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Segmentation of Crops and Weeds Using Supervised Learning Technique

Segmentation of Crops and Weeds Using Supervised Learning Technique

Noureen Zafar, Saif Ur Rehman, Saira Gillani, Sohail Asghar
Copyright: © 2015 |Pages: 26
ISBN13: 9781466685130|ISBN10: 1466685131|EISBN13: 9781466685147
DOI: 10.4018/978-1-4666-8513-0.ch015
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MLA

Zafar, Noureen, et al. "Segmentation of Crops and Weeds Using Supervised Learning Technique." Improving Knowledge Discovery through the Integration of Data Mining Techniques, edited by Muhammad Usman, IGI Global, 2015, pp. 308-333. https://doi.org/10.4018/978-1-4666-8513-0.ch015

APA

Zafar, N., Rehman, S. U., Gillani, S., & Asghar, S. (2015). Segmentation of Crops and Weeds Using Supervised Learning Technique. In M. Usman (Ed.), Improving Knowledge Discovery through the Integration of Data Mining Techniques (pp. 308-333). IGI Global. https://doi.org/10.4018/978-1-4666-8513-0.ch015

Chicago

Zafar, Noureen, et al. "Segmentation of Crops and Weeds Using Supervised Learning Technique." In Improving Knowledge Discovery through the Integration of Data Mining Techniques, edited by Muhammad Usman, 308-333. Hershey, PA: IGI Global, 2015. https://doi.org/10.4018/978-1-4666-8513-0.ch015

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Abstract

In this article, segmentation of weeds and crops has been investigated by using supervised learning based on feed forward neural network. The images have been taken from the satellite imaginary for a specified region on the geographical space in Pakistan and perform edge detection by classical image processing scheme. The obtained samples are classified by data mining, based on artificial neural network model based on linear activation function at the input and output layer while threshold ramp function at hidden layer. A scenario based results are obtained at a huge samples of the weeds of the corn field and crop in the form of the mean square error based fitness evaluation function. The given scheme has the perks on the existed schemes as applicability of the designed framework, ease in implementation and less hardware needed for implementation.

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