In collaboration with Iranian Phytopathological Society

Document Type : Agricultural Entomology

Authors

1 Graduated student of Shiraz University

2 Phd student of entomology, colleg of agriculture in Razi university

3 Shiraz university

Abstract

In this research, a learning vector quantization neural network (LVQ) model was developed to predict the spatial distribution of Sitona humeralis in Marvdasht. This method was evaluated on data of pest density from alfalfa field. Pest density assessments were performed following a 10 m × 10 m grid pattern on the field and a total of 100 sampling units on field. Some statistical tests, such as means comparison, variance and statistical distribution were performed between the observed points samples data and the estimated pest values to evaluate the performance of prediction of pest distribution. The Results showed that in training and test phase, there were no significant differences, with the confidence level of 95%, between the statistical parameters such as average, variance, statistical distribution and also coefficient of determination in the observed and the estimated pest density. The results suggest that learning vector quantization (LVQ4) neural network can learn pest density model precisely. In addition the results also indicated that trained LVQ4 neural network had a high capability (88%) in predicting pest density for non-sampled points. The technique showed that the LVQNN could predict and map the spatial distribution of Sitona humeralis. The map showed that the pest has aggregation distribution so there is possibility potential for using site-specific pest control on this field.

Keywords

AESCHLIMANN, J. P. 1980. The Sitona species occurring on medicago and their natural enemies in. Mediterranean region. Entomophaga, 25(4): 139-153.
GARZIA, T. G., SISCARO, G., BIONDI, A. and ZAPPALA, L. 2011. Distribution and damage of Tuta absoluta, an exotic invasive pest from South America. Proceeding of 1th In: International symposium on  management of Tuta absoluta (Tomato borer). 13 -15 November, Morocco.PP16.
GOEL, P. K., PRASHER, S. O., PATEL, R. M., LANDRY, J. A., BBONNELL, R. B. and viau, A. A. 2003. Classification of hyperspectral 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.
HEYKIN, S. 1999. Neural Networks A Comprehensive Foundation. 2thed. 125pp. Oxford University press.
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.
SHABANI NEJAD, A. R. and TAFAGHODINIA, B. 2016. Evaluation of the Ability of LVQ4 Artificial Neural Network model to Predict the 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. and TAFAGHODINIA, B. 2017. Automatic clustering of data from sampling and evaluationg of neuro-fuzzy network for estimating the distribution of Bemisia tabaci (Hem.:Aleyrodidae). Entomological Society of Iran 37, 91-105. [In Persian with English summary]
SHABANI NEJAD, A. R. and 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.2018 Journal of Applied Entomology and Phytopathology 85, 22-30. [In Persian with English summary].
SHABANI NEJAD, A. R. and TAFAGHODINIA, B., ZANDI- SIHANI N. 2016. Hybrid neural network with genetic algorithms for predicting distribution pattern of Tetranychus urticae. in cucumbers field of Rāmhormoz. Persian Journal of Acarology 8, 240-252.
VAKIL-BAGHMISHEH, M. T. and PAVESIC, N. 2003. Premature clustering phenomenon and new training algorithms for LVQ. Pattern recognition, 36: 1901-1921.
VAKIL-BAGHMISHEH, M. T. and PAVESIC, N. 2003. A fast simplified fuzzy ARTMAP network. Neural Processing Letters, 17: 273-301.
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.
YOUNG, P., JA-MYUNG, K., BUOM-YOUNG, L., YEONG-JIN. and YOOSHIN, K. 2010. 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.
ZHANG, W. J., ZHONG, X. Q. and LIU, G. H. 2008. Recognizing spatial distribution patterns of grassland insects: neural network approaches. Stochastic Environmental. Research and Risk Assessment, 22:207–216.
ZHANG, Y. F. and FUH, J. Y. H. 1998. A neural network approach for early cost estimation of packaging products. Computers & Industrial Engineering, 34: 433-50.