| تعداد نشریات | 282 |
| تعداد نشریات سازمان | 80 |
| تعداد شمارهها | 3,186 |
| تعداد مقالات | 35,332 |
| تعداد مشاهده مقاله | 51,393,707 |
| تعداد دریافت فایل اصل مقاله | 91,299,063 |
مقایسه عملکرد شبکه عصبی مصنوعی بهینه شده با الگوریتم رقابت استعماری و جهش قورباغه مخلوط شده برای پیشبینی الگوی توزیع کفشدوزک هفتنقطهای Coccinella septempunctata در مزرعه یونجه شهرستان زرقان | ||
| آفات و بیماری های گیاهی | ||
| مقاله 4، دوره 90، شماره 1 - شماره پیاپی 114، 1401، صفحه 41-51 اصل مقاله (593.16 K) | ||
| نوع مقاله: حشره شناسی کشاورزی | ||
| شناسه دیجیتال (DOI): 10.22092/jaep.2022.356397.1418 | ||
| نویسندگان | ||
| روناک محمدی* 1؛ مریم ال عصفور2 | ||
| 1دانشجوی دکترا رشته حشره شناسی کشاورزی دانشگاه رازی کرمانشاه | ||
| 2عضو هیت علمی دانشگاه شیراز | ||
| چکیده | ||
| امروزه تشریح الگوهای پراکندگی حشرات با استفاده از روشهای درونیابی و تخمین تراکم مورد توجه بسیاری از محققین قرار گرفته است. این پژوهش به منظور پیش بینی و ترسیم نقشه توزیع مکانی کفشدوزک هفتنقطهای با استفاده از شبکههای عصبی پرسپترون چندلایه (MLP) ترکیب شده با الگوریتم رقابت استعماری، جهش قورباغه در سطح مزرعه انجام شد. داده از صد نقطه سطح یک مزرعه یونجه شهرستان زرقان در سال 1398 به دست آمده است. برای ارزیابی شبکههای عصبی مورد استفاده و مقایسه عملکرد آنها در پیش بینی توزیع این گونه از پارامترهای آماری مانند توزیع آماری، مقایسه میانگین و ضریب تبیین بین مقادیر پیش بینی شده مکانی توسط شبکه عصبی و مقادیر واقعی آنها استفاده شد. نتایج نشان داد که در فازهای آموزش و آزمایش، بین مقادیر توزیع آماری و میانگین مجموعه دادههای واقعی و پیش بینی شده مکانی این گونه توسط شبکه عصبی ترکیب شده با الگوریتم جهش قورباقه مخلوط شده ، تفاوت معنیداری وجود نداشت و الگوریتم جهش قورباقه مخلوط شده دقت بالاتری در تشخیص توزیع داشت. بر اساس نقشههای ترسیمی ما ا توزیع این گونه کفشدوزک تجمعی است. | ||
| کلیدواژهها | ||
| الگوریتم متاهیورستیک؛ توزیع مکانی؛ شبکه عصبی مصنوعی؛ کفشدوزک هفت نقطهای | ||
| عنوان مقاله [English] | ||
| Comparison of optimized artificial neural network performance with imperialist competitive algorithm and shuffled frog leaping algorithm to predict the distribution pattern of Coccinella septempunctata (Col: Coccinellidae ) in alfalfa fields of zarghan | ||
| نویسندگان [English] | ||
| Ronak Mohamaddi1؛ Maryam Al osfoor2 | ||
| 1Phd student of entomology, colleg of agriculture in Razi university | ||
| 2Shiraz university | ||
| چکیده [English] | ||
| Nowadays, many researchers have paid attention to explaining the patterns of insect dispersion using interpolation and density estimation methods in order to investigate the possibility of proper integrated management with the location of pests. This study was conducted to predict and map the spatial distribution of the Coccinella septempunctata using multilayer perceptron neural networks (MLPs) combined with the imperialist competitive algorithm and shuffled frog leaping algorithm at the field level. Data have been obtained through100 samples taking from the surface of a hay field of zarghan area in 2019. To evaluate the neural networks used and compare their performance, statistical parameters such as statistical distribution, mean comparison and coefficient of explanation between the spatially predicted values by the neural network and their actual values were used to predict the distribution of this species. The results showed that in the training and experimental phases, there was no significant difference between the values of statistical distribution and the mean of real and predicted spatial data sets of this species combined by neural network with shuffled frog leaping algorithm. Shuffled frog leaping algorithm was more accurate in detecting the distribution. Our map showed that pest distribution was patchy | ||
