Automated detection of citrus trees from a digital surface model

Ok, Ali Özgün; Ok, Aslı Özdarıcı

doi:10.1109/siu.2017.7960165

Cited by 2 publications

(1 citation statement)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…O Brasil responde por mais de três quartos das exportações globais de suco de laranja, sendo o maior produtor do mundo; em 2019, a produção de citrus pelo Brasil está prevista em 17,8 milhões de toneladas [1]. Diversos trabalhos anteriores abordaram a deteção de árvores de citrus em imagens provenientes de sensoriamento remoto, utilizando métodos como transformada de Hough circular [2], transformada simétrica radial [3]- [5], casamento de padrões [6], e mais recentemente, redes neurais convolucionais [7].…”

Section: Trabalhos Relacionadosunclassified

Citrus Tree Classification from UAV Images: Analysis and Experimental Results

Kobayashi¹,

Mattos²,

Macedo³

et al. 2019

Anais Do XV Workshop De Visão Computacional (WVC 2019)

View full text Add to dashboard Cite

The use of unmanned aerial vehicles (UAVs) and computer vision for automating farm operations is growing rapidly: time-consuming tasks such as crop monitoring may be solved in a more efﬁcient, precise, and less error-prone manner. In particular, for estimating productivity and managing pests, it is fundamental to characterize crop regions into four classes: (i) full-grown trees, (ii) tree seedlings, (iii) tree gaps, and (iv) background. In this paper, we address the classiﬁcation of images from citrus plantations, acquired by UAVs, into the previously mentioned categories. While Deep learning-based methods allow to achieve high accuracy values for classiﬁcation, explainability remains an issue. Therefore, our approach is to run an experimental analysis that allows to derive the effects of different parametrizations (involving descriptors, classiﬁers, and sampling methods) when applied to our citrus dataset.

show abstract