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Weed Estimation on Lettuce Crops Using Histograms of Oriented Gradients and Multispectral Images

Weed Estimation on Lettuce Crops Using Histograms of Oriented Gradients and Multispectral Images

Andres Esteban Puerto Lara, Cesar Pedraza, David A. Jamaica-Tenjo
Copyright: © 2020 |Pages: 25
ISBN13: 9781799818397|ISBN10: 179981839X|ISBN13 Softcover: 9781799818403|EISBN13: 9781799818410
DOI: 10.4018/978-1-7998-1839-7.ch009
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MLA

Puerto Lara, Andres Esteban, et al. "Weed Estimation on Lettuce Crops Using Histograms of Oriented Gradients and Multispectral Images." Pattern Recognition Applications in Engineering, edited by Diego Alexander Tibaduiza Burgos, et al., IGI Global, 2020, pp. 204-228. https://doi.org/10.4018/978-1-7998-1839-7.ch009

APA

Puerto Lara, A. E., Pedraza, C., & Jamaica-Tenjo, D. A. (2020). Weed Estimation on Lettuce Crops Using Histograms of Oriented Gradients and Multispectral Images. In D. Burgos, M. Vejar, & F. Pozo (Eds.), Pattern Recognition Applications in Engineering (pp. 204-228). IGI Global. https://doi.org/10.4018/978-1-7998-1839-7.ch009

Chicago

Puerto Lara, Andres Esteban, Cesar Pedraza, and David A. Jamaica-Tenjo. "Weed Estimation on Lettuce Crops Using Histograms of Oriented Gradients and Multispectral Images." In Pattern Recognition Applications in Engineering, edited by Diego Alexander Tibaduiza Burgos, Maribel Anaya Vejar, and Francesc Pozo, 204-228. Hershey, PA: IGI Global, 2020. https://doi.org/10.4018/978-1-7998-1839-7.ch009

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Abstract

Each crop has their own weed problems. Therefore, to understand each problem, agronomists and weed scientists must be able to determine the weed abundance with the most precise method. There are several techniques to scouting, including visual counting for density or estimations for coverage of weeds. However, this technique depends by the evaluator subjectivity, performance, and training, causing errors and bias when estimating weeds abundance. This chapter introduces a methodology to process multispectral images, based on histograms of oriented gradients and support vector machines to detect weeds in lettuce crops. The method was validated by experts on weed science, and the statistical differences were calculated. There were no significant differences between expert analysis and the proposed method. Therefore, this method offers a way to analyze large areas of crops in less time and with greater precision.

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