultural and Biological Engineers, 49: 2027-2037. doi.org/10.13031/2013.22264 JANG, J.S.R. 1993. ANFIS: adaptive-network-based fuzzy inference system. IEEE T Syst Man Cy, 23(3):665–85. doi.org/10.1109/21.256541 JOKAR, M. 2023. Spatial Distribution of Cotton Bollworm in Southeastern Shores of the Caspian Sea, Golestan Province, Iran. Current Applied Science and Technology, 23(1): 1-8. [In Persian with English summary]. doi.org/10.55003/cast.2022.01.23.014. KATHRINE, A. R. 2001. Geostatistic using SAS software. Own analyticinc. 360 PP. Oxford University Press. Londen. doi.org/10.1007/978-3-642-03647-7_8 LIEBHOLD, A. M., X. ZHANG, M. E. HOHN, J. S., ELKINTON, M. TICEHURST, C. L. BENZON and R. W. CAMPBELL, 1991. Geostatistical analysis of Gypsy moth (Lepidoptera: Lymantridae) egg mass population. Environmental Entomology, 20: 1407-1417. doi.org/10.1093/ee/20.5.1407 MAKARIAN, H., M. H. RASHED, M. BANNAYAN and M. NASSIRI, 2007. Soil seed bank and seedling populations of Hordeum murinum and Cardaria draba in saffron fields. Agriculture Ecosystems and Environment, 120(13): 307- 312. doi.org/10.1016/j.agee.2006.10.020 MERTENS, M. &HUWE B. 2002. Fun-Balance: a fuzzy balance approach for the calculation of nitrate leaching with incorporation of data imprecision. Geoderma,109(12):269 287. doi.org/10.1016/S0016-7061(02)00179-9 MOUSAVI, S.F. AMIRI, M.J. 2012. Modelling nitrate concentration of groundwater using adaptive neural based fuzzy inference system. Journal of Soil and water Research 7(11): 73-83. [In Persian with English summary]. doi.org/10.17221/46/2010-SWR. MOHAMMADI, R., SHABANI NEJAD, A. AND ALICHI, M. 2018. Application of combined geostatistics with optimized artificial neuralnetwork by genetic algorithm to estimate the distribution of Coccinella septempunctata in the alfalfa farm of Bajgah. Journal of Entomological Society of Iran, 38 (1):1-14. [In Persian with English summary] doi.org/10.22117/JESI.2018.116187.1154. MILONAS, P., GOGOU, C., PAPADOPOULOU, A., FOUNTAS, S., LIAKOS, V., AND PAPADOPOULOS, N. T. 2016. Spatio-temporal distribution of Helicoverpa armigera (Hübner)(Lepidoptera: Noctuidae) and Pectinophora gossypiella (Saunders)(Lepidoptera: Gelechiidae) in a cotton production area. Neotropical entomology, 45(10): 240-251. doi.org/10.1007/s13744-015-0358-6. SINGLA, A., AND SINGH, R. 2020. Estimation of avoidable yield losses in chickpea caused by Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae). Phytoparasitica, 12(9):14-25. doi.org/10.1007/s12600-020-00833-4. SOUSA, N., FILHO, M.F., SILVA, P.A., TORRES, J.B. 2020. Determination of an Economic Injury Level for Old World Bollworm (Lepidoptera: Noctuidae) in Processing Tomato in Brazil. Journal of Economic Entomology, 113(4): 1881–1887. doi.org/10.1093/jee/toaa082. SEETHALAM, M., BAPATLA, K. G., KUMAR, M., NISA, S., CHANDRA, P., MATHYAM, P., AND SENGOTTAIYAN, V. 2021. Characterization of Helicoverpa armigera spatial distribution in pigeonpea crop using geostatistical methods. Pest Management Science, 77(11): 4942-4950. doi.org/10.1002/ps.6536. SHABANI NEJAD, A. AND TAFAGHODINIYA, B. 2017. Evaluation of Geostatistical Methods and Artificial Neural Network for Estimating the Spatial Distribution of Tetranychus urticae (Acari: Tetranychidae) in Cucumber field Ramhormoz. Journal of Applied Entomology and Phytopathology 85(11): 22-30. [In Persian with English summary]. doi.org/10.22092/jaep.2017.107121.1110. SHRESTHA, R.R, BARDOSSY, A., RODE, M. 2007. A hybrid deterministic fuzzy rule based model for catchment scale nitrate dynamics. Journal of Hydrology, 342(1): 143 156. doi.org/10.1016/j.jhydrol.2007.05.020. TAY, A., LAFONT, F., BALMAT, J.F. 2021. Forecasting pest risk level in roses greenhouse: Adaptive neuro-fuzzy inference system vs artificial neural networks. Information Processing in Agriculture, 8(3): 386-397. doi.org/10.1016/j.inpa.2020.10.005. VIVAN, L. M., TORRES, J.B., AND FERNANDES. L.S. 2016. Activity of selected formulated biorational and synthetic insecticides against larvae of Helicoverpa armigera (Lepidoptera: Noctuidae). Journal of Economic Entomolgy,101(25): 118–126. doi.org/10.1093/jee/tow244. YUXIN, M., D. J. MULLA and C. R. PIERRE, 2006. Identifying important factors influencing corn yield and grain quality variability using artificial neural networks. Precision Agriculture, 7(2): 117–135. doi.org/10.1007/s11119-006-9004- | ||
|
آمار تعداد مشاهده مقاله: 363 تعداد دریافت فایل اصل مقاله: 317 |
||