20210120_



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[512]
:
Detection of canopy wilting in soybean via deep learning: a screening tool for precision phenotyping
1, 1, 1, 1, 2, 3, 1, 4, 5, 6, 2, 7, 3, 1, 1, 11. 2. 3. 4. T-PIRC5. 6. 7. Imachi, Y.1, Y. Toda1, G. Sasaki1, Y. Omori1, Y. Yamasaki2, H. Takahashi3, H. Takanashi1, M. Tsuda4, Y. Sawada5, H. Kajiya-Kanegae6, H. Tsujimoto2, A. Kaga7, M. Nakazono3, T. Fujiwara1, H. Iwata1, W. Guo1 (1. Grad. Sch. Agr. Life Sci., Univ. Tokyo, 2. Arid Land Res. Ctr., Tottori Univ., 3. Grad. Sch. Bioagri. Sci., Nagoya Univ., 4. T-PIRC, Univ. Tsukuba, 5. Ctr. for Sustainable Resource Sci., RIKEN, 6. Res. Ctr. for Agr. Info. Tech., NARO, 7. Inst. Crop Sci., NARO)

2 3 20 (13:20 16:00)
[514]
UAV
Deep learning for prediction of soybean biomass based on UAV remote sensing images
1, Clement Barras1, 1, 1, 2, 3, 1, 4, 5, 2, 6, 3, 1, 11. 2. 3. 4. T-PIRC5. 6. Okada, M.1, C. Barras1, Y. Toda1, Y. Oomori1, Y. Yamasaki2, H. Takahashi3, H. Takanashi1, M. Tsuda4, M. Hirai5, H. Tsujimoto2, A. Kaga6, M. Nakazono3, T. Fujiwara1, H. Iwata1 (1. Grad. Sch. Agr. Life Sci., Univ. Tokyo, 2. Arid Land Res. Ctr., Tottori Univ., 3. Grad. Sch. Bioagri. Sci., Nagoya Univ., 4. T-PIRC, Univ. Tsukuba, 5. Ctr. for Sustainable Resource Sci., RIKEN, 6. Inst. Crop Sci., NARO)

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13:20-16:00

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13:20-16:15

[509-518]
13:20-16:00

14:25-16:15



(208-217)
(1aE01-08)
2020/3/26 9:0011:48 F Room F1F
(1aF01-09) 2020/3/26 9:0011:48 F Room F1F











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[509] " " Growth characteristics and varietal differences in barley under indoor cultivation system with Speed Breeding condition
, , , Shimizu, H., G. Ishikawa, E. Aoki, T. Tonooka (Inst. Crop. Sci., NARO)
2 3 20 (13:20 16:00)
[510] Genetic factors controlling annual deviation of heading in barley genetic resources
1, 1, 1,21. 2. Sato, K.1, M. Ishii1, K. Mochida1,2 (1. IPSR, Okayama Univ., 2. CSRS, RIKEN)
(509-518)

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2 3 20 (13:20 16:00)
[511] Head and anthesis detection for wheat grown at high densities use photography and deep learning
1, 2, 1,Haozhou Wang1, 4, 3,4, 11. 2. 3. 4. Guo, W.1, S. Nasuda2, K. Kuroki1, H. Wang1, T. Tameshige4, K. Shimizu3,4, Y. Iwata1 (1. Grad. Sch. Agr. Life Sci., Univ. Tokyo, 2. Grad. Sch. Agric., Kyoto Univ., 3. Univ. Zurich, Dept. Evol. Biol. Env. Sci., 4. Kihara Inst. Biol. Res, Yokohama City Univ.)

2 3 20 (13:20 16:00)
[512] : Detection of canopy wilting in soybean via deep learning: a screening tool for precision phenotyping
1, 1, 1, 1, 2, 3, 1, 4, 5, 6, 2, 7, 3, 1, 1, 11. 2. 3. 4. T-PIRC5. 6. 7. Imachi, Y.1, Y. Toda1, G. Sasaki1, Y. Omori1, Y. Yamasaki2, H. Takahashi3, H. Takanashi1, M. Tsuda4, Y. Sawada5, H. Kajiya-Kanegae6, H. Tsujimoto2, A. Kaga7, M. Nakazono3, T. Fujiwara1, H. Iwata1, W. Guo1 (1. Grad. Sch. Agr. Life Sci., Univ. Tokyo, 2. Arid Land Res. Ctr., Tottori Univ., 3. Grad. Sch. Bioagri. Sci., Nagoya Univ., 4. T-PIRC, Univ. Tsukuba, 5. Ctr. for Sustainable Resource Sci., RIKEN, 6. Res. Ctr. for Agr. Info. Tech., NARO, 7. Inst. Crop Sci., NARO)



