MAKING WEED MANAGEMENT MAPS BY ARTIFICIAL NEURAL NETWORKS FOR USING IN PRECISION AGRICULTURE

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.

ROHANI A.; MAKARIAN H.

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

Journal Paper

Paper Information

Journal: JOURNAL OF AGRICULTURAL MACHINERY Year:2012 | Volume:1 | Issue:2 Page(s): 74-83

Download Full-Text

Persian Verion

View:

930

Download:

Cites:

Information Journal Paper

Title

MAKING WEED MANAGEMENT MAPS BY ARTIFICIAL NEURAL NETWORKS FOR USING IN PRECISION AGRICULTURE

Author(s)

ROHANI A. | MAKARIAN H. | Issue Writer Certificate

Keywords

CLASSIFICATIONQ3

NEURAL NETWORKQ2

SITE-SPECIFIC MANAGEMENTQ3

SPATIAL DISTRIBUTIONQ2

Abstract

With the rise of new powerful statistical techniques and NEURAL NETWORKs models, the development of predictive species distribution models has rapidly increased in ecology. In this research, a learning vector quantization (LVQ) and multi-layer perceptron (MLP) NEURAL NETWORK models have been employed to predict, classify and map the SPATIAL DISTRIBUTION of A. repens L. density. This method was evaluated based on data of weed density counted at 550 points of a fallow field located in Faculty of Agriculture, Shahrood University of Technology, Semnan, Iran, in 2010. Some statistical tests, such as comparisons of the means, variance, statistical distribution as well as coefficient of determination in linear regression were used between the observed point sample data and the estimated weed seedling density surfaces by two NEURAL NETWORKs to evaluate the performance of the pattern recognition method. Results showed that in the training and test phases non-significant different was observed between average, variance, statistical distribution in the observed and the estimated weed density by using LVQ NEURAL NETWORK. While this comparisons was significant except statistical distribution by using MLP NEURAL NETWORK. In addition, results indicated that trained LVQ NEURAL NETWORK has a high capability in predicting weed density with recognition error less than 0.64 percent at unsampled points.While, MLP NEURAL NETWORK recognition error was less than 14.6 percent at unsampled points. The maps showed that, patchy weed distribution offers large potential for using site-specific weed control on this field.

Cites

EVALUATION OF THE ABILITY OF LVQ4A2 ARTIFICIAL NEURAL NETWORK MODEL TO PREDICT THE SPATIAL DISTRIBUTION PATTERN OF CADMIUM IN SOIL

References

No record.

Cite

APA: Copy

ROHANI, A., & MAKARIAN, H.. (2012). MAKING WEED MANAGEMENT MAPS BY ARTIFICIAL NEURAL NETWORKS FOR USING IN PRECISION AGRICULTURE. JOURNAL OF AGRICULTURAL MACHINERY, 1(2), 74-83. SID. https://sid.ir/paper/201296/en

Vancouver: Copy

ROHANI A., MAKARIAN H.. MAKING WEED MANAGEMENT MAPS BY ARTIFICIAL NEURAL NETWORKS FOR USING IN PRECISION AGRICULTURE. JOURNAL OF AGRICULTURAL MACHINERY[Internet]. 2012;1(2):74-83. Available from: https://sid.ir/paper/201296/en

IEEE: Copy

A. ROHANI, and H. MAKARIAN, “MAKING WEED MANAGEMENT MAPS BY ARTIFICIAL NEURAL NETWORKS FOR USING IN PRECISION AGRICULTURE,” JOURNAL OF AGRICULTURAL MACHINERY, vol. 1, no. 2, pp. 74–83, 2012, [Online]. Available: https://sid.ir/paper/201296/en

Related Journal Papers