Scientific journal paper Q1
Automatic detection of Acacia longifolia invasive species based on UAV-acquired aerial imagery
Carolina Gonçalves (Gonçalves, C.); Pedro Santana (Santana, P.); Tomás Brandão (Brandão, T.); Magno Guedes (Guedes, M.);
Journal Title
Information Processing in Agriculture
Year (definitive publication)
2022
Language
English
Country
Netherlands
More Information
Web of Science®

Times Cited: 9

(Last checked: 2024-05-15 23:52)

View record in Web of Science®

Scopus

Times Cited: 12

(Last checked: 2024-05-16 13:52)

View record in Scopus


: 0.6
Google Scholar

Times Cited: 16

(Last checked: 2024-05-13 23:30)

View record in Google Scholar

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.
Acknowledgements
--
Keywords
Pattern recognition,Convolutional neural networks,Invasive plants,Acacia longifolia
  • Computer and Information Sciences - Natural Sciences
  • Electrical Engineering, Electronic Engineering, Information Engineering - Engineering and Technology
  • Agriculture, Forestry and Fisheries - Agriculture Sciences
Funding Records
Funding Reference Funding Entity
PTDC/AAG-REC/4896/2014 Fundação para a Ciência e a Tecnologia
UIDB/04466/2020 Fundação para a Ciência e a Tecnologia

With the objective to increase the research activity directed towards the achievement of the United Nations 2030 Sustainable Development Goals, the possibility of associating scientific publications with the Sustainable Development Goals is now available in Ciência-IUL. These are the Sustainable Development Goals identified by the author(s) for this publication. For more detailed information on the Sustainable Development Goals, click here.