Identification and determination of the number of immature green citrus fruit in a canopy under different ambient light conditions
By Subhajit Sengupta, Won Suk Lee. 2014
164 citations recorded by the source corpus.
Subfields: Environmental Engineering, Mechanical Engineering, Plant Science
View Paper: https://www.semanticscholar.org/paper/19faff8c43739658c1e5ee3ceff435afa58c0cbb
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Environmental Engineering
- Suchet Bargoti
- James Patrick Underwood
- Francesc X. Prenafeta-Boldu
- Andreas Kamilaris
- Horea-Bogdan Muresan
- Mihai Oltean
- Maryam Rahnemoonfar
- Clay Sheppard
In Mechanical Engineering
In Plant Science
- Suchet Bargoti
- James Patrick Underwood
- Dionysis D. Bochtis
- Simon Pearson
- Konstantinos G. Liakos
- Dimitrios Moshou
- Patrizia Busato
- Francesc X. Prenafeta-Boldu
Citations in the corpus
- Machine Learning in Agriculture: A Review (2018, background): “In another study [76], authors developed an early yield mapping system for the identification of immature green citrus in a citrus grove under outdoor conditions.”
- Image Segmentation for Fruit Detection and Yield Estimation in Apple Orchards (2016, background): “…& Blanke, 2009; Wang et al., 2013), citrus (Annamalai, Lee, & Burks, 2004; Li, Lee, & Hsu, 2011; Qiang, Jianrong, Bin, Lie, & Yajing, 2014; Regunathan & Lee, 2005; Sengupta & Lee, 2014), kiwifruit (Wijethunga, Samarasinghe, Kulasiri, & Woodhead, 2009), and peaches (Kurtulmus, Lee, & Vardar, 2014).”
- Deep fruit detection in orchards (2016, methodology): “To detect citrus fruit, Circular Hough Transforms (CHT) have been used to extract key-points, which were then classified using a Support Vector Machine (SVM) [10].”