| 4/3
| Á°´üÆüÄøÄ´À°
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| 4/10
| ±¡À¸¸¦µæ¾Ò²ð(4²óÀ¸¸þ¤±)¡¡Ô軳(çõ)¡¢ÃæÅè¡¢¾¾²¬
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| 4/17
| ±¡À¸¸¦µæ¾Ò²ð(4²óÀ¸¸þ¤±) ÂçÃÝ¡¢Äø¡¢
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| 4/24
| ±¡À¸¸¦µæ¾Ò²ð(4²óÀ¸¸þ¤±) ¶µØæ¡¢ÍûÍÌ¡¢Î¹ë·æ
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| 5/1
| ¤ä
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| 5/8
| M1 ʸ¸¥¥ì¥Ó¥å¡¼ ¥Á¡¼¥àA Ô軳[IDW,NDT]¡¢½ù[SOA]
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| 5/15
| M1 ʸ¸¥¥ì¥Ó¥å¡¼B µÜùõ¡¢º¸
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| 5/22
| M1 ʸ¸¥¥ì¥Ó¥å¡¼:¥ê¥â¥»¥ó: ¡¢Â¹ / ÃæÅè: ¿§ÊäÀµ, ¾¾²¬: ¥É¥í¡¼¥ó, Ȭ½½Åç: ÃâÁÇ, ¹: Robot Tractor
|
| 5/29
| ¤ä
|
| 6/5
| M2Êó¹ð
Kyon: Human Detection using 3d-LIDAR in Real-Time
Take: Semantic Segmentation for deer detection
|
| 6/12
| M2Êó¹ð
KAWASAKI: Estimation of the Number of rice stem for the appropriate decision of midsummer drainage with drone
Cheng: human detection a combine harvester using yolo algorithm with panoramic camera
|
| 6/19
| Li Yang: speeding up regional segmentation based on deep learning in the rice field
Liu: realtime human detection by stereo camera for unmanned harvester
crop specific variable rate fertilization
|
| 6/26
| Sikai Chen ¼«¸Ê¾Ò²ð
|
| 7/3
| M1 ¸¦µæ·×²è
|
| 7/10
| M1 ¸¦µæ·×²è
Ô軳: The automatic driving system and obstacle detection for robotic combine's navigation on the farm road
¾¾²¬: Research plan ¼ýÎÌͽ¬Ū¤Ê´¶¤¸
½ùÌÐο: ¿¼ï¤ÇÊ£¿ôÂæ¤ÎÇÀ¶È¥í¥Ü¥Ã¥È¤ò¥ê¥¢¥ë¥¿¥¤¥à¤Ç´Æ»ë¤¹¤ë¥·¥¹¥Æ¥à
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| 7/17
| M1 ¸¦µæ·×²è
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| 7/24
| ͽÈ÷Æü
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| 7/31
|
|
| 9/4
|
|
| 9/11
|
|
| 9/18
| ¸å´üÆüÄøÄ´À°
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| 9/25
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Kyon, Suyama, Takma, LiYang, Take
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| 10/2
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| 10/9
| 4²óÀ¸Â´ÏÀ³µÍ×Êó¹ð
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| 10/16
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| 10/23
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| 10/30
| M2 All (4̾) + D3
ÂçÃÝ: Deer Segmentation by Semantic Segmentation Progress
²Ïºê: ¿å°ð¤ÎÃæ´³¤·»þ´ü¤Ë¤ª¤±¤ëUAV¤òÍѤ¤¤¿¥ê¥â¡¼¥È¥»¥ó¥·¥ó¥°
ÄÄ: Human detection for combine harvester using YOLO algorithm and rear view camera
Íû¶µ: ¥³¥ó¥Ð¥¤¥óºî¶È¤Ë¤ª¤±¤ë3D-LIDAR¤ÎÈ¿¼ÍÆÃÀ¤òÍѤ¤¤¿¾ã³²Êª¸¡½Ð
ÍûÍÈ: Semantic segmentation of rice field image based on sensor fusion
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| 11/6
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| 11/13
| Morylin: ¥¹¥Þ¡¼¥ÈÇÀ¶È¤Ë¤ª¤±¤ëÇÀ¶È¥í¥Ü¥Ã¥È¤Î»ÈÍѾõ¶·, ¥á¥ë¥Ü¥ë¥ó¤Î¤ª¤â¤Ò¤Ç¡£
M1 Team-A
Ô軳: ROS¤ò±þÍѤ·¤¿¥í¥Ü¥Ã¥È¥³¥ó¥Ð¥¤¥ó¤Î³«È¯, ¥È¥ë¥³À¬Éþ.
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½ù: WEB¤òÍѤ¤¤ÆÇÀ¶È¥í¥Ü¥Ã¥È¤ò¥â¥Ë¥¿¥ê¥ó¥°¤¹¤ë¥·¥¹¥Æ¥à
ÃæÅè: Calculation of Yellow Grain Rate by Smartphone Application
ÄÄ: ¥¹¥Þ¡¼¥ÈÇÀ¶È¼Â¾ÚÊà¾ì¤Ë¤ª¤±¤ë¼ýÎ̥ޥåפκîÀ®
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| 11/20
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| 11/27
| 12:45¤«¤é M1 Team-B
Ȭ½½Åç: ¥¹¥Þ¡¼¥È¥Õ¥©¥ó¤òÍѤ¤¤¿°ð¤ÎºÇŬÄÉÈîÎÌ¿äÄê
µÜºê: ¥â¥Î¥¯¥í¡¼¥à²èÁü¤òÍѤ¤¤¿¿¼Áسؽ¬¤Ë¤è¤ë¼¯¤Î¸¡½Ð
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Sun: Ã滳´ÖÃÏÊà¾ì¤Ë¤ª¤±¤ë½Úŷĺ¬°Ì¥·¥¹¥Æ¥à¤Î¬°ÌÀºÅÙ
Zuo: ¥í¥Ü¥Ã¥È¥³¥ó¥Ð¥¤¥ó¾×ÆͲóÈò¤Î¤¿¤á¤Î¥Ç¥×¥¹¥«¥á¥é¤òÍѤ¤¤¿¾ã³²Êª¸¡½Ð
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| 12/4
| B4 All
Morylin: the Management Status of Autonomous Farm Machinery on a Smart Agriculture Demonstration Project
¥¹¥Þ¡¼¥ÈÇÀ¶Èµ»½Ñ¤òÍѤ¤¤¿¿å°ðºîÂηϤˤª¤±¤ëºî¶È»þ´Ö¤ÎʬÀÏ
Dawa: Deer parts detection with YOLO v3
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Koide: Deer's Faces Identification Using VGG16
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Kato: Detection Skips in Rice Fields for Analysis of Unmanned Aerial Vehicle (UAV) Images.
UAV²èÁüʬÀϤˤè¤ë¿å°ðÊà¾ìÃæ¤Î·ç³ô¸¡½Ð
Ota: Detection of Harvestable Shiitake Mushroom by R-CNN
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| 12/11
| (¤ä)¥»¥ó¥¿¡¼»î¸³ÀâÌÀ²ñ
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| 12/18
| M2 All
ÂçÃÝ: Deer Detection by Semantic Segmentation, Semantic Segmentation ¤òÍѤ¤¤¿¼¯¤Î¸¡½Ð
Deer Detection by Deep Learning -- SS and Yv3
²Ïºê: Remote Sensing of the Rice Stem Number in midsummer drainage with UAV ¿å°ð¤ÎÃæ´³¤·»þ´ü¤Ë¤ª¤±¤ëUAV ¤òÍѤ¤¤¿¥ê¥â¡¼¥È¥»¥ó¥·¥ó¥°
UAV¥ê¥â¡¼¥È¥»¥ó¥·¥ó¥°¤Ë¤è¤ë¿å°ð¤ÎÃæ´³¤·»þ´ü¤Ë¤ª¤±¤ë·Ô¿ô¤Î¿äÄê estimation of rice stem number in midsummer drainage with UAV Remote Sensing
Íû(Kyon): °ð¼ý³Ïºî¶È¤Ë¤ª¤±¤ë3D-LIDAR¤ÎÈ¿¼ÍÆÃÀ¤òÍѤ¤¤¿¾ã³²Êª¸¡½Ð Obstacle Detection using Intensity Value of 3D-LIDAR during Rice Harvesting
ÄÄ: Human Detection for a Combine using YOLO Algorithm and Panoramic Camera
|
| 12/25
| ͽÈ÷Æü ¥á¥ê¡¼¥¯¥ê¥¹¥Þ¥¹
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| 1/1
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| 1/8
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½ùÌÐο: