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Weed Estimation on Lettuce Crops Using Histograms of Oriented Gradients and Multispectral Images
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Author(s): Andres Esteban Puerto Lara (Fundacion Universitaria Panamericana, Colombia), Cesar Pedraza (Universidad Nacional de Colombia, Colombia)and David A. Jamaica-Tenjo (Universidad Nacional de Colombia, Colombia)
Copyright: 2020
Pages: 25
Source title:
Pattern Recognition Applications in Engineering
Source Author(s)/Editor(s): Diego Alexander Tibaduiza Burgos (Universidad Nacional de Colombia, Colombia), Maribel Anaya Vejar (Universidad Sergio Arboleda, Colombia)and Francesc Pozo (Universitat Politècnica de Catalunya, Spain)
DOI: 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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