Seishi Ninomiya
Seishi Ninomiya
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On plant detection of intact tomato fruits using image analysis and machine learning methods
K Yamamoto, W Guo, Y Yoshioka, S Ninomiya
Sensors 14 (7), 12191-12206, 2014
Illumination invariant segmentation of vegetation for time series wheat images based on decision tree model
W Guo, UK Rage, S Ninomiya
Computers and electronics in agriculture 96, 58-66, 2013
An informative linkage map of soybean reveals QTLs for flowering time, leaflet morphology and regions of segregation distortion
N Yamanaka, S Ninomiya, M Hoshi, Y Tsubokura, M Yano, Y Nagamura, ...
DNA research 8 (2), 61-72, 2001
Analysis of Petal Shape Variation of Primula sieboldii by Elliptic Fourier Descriptors and Principal Component Analysis
Y Yoshioka, H Iwata, RYO Ohsawa, S Ninomiya
Annals of Botany 94 (5), 657-664, 2004
A weakly supervised deep learning framework for sorghum head detection and counting
S Ghosal, B Zheng, SC Chapman, AB Potgieter, DR Jordan, X Wang, ...
Plant Phenomics, 2019
Quantitative evaluation of soybean (Glycine max L. Merr.) leaflet shape by principal component scores based on elliptic Fourier descriptor
N Furuta, S Ninomiya, N Takahashi, H Ohmori, U Yasuo
Japanese Journal of Breeding 45 (3), 315-320, 1995
AntMap: constructing genetic linkage maps using an ant colony optimization algorithm
H Iwata, S Ninomiya
Breeding science 56 (4), 371-377, 2006
Automated characterization of flowering dynamics in rice using field-acquired time-series RGB images
W Guo, T Fukatsu, S Ninomiya
Plant methods 11, 1-15, 2015
Comparison of ground cover estimates from experiment plots in cotton, sorghum and sugarcane based on images and ortho-mosaics captured by UAV
T Duan, B Zheng, W Guo, S Ninomiya, Y Guo, SC Chapman
Functional Plant Biology 44 (1), 169-183, 2016
Chalkiness in rice: potential for evaluation with image analysis
Y Yoshioka, H Iwata, M Tabata, S Ninomiya, R Ohsawa
Crop Science 47 (5), 2113-2120, 2007
Automatic estimation of heading date of paddy rice using deep learning
SV Desai, VN Balasubramanian, T Fukatsu, S Ninomiya, W Guo
Plant Methods 15, 1-11, 2019
Aerial imagery analysis–quantifying appearance and number of sorghum heads for applications in breeding and agronomy
W Guo, B Zheng, AB Potgieter, J Diot, K Watanabe, K Noshita, DR Jordan, ...
Frontiers in plant science 9, 1544, 2018
Data mining and wireless sensor network for agriculture pest/disease predictions
AK Tripathy, J Adinarayana, D Sudharsan, SN Merchant, UB Desai, ...
2011 World Congress on Information and Communication Technologies, 1229-1234, 2011
Diallel analysis of leaf shape variations of citrus varieties based on elliptic Fourier descriptors
H Iwata, H Nesumi, S Ninomiya, Y Takano, Y Ukai
Breeding Science 52 (2), 89-94, 2002
Intact detection of highly occluded immature tomatoes on plants using deep learning techniques
Y Mu, TS Chen, S Ninomiya, W Guo
Sensors 20 (10), 2984, 2020
Providing agricultural models with mediated access to heterogeneous weather databases
MR Laurenson, T Kiura, S Ninomiya
Applied Engineering in Agriculture 18 (5), 617, 2002
EasyPCC: benchmark datasets and tools for high-throughput measurement of the plant canopy coverage ratio under field conditions
W Guo, B Zheng, T Duan, T Fukatsu, S Chapman, S Ninomiya
Sensors 17 (4), 798, 2017
Combining regression trees and radial basis function networks
M Orr, J Hallam, K Takezawa, A Murray, S Ninomiya, M Oide, T Leonard
International Journal of Neural Systems 10 (06), 453-465, 2000
The evaluation of genotype× environment interactions of citrus leaf morphology using image analysis and elliptic Fourier descriptors
H Iwata, H Nesumi, S Ninomiya, Y Takano, Y Ukai
Breeding Science 52 (4), 243-251, 2002
E-learning in higher education makes its debut in Cambodia: the provincial business education project
BR Abdon, S Ninomiya, RT Raab
International Review of Research in Open and Distributed Learning 8 (1), 1-14, 2007
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