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Author(s): Gonçalves, C.
Santana, P.
Brandão, T.
Guedes, M.
Date: 2022
Title: Automatic detection of Acacia longifolia invasive species based on UAV-acquired aerial imagery
Journal title: Information Processing in Agriculture
Volume: 9
Number: 2
Pages: 276 - 287
Reference: Gonçalves, C., Santana, P., Brandão, T., & Guedes, M. (2022). Automatic detection of Acacia longifolia invasive species based on UAV-acquired aerial imagery. Information Processing in Agriculture, 9(2), 276-287.
ISSN: 2214-3173
DOI (Digital Object Identifier): 10.1016/j.inpa.2021.04.007
Keywords: Pattern recognition
Convolutional neural networks
Invasive plants
Acacia longifolia
Abstract: The Acacia longifolia species is known for its rapid growth and dissemination, causing loss of biodiversity in the affected areas. In order to avoid the uncontrolled spread of this species, it is important to effectively monitor its distribution on the agroforestry regions. For this purpose, this paper proposes the use of Convolutional Neural Networks (CNN) for the detection of Acacia longifolia, from images acquired by an unmanned aerial vehicle. Two models based on the same CNN architecture were elaborated. One classifies image patches into one of nine possible classes, which are later converted into a binary model; this model presented an accuracy of and in the validation and training sets, respectively. The second model was trained directly for binary classification and showed an accuracy of and for the validation and test sets, respectively. The results show that the use of multiple classes, useful to provide the aerial vehicle with richer semantic information regarding the environment, does not hamper the accuracy of Acacia longifolia detection in the classifier’s primary task. The presented system also includes a method for increasing classification’s accuracy by consulting an expert to review the model’s predictions on an automatically selected sub-set of the samples.
Peerreviewed: yes
Access type: Open Access
Appears in Collections:ISTAR-RI - Artigos em revistas científicas internacionais com arbitragem científica

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