| کلیدواژهها [English] | ||
| Artificial neural network, Coccinella septempunctata, metaheuristic algorithm, spatial distribution | ||
| مراجع | ||
|
ALI, A. & RIZVI, P.O., 2010. Age and stage specific life table of Coocinella septemounctata L. (Coleoptera: Coccinellidae) at varying temperature. World Journal of Agricultural Sciences 6, 268-273. ALICHI, M. and MOHAMMADI, R. 2017. Biology and food spectrum of Seven- spotted ladybird, Coccinella septempunctata L. (Col.: Coccinellidae), in alfalfa fields of Badjgah area (Shiraz). Plant Pest Research 7, 43-53. ANSARI POUR, A., AGHASI, K. and BANNISTER, M., 2012. Density and sex ratio of seven spotted ladybird (Coccinella septempunctata) in three altitudes of Khorramabad. Life Science Journal 9, 830-834. ATASHPAZ-GARGARI, E., HASHEMZADEH, F., RAJABIOUN, R. & LUCAS, C., 2008. Colonial competitive algorithm: A novel approach for PID controller design in MIMO distillation column process. International Journal of Intelligent Computing and Cybernetics 1, 337–355. ATASHPAZ-GARGARI, E 2009. Imperialist Competitive Algorithm development and it is applications, M.S. Thesis, University of Tehran (in Persian). CHOUDHURY, S. K. and BARTARYA, G 2003. Role of temperature and surface finish in predicting tool wear using neural network and design of experiments. International Journal of Machine Tools & Manufacture 10, 747–753. CASTERA, I. and BOYD, M., 1996. Designing a Artificial Neural Network for forecasting financial and economic time series. Neuro computing 12, 13–19. ENAYATIFAR, R., SADAEI, H. J., ABDULLAH, A. H. and GANGI, A. 2013. Imperialist competitive algorithm combined with refined high-order weighted fuzzy time series (RHWFTS–ICA) for short term load forecasting. Energy Conversion and Management 76,1104-1116. EUSUFF, M. M. AND LANSEY, K. E. 2003. Optimization of Water Distribution Network Design Using the Shuffled Frog Leaping Algorithm. Journal of Water Resources Planning and Management 129, 210-225. FREEMAN, J. and SAKURA, D. 2005. Neural Networks: Algorithms, Applications, and Programming Techniques. Addison-Wesley, Berlin. GOEL, P. K., PRASHER, S. O., PATEL, R. M., LANDRY, J. A., BONNELL, R. B. and VIAU, A. A. 2003. Classification of hyper spectral data by decision trees and artificial neural networks to identify weed stress and nitrogen status of corn. Computers and Electronics in Agriculture 39,67–93. HAGEN, J.S. 1962. Biology and ecology of predceuos Coccinllidae. Annual Review of Entomology 7, 289-326. HODEK, I. 1973. Biology of Coccinellidae. Aeademia publishing house of the Czechoslosvak Academy of Sciences Pragus, 260pp. HONEK, A. and MARTINKOVA, Z. 2005. Long term changes in abundance of Coccinella septempunctata L. (Coleoptera: Coccinellidae) in the Czech Republic. European Journal of Entomology 102, 443-448. HEIDARI, M., VALIDI, J., EBRAHIMI, S. 2021. Portfolio Optimization Based on Robust Probablistic Planning Model Using Genetic Algorithm and Shuffled Frog-leaping Algorithm. Financial Engineering and Portfolio Management 12, 564-586. IRMAK, A., JONES, J. W., BATCHELOR, W. D., IRMAK, S., BOOTE, K. J. and PAZ, J. 