Plant classification for weed detection using hyperspectral imaging with wavelet analysis
By Hiroshi Okamoto, Tetsuro Murata, Takashi Kataoka, S. Hata. 2007
104 citations recorded by the source corpus.
Subfields: Analytical Chemistry, Plant Science
View Paper: https://www.semanticscholar.org/paper/7c6cbbfac40bb8698a0c2f154858e4f987e4e2c9
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Analytical Chemistry
- Clive H. Bock
- Chan Seok Ryu
- U. Shapira
- Arnon Karnieli
- Mikio Umeda
- Ittai Herrmann
- Masahiko Suguri
- David J. Bonfil
In Plant Science
- Clive H. Bock
- Chan Seok Ryu
- T. Gottwald
- Arnon Karnieli
- Mikio Umeda
- Ittai Herrmann
- P. Parker
- Masahiko Suguri
Citations in the corpus
- Hyperspectral Imaging Applications in Agriculture and Agro-Food Product Quality and Safety Control: A Review (2013, background): “(57) developed a model focusing on weed detection and classification of plants under the same conditions described above.”
- Multivariate analysis of nitrogen content for rice at the heading stage using reflectance of airborne hyperspectral remote sensing (2011, background): “A ground-based hyperspectral image might be suitable to clearly analyze the absolute reflectance of the objective and compare it with a mixed reflectance (Park et al., 2007; Okamoto et al., 2007).”
- Advanced methods of plant disease detection. A review (2014, unknown): “…in situ for precision farming and pest management applications (Yang et al. 2007; Chen et al. 2008; Moshou et al. 2004, 2005; Mirik et al. 2006; Okamoto et al. 2007; Nutter 1989); and (iii) the spatilized scale, which can encompass an entire farm by exploiting airborne data (Seiffert and…”
- Plant Disease Severity Estimated Visually, by Digital Photography and Image Analysis, and by Hyperspectral Imaging (2010, background): “Applications have included monitoring fruit quality, fecal detection on fruits and animal carcasses, creating land use/land cover maps, detecting insect and weed infestations in crops, and disease detection and quantification in crops (Hirano et al., 2003; Uno et al., 2005; Jensen, 2007; Okamoto et ”
- Ground-level hyperspectral imagery for detecting weeds in wheat fields (2013, background): “Since the canopy in the current study is denser than mentioned in the literature for operating similar sensors (Borregaard et al. 2000; Feyaerts and van Gool 2001; Okamoto et al. 2007; Nieuwenhuizen et al. 2010) and since the current study is expected to be a step towards up-scaling (soil will be in”
- Current and future applications of statistical machine learning algorithms for agricultural machine vision systems (2019, background): “…system (Shearer and Holmes, 1990; Zhang and Chaisattapagon, 1995; Meyer et al., 1998; Lee et al., 1999; Perez et al., 2000; Burks et al., 2000; Gebhardt et al., 2006; Okamoto et al., 2007; Piron et al., 2008 and 2009; Chang et al., 2012; Kazmi et al., 2015; Rehman, 2017; Rehman et al., 2018).”