[M-TT46] Introducing metaverse to agriculture. Are we ready?

Fri. Jun 3, 2022 11:00 AM - 1:00 PM Online Poster Zoom Room (35) (Ch.35)

convener:Seishi Ninomiya(Graduate School of Agriculture and Life Sciences, the University of Tokyo), convener:Yukihiro Takahashi(Department of Cosmosciences, Graduate School of Science, Hokkaido University), Chairperson:Seishi Ninomiya(Graduate School of Agriculture and Life Sciences, the University of Tokyo)

Since the 1970s, we have experienced the long PC and Internet era, and since the 2000s, we have rapidly moved into the mobile Internet era. Today, about 60% of the world's population are active Internet users, and the Internet has penetrated almost 100% of our daily lives. There is no doubt that we are looking forward to the next new Internet generation, the era of the metaverse.
In this session, we will discuss the most important fundamental technologies needed to implement the metaverse in agriculture for the next era: data transfer and interaction, human-computer interaction platforms, and AI technologies. Examples of topics to be discussed are high performance satellite imagery, short-range remote sensing, AIoT, 5G, cloud/edge computing to collect and transmit big data via cyber-physical and real world. Artificial intelligence, computer vision, natural language processing, machine learning, VR/AR, PLATEAU, and digital twin technologies help capture and create the cyber-physical and real worlds and help humans become part of the world.

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11:00 AM - 1:00 PM

[MTT46-P01] Data collection and xR platform for digital clone of agricultural fields and plants

*Masayuki Hirafuji¹, Wei Guo¹, Seishi Ninomiya¹ (1.The University of Tokyo)

11:00 AM - 1:00 PM

[MTT46-P02] Search for Machine Learning Suitable for Crop State Discrimination by Angle-Dependent Multispectrum

*Nobuyasu Naruse¹, Takeshi Kanai¹ (1.Faculty of Medicine, Shiga University of Medical Science)

11:00 AM - 1:00 PM

[MTT46-P03] Diagnosis of Garlic health status from reflectance spectral data using machine learning

*Shuichi Ando¹, Yusuke Iida¹, Yukihiro Takahashi², Nobuyasu Naruse³, Yurino Ishida² (1.Niigata University, 2.Hokkaido University, 3.Shiga University of Medical Science)

11:00 AM - 1:00 PM

[MTT46-P04] Creating decision trees for plant species identification in meadows using spectral reflectance

*Yurino Ishida¹, Yukihiro Takahashi¹, Nobuyasu Naruse², Garid Zorigoo¹, Takanori Emaru¹, Kuriki Murahashi¹, Tatsuharu Ono¹, Hokuto Nakata¹ (1.Hokkaido University, 2.Shiga University of Medical Science)

11:00 AM - 1:00 PM

[MTT46-P05] Preliminary Study of Machine Learning-based Tree Species Discrimination using Spectrometer Data Measured at Uryu Research Forest, Hokkaido

*Ahmad Shaqeer Mohamed Thaheer¹, Yukihiro Takahashi¹ (1.Hokkaido University)

11:00 AM - 1:00 PM

[MTT46-P06] Neural Network-Assisted Semantic Segmentation Of Vegetation Via Close-Range Remote Sensing

*CEDRIC JEAN VINCENT CAREMEL¹, WEI GUO², KAORU SENDA¹, DAISUKE TAKADA⁴, KOTA TSUBOUCHI³, YOSHIHIRO KAWAHARA¹ (1.Graduate School of Engineering, department of Electrical Engineering and Information Systems, The University of Tokyo, 2. Graduate School of Agricultural and Life Science, The University of Tokyo, 3.Yahoo! JAPAN Research, 4.Faculty of Food and Agricultural Sciences, Fukushima University)

11:00 AM - 1:00 PM

[MTT46-P07] Channel Attention-GAN based Synthetic Weed Generation for precise Weed identification

*Tang Li¹, Yoichiro Kato¹, Yuya Fukano¹, Wei Guo¹ (1.Graduate School of Agricultural and Life Sciences, The University of Tokyo)

11:00 AM - 1:00 PM

[MTT46-P08] Improved soybean pod counting and localization with feature level considered

*Jiangsan Zhao¹, Akito Kaga², Masayuki Hirafuji¹, Seishi Ninomiya¹, Wei Guo¹ (1.Graduate School of Agriculture and Life Sciences, The University of Tokyo, 2.Crop Gene Function Group, Institute of Crop Science, NARO)

Session information

[M-TT46] Introducing metaverse to agriculture. Are we ready?

[MTT46-P01] Data collection and xR platform for digital clone of agricultural fields and plants

[MTT46-P02] Search for Machine Learning Suitable for Crop State Discrimination by Angle-Dependent Multispectrum

[MTT46-P03] Diagnosis of Garlic health status from reflectance spectral data using machine learning

[MTT46-P04] Creating decision trees for plant species identification in meadows using spectral reflectance

[MTT46-P05] Preliminary Study of Machine Learning-based Tree Species Discrimination using Spectrometer Data Measured at Uryu Research Forest, Hokkaido

[MTT46-P06] Neural Network-Assisted Semantic Segmentation Of Vegetation Via Close-Range Remote Sensing

[MTT46-P07] Channel Attention-GAN based Synthetic Weed Generation for precise Weed identification

[MTT46-P08] Improved soybean pod counting and localization with feature level considered

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