Zobrazeno 1 - 8
of 8
pro vyhledávání: '"Miyuki Kayamori"'
Publikováno v:
Mycoscience. 64:11-18
Autor:
Tsutomu Komatsu, Shinji Yasuoka, Miyuki Kayamori, Ayumi Notsu, Akinori Shinmura, Toshikazu Yamana, Jun Sasaki, Tohru Kozawa, Motoshige Shimizu, Minako S-Iketani
Publikováno v:
Journal of General Plant Pathology. 86:149-153
QoI fungicides have been an important tool for controlling Cercospora leaf spot (CLS) of sugar beet in Japan. In 2013, CLS control failed at an experimental plot in Okhotsk subprefecture of Hokkaido after the application of trifloxystrobin. Among iso
Publikováno v:
Journal of General Plant Pathology. 83:147-151
Leaf spots on spinach were found in three greenhouses in Hokkaido and Iwate, Japan, in 2011–2012. Three isolates obtained from the lesions were classified into Stemphylium sp. Subgroups C2 and E3, based on morphology and molecular analyses. We comp
Publikováno v:
European Journal of Plant Pathology. 160:245-245
Autor:
Miyuki Kayamori, Yuichi Yamaoka, Takuya Miyamoto, Patricia K. Bryson, Hideo Ishii, Guido Schnabel
Publikováno v:
Pesticide Biochemistry and Physiology. 171:104737
In the European Union (EU), regulation of sterol demethylation inhibiting (DMI) fungicides is tightened due to their suspected endocrine disrupting properties. However, the new DMI fungicide mefentrifluconazole was reported to have high fungicidal ac
Publikováno v:
Computers and Electronics in Agriculture. 116:65-79
An image-based foliar disease monitoring on a single leaf scale is proposed.OCM based robust foliage tracking against various changes in open field.Novel feature of L?, a?, Entropy×Density for classifying CLS from soil background.Promising for autom
Publikováno v:
Computers and Electronics in Agriculture. 108:58-70
First study introduces orientation code matching (OCM) for detecting foliar disease in plants.Robust, continuous, and site-specific observations of CLS changes in sugar beet.Pixel-wise CLS classification under varying light conditions.Classification
Publikováno v:
CANDAR
This paper present a novel method for robust and early Cercospora leaf spot detection in sugar beet using hybrid algorithms of template matching and support vector machine. We adopt three-stage framework to achieve our research target: first, a plant