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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

C. Gonçalves et al.,  "Automatic detection of Acacia longifolia invasive species based on UAV-acquired aerial imagery", in Information Processing in Agriculture, vol. 9, no. 2, pp. 276-287, 2022

@article{gonçalves2022_1730780263616,
	author = "Gonçalves, C. and Santana, P. and Brandão, T. and Guedes, M.",
	title = "Automatic detection of Acacia longifolia invasive species based on UAV-acquired aerial imagery",
	journal = "Information Processing in Agriculture",
	year = "2022",
	volume = "9",
	number = "2",
	doi = "10.1016/j.inpa.2021.04.007",
	pages = "276-287",
	url = "https://www.sciencedirect.com/journal/information-processing-in-agriculture"
}

TY  - JOUR
TI  - Automatic detection of Acacia longifolia invasive species based on UAV-acquired aerial imagery
T2  - Information Processing in Agriculture
VL  - 9
IS  - 2
AU  - Gonçalves, C.
AU  - Santana, P.
AU  - Brandão, T.
AU  - Guedes, M.
PY  - 2022
SP  - 276-287
SN  - 2214-3173
DO  - 10.1016/j.inpa.2021.04.007
UR  - https://www.sciencedirect.com/journal/information-processing-in-agriculture
AB  - 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.
ER  -