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. KENNDY, J. and EBERHART, R.C. 1995. Particle Swarm Optimization. In Proceedings of the IEEE International Conference on Neural Networks IV. KIANPOUR, R., FATHIPOUR, Y., KAMALI, K. & NASERI, B. 2010. Bionomics of Aphis gossypii (Homoptera: Aphididae) and its predators Coccinella septempunctata and Hippodamia variegata (Coleoptera: Coccinellidae) in natural conditions. Journal of Agricultural Science and Technology 12, 1-11. Kim, K. 2006. Artificial Neural Network with evolutionary instance selection for financial forcasting. Expert systems with application 30,519–526. KHAN, M.H. and YOLDAS, Z. 2018. Investigations on the cannibalistic behavior of ladybird beetle Coccinella septempunctata L. (Coleoptera: Coccinellidae) under laboratory conditions. Turkish Journal of Zoology 42,432-438. MAKARIAN, H., RASHED MOHASSEL, M. H., BANNAYAN, M. & NASSIRI, M. 2007. Soil seed bank and seedling populations of Hordeum murinum and Cardaria draba in saffron fields. Agriculture Ecosystems and Environment 120, 307- 312. MOHAMADDDI, R., SHABANI NEJAD, A.R. & ALICHI, M. 2018. An Application of Combined Geostatistics with Optimized Artificial Neural Network by Genetic Algorithm to estimate the distribution of Coccinella septempunctata (Col:.Coccinellidae) in the alfalfa farm of Bajgah. Journal of Entomological Society of Iran 38,1-14. [In Persian with English summary. MOHAMADDDI, R., SHABANI NEJAD, A.R., ALICHI, M. & SHABANI NEJAD, M.R. 2018. Evaluation of GMDH artificial neural network model to predict the spatial distribution of Coccinella septempunctata (Col.: Coccinellidae) in the alfalfa farm of Bajgah, Shiraz. Journal of Entomological Society of Iran 38,275-287. [In Persian with English summary. MORADI, H. and ZANDIEH, M. 2013. An imperialist competitive algorithm for a mixed-model assembly line sequencing problem. Journal of Manufacturing Systems 32,46 – 56. OBRYCKI, J.J. and KRING, T.J. 1998. Pradaceus Coccinellidae in biological control. Annual Review of Entomology 43, 295-321. SHABANI NEJAD, A. R. and TAFAGHODINIA, B. 2016. Evaluation of the Ability of LVQ4 Artificial Neural Network Model to Predictthe Spatial Distribution Pattern of Tuta absoluta in the tomato field in Ramhormoz. Journal of Entomological Society of Iran 36,195-204. [In Persian with English summary. SHABANI NEJAD, A. R. & TAFAGHODINIA, B. 2016. 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, 22-30. [In Persian with English summary]. SHABANI NEJAD, A. R. TAFAGHODINIA, B. & ZANDI- SOHANI, N. 2016. Hybrid neural network with genetic algorithms for predicting distribution pattern of Tetranychus urticae T. in cucumbers field of Rāmhormoz. Persian Journal of Acarology 8, 240-252. SHABANI NEJAD, A. R. and TAFAGHODINIA, B. 2017. Automatic clustering of data from sampling and evaluationg of neuro-fuzzy network for estimateinge the distribution of Bemisia. tabaci (Hem.:Aleyrodidae).Journal if Iranian entomology socity 37, 91-105. [In Persian with English summary]. VELLIDO, A., LIBOA, P. J. G. and VAUGHAN, J. 2010. Neural Networks in Business: a Survey of Applications. Expert Systems with Application 19, 12-24. VAKIL-BAGHMISHEH, M.T. and. PAVEŠIC, N 2003. Premature clustering phenomenon and new training algorithms for LVQ. Pattern recognition 36(5), 1901-1921. YOUNG, S.P. JA-MYUNG, K., BUOM-YOUNG, L., YEONG-JIN, L. and YOOSHIN, K. 2000. Use of an artificial neural network to predict population dynamics of the forest–pest pine needle gall midge (Diptera: Cecidomyiida). Environmental Entomology 29, 1208–1215. YUXIN, M., MULLA, D. J. and PIERRE, C. R. 2006. Identifying important factors influencing corn yield and grain quality variability using artificial neural networks. Precision Agriculture 7,117–135. ZHANG, Y. F. and FUH, J. Y. H. 1998. A neural network approach for early cost estimation of packaging products. Computer Ind Engineer 34, 433-50. ZHANG, W. J., ZHONG, X. O. and LIU, G. H. 2008. Recognizing spatial distribution patterns of grassland insects: neural network approaches. Stochastic Environmental. Research and Risk Assessment 22, 207–216. | ||
|
آمار تعداد مشاهده مقاله: 592 تعداد دریافت فایل اصل مقاله: 564 |
||