Predicting weed invasion in a sugarcane cultivar using multispectral image |
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| Authors: | Ana J. Righetto Luiz R. Nakamura Pedro L. D. B. Castanho Christel Faes Taciana V. Savian |
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| Affiliation: | 1. Departamento de Ciências Exatas, Escola Superior de Agricultura ‘Luiz de Queiroz’, Universidade de S?o Paulo, Piracicaba, Brazil;2. Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Hasselt, Belgium;3. Departamento de Informática e Estatística, Universidade Federal de Santa Catarina, Florianópolis, Brazil;4. Raízen, Piracicaba, Brazil;5. Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Hasselt, Belgium |
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| Abstract: | The cultivation of sugar cane has been gaining great focus in several countries due to its diversity of use. The modernization of agriculture has allowed high productivity, which is affected by the invasion of weeds. With sustainable agriculture, the use of herbicides has been increasingly avoided in society, requiring more effective weed control methods. In this paper, we propose a statistical model capable of identifying the invasion of weeds in the field, using four color spectra as regressor variables obtained by a multispectral camera mounted on an unmanned aerial vehicle. With the exact identification of the weed infestation, it is possible to carry out the management in the field with herbicide applications in the exact places, thus avoiding the increase of the cost of production or even dispensing with the use of herbicides, effecting the mechanical removal of them. Results show that in the experimental field, it was possible to reduce herbicide spraying by 57%. |
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| Keywords: | GAMLSS multinomial logistic regression modern agriculture statistical modeling weed management |
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