0
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5

(multispectral: MS) MS 200 MS RS)UAV RS
2019 4 W1−W4 ) 200 4 W1 W4 RS 1.5 m RS 20 m MS 1 2 1 3 475 nm550 nm660 nm725 nm850 nm 5 660 nm850 nm NDVI MS NDVI
RS MS 550 nm NDVI 0.49−0.87 0.00−0.48 5 RS 1 PC1 850 nm 4 2 PC2 850 nm RS PC1 42 %75 %PC2 28 %21 % PC1 PC2 MS NDVI RS W4 RS W1 RS W1 MS W4 RS MS MS MS GxEGxE MS
139 319321
3

2020/10/30 10:19







(ResistanceTolerance)
3 3 21 930 11:30


3 3 21 930 11:30
[P055-C]

Prediction of soybean growth under drought based on multispectral imaging using drone and ground-level vehicle remote sensing 1, 1, 1, 2, 3, 1, 4, 1, 2, 5, 3, 1, 11. , 2. , 3. , 4. T-PIRC, 5. " " Sakurai, K.1, Y. Toda1, Y. Omori1, Y. Yamasaki2, H. Takahashi3, H. Takanashi1, M. Tsuda4, M. Ishimori1, H. Tsujimoto2, A. Kaga5, M. Nakazono3, T. Fujiwara1, H. Iwata1 (1. Grad. Sch. Agr. Life Sci., Univ. Tokyo, 2. Arid Land Res. Ctr., Tottori Univ., 3. Grad. Sch. Bioagri. Sci., Nagoya Univ., 4. TPIRC, Univ. Tsukuba, 5. Inst. Crop Sci., NARO)







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[513] UAV Development of a prediction model based on marker genotype and a growth model: an application to soybean canopy area measured by UAV 1, 1, 1, 2, 3, 1, 4, 5, 6, Lopez-Lozano Raul7, 2, 8, 3, 1,Frederic Baret7, 11. 2. 3. 4. T-PIRC5. 6. 7.INRA EMMAH8.
ID @.com







2 3 20 (13:20 16:00)
[512] : Detection of canopy wilting in soybean via deep learning: a screening tool for precision phenotyping
1, 1, 1, 1, 2, 3, 1, 4, 5, 6, 2, 7, 3, 1, 1, 11. 2. 3. 4. T-PIRC5. 6. 7. Imachi, Y.1, Y. Toda1, G. Sasaki1, Y. Omori1, Y. Yamasaki2, H. Takahashi3, H. Takanashi1, M. Tsuda4, Y. Sawada5, H. Kajiya-Kanegae6, H. Tsujimoto2, A. Kaga7, M. Nakazono3, T. Fujiwara1, H. Iwata1, W. Guo1 (1. Grad. Sch. Agr. Life Sci., Univ. Tokyo, 2. Arid Land Res. Ctr., Tottori Univ., 3. Grad. Sch. Bioagri. Sci., Nagoya Univ., 4. T-PIRC, Univ. Tsukuba, 5. Ctr. for Sustainable Resource Sci., RIKEN, 6. Res. Ctr. for Agr. Info. Tech., NARO, 7. Inst. Crop Sci., NARO)



2 3 20 (13:20 16:00)
[512] : Detection of canopy wilting in soybean via deep learning: a screening tool for precision phenotyping
1, 1, 1, 1, 2, 3, 1, 4, 5, 6, 2, 7, 3, 1, 1, 11. 2. 3. 4. T-PIRC5. 6. 7. Imachi, Y.1, Y. Toda1, G. Sasaki1, Y. Omori1, Y. Yamasaki2, H. Takahashi3, H. Takanashi1, M. Tsuda4, Y. Sawada5, H. Kajiya-Kanegae6, H. Tsujimoto2, A. Kaga7, M. Nakazono3, T. Fujiwara1, H. Iwata1, W. Guo1 (1. Grad. Sch. Agr. Life Sci., Univ. Tokyo, 2. Arid Land Res. Ctr., Tottori Univ., 3. Grad. Sch. Bioagri. Sci., Nagoya Univ., 4. T-PIRC, Univ. Tsukuba, 5. Ctr. for Sustainable Resource Sci., RIKEN, 6. Res. Ctr. for Agr. Info. Tech., NARO, 7. Inst. Crop Sci., NARO)
(509-518)

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