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Seishi Ninomiya

Showing results (1-10 of 30) with videos related to

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Breeding Science|September 1, 2022
High-throughput field crop phenotyping: current status and challengesSeishi Ninomiya
Plant Methods|February 24, 2015
Automated characterization of flowering dynamics in rice using field-acquired time-series RGB imagesWei Guo, Tokihiro Fukatsu, Seishi Ninomiya
Sensors (Basel, Switzerland)|July 12, 2016
Node Detection and Internode Length Estimation of Tomato Seedlings Based on Image Analysis and Machine LearningKyosuke Yamamoto, Wei Guo, Seishi Ninomiya
Ecology and Evolution|November 19, 2020
Field-based individual plant phenotyping of herbaceous species by unmanned aerial vehicleWei Guo, Yuya Fukano, Koji Noshita, et al.
Annals of Botany|September 18, 2004
Analysis of petal shape variation of Primula sieboldii by elliptic fourier descriptors and principal component analysisYosuke Yoshioka, Hiroyoshi Iwata, Ryo Ohsawa, et al.
Sensors (Basel, Switzerland)|July 28, 2022
Image-Based Phenotyping for Non-Destructive In Situ Rice (<i>Oryza sativa</i> L.) Tiller Counting Using Proximal SensingYuki Yamagishi, Yoichiro Kato, Seishi Ninomiya, et al.
Sensors (Basel, Switzerland)|June 8, 2017
Machine Learning-Based Calibration of Low-Cost Air Temperature Sensors Using Environmental DataKyosuke Yamamoto, Takashi Togami, Norio Yamaguchi, et al.
Sensors (Basel, Switzerland)|July 11, 2014
On plant detection of intact tomato fruits using image analysis and machine learning methodsKyosuke Yamamoto, Wei Guo, Yosuke Yoshioka, et al.
Sensors (Basel, Switzerland)|May 30, 2020
Intact Detection of Highly Occluded Immature Tomatoes on Plants Using Deep Learning TechniquesYue Mu, Tai-Shen Chen, Seishi Ninomiya, et al.
Plos One|September 15, 2023
The effect of calcium silicate and ground magnesium limestone (GML) on the chemical characteristics of acid sulfate soilElisa Azura Azman, Roslan Ismail, Seishi Ninomiya, et al.
Pageof 3

Showing results (1-10 of 30) with videos related to

Sort By:
Pageof 3
Breeding Science|September 1, 2022
High-throughput field crop phenotyping: current status and challengesSeishi Ninomiya
Plant Methods|February 24, 2015
Automated characterization of flowering dynamics in rice using field-acquired time-series RGB imagesWei Guo, Tokihiro Fukatsu, Seishi Ninomiya
Sensors (Basel, Switzerland)|July 12, 2016
Node Detection and Internode Length Estimation of Tomato Seedlings Based on Image Analysis and Machine LearningKyosuke Yamamoto, Wei Guo, Seishi Ninomiya
Ecology and Evolution|November 19, 2020
Field-based individual plant phenotyping of herbaceous species by unmanned aerial vehicleWei Guo, Yuya Fukano, Koji Noshita, et al.
Annals of Botany|September 18, 2004
Analysis of petal shape variation of Primula sieboldii by elliptic fourier descriptors and principal component analysisYosuke Yoshioka, Hiroyoshi Iwata, Ryo Ohsawa, et al.
Sensors (Basel, Switzerland)|July 28, 2022
Image-Based Phenotyping for Non-Destructive In Situ Rice (<i>Oryza sativa</i> L.) Tiller Counting Using Proximal SensingYuki Yamagishi, Yoichiro Kato, Seishi Ninomiya, et al.
Sensors (Basel, Switzerland)|June 8, 2017
Machine Learning-Based Calibration of Low-Cost Air Temperature Sensors Using Environmental DataKyosuke Yamamoto, Takashi Togami, Norio Yamaguchi, et al.
Sensors (Basel, Switzerland)|July 11, 2014
On plant detection of intact tomato fruits using image analysis and machine learning methodsKyosuke Yamamoto, Wei Guo, Yosuke Yoshioka, et al.
Sensors (Basel, Switzerland)|May 30, 2020
Intact Detection of Highly Occluded Immature Tomatoes on Plants Using Deep Learning TechniquesYue Mu, Tai-Shen Chen, Seishi Ninomiya, et al.
Plos One|September 15, 2023
The effect of calcium silicate and ground magnesium limestone (GML) on the chemical characteristics of acid sulfate soilElisa Azura Azman, Roslan Ismail, Seishi Ninomiya, et al.
Pageof 3