Evaluation of the Ability LVQ4 Artificial Neural Network Model to Predict the Spatial Distribution Pattern of Sitona humeralis in the alfalfa field in Marvdasht

Q: How can I download an article?

To download an article from SID, first log in to the site, search for the article title, and click on the 'Download Article' option.

Q: How can I download an ISI article?

To download an ISI article on SID, enter the keyword or article title in the search bar, view the relevant results, click on the desired article, and select the 'Download Article' option.

Q: How can I access the SID database?

To access the SID database, visit SID.ir, create an account, and log in to access scientific resources.

Q: Is downloading articles from SID free?

Some articles on SID are available for free, while others require payment. Details are specified on the article's page.

SEDIGH H.; MOHAMMADI R.; ALICHI M.; ALEOSFOOR M.

مرکز اطلاعات علمی Scientific Information Database (SID) - Trusted Source for Research and Academic Resources

Journal Paper

Paper Information

Journal: Applied Entomology and Phytopathology Year:2020 | Volume:87 | Issue:2 (109) Page(s): 173-179

Download Full-Text

Persian Verion

View:

364

Download:

Cites:

Information Journal Paper

Title

Evaluation of the Ability LVQ4 Artificial Neural Network Model to Predict the Spatial Distribution Pattern of Sitona humeralis in the alfalfa field in Marvdasht

Author(s)

SEDIGH H. | MOHAMMADI R. | ALICHI M. | ALEOSFOOR M. | Issue Writer Certificate

Keywords

Artificial neural networkQ1

spatial distributionQ1

Sitona humeralisQ1

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 point 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 not 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 (92%) 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 this pest has aggregation distribution so there is possibility potential for using site-specific pest control on this field.

Cites

No record.

References

No record.

Cite

APA: Copy

SEDIGH, H., MOHAMMADI, R., ALICHI, M., & ALEOSFOOR, M.. (2020). Evaluation of the Ability LVQ4 Artificial Neural Network Model to Predict the Spatial Distribution Pattern of Sitona humeralis in the alfalfa field in Marvdasht. APPLIED ENTOMOLOGY AND PHYTOPATHOLOGY, 87(2 (109) ), 173-179. SID. https://sid.ir/paper/380274/en

Vancouver: Copy

SEDIGH H., MOHAMMADI R., ALICHI M., ALEOSFOOR M.. Evaluation of the Ability LVQ4 Artificial Neural Network Model to Predict the Spatial Distribution Pattern of Sitona humeralis in the alfalfa field in Marvdasht. APPLIED ENTOMOLOGY AND PHYTOPATHOLOGY[Internet]. 2020;87(2 (109) ):173-179. Available from: https://sid.ir/paper/380274/en

IEEE: Copy

H. SEDIGH, R. MOHAMMADI, M. ALICHI, and M. ALEOSFOOR, “Evaluation of the Ability LVQ4 Artificial Neural Network Model to Predict the Spatial Distribution Pattern of Sitona humeralis in the alfalfa field in Marvdasht,” APPLIED ENTOMOLOGY AND PHYTOPATHOLOGY, vol. 87, no. 2 (109) , pp. 173–179, 2020, [Online]. Available: https://sid.ir/paper/380274/en

Related Journal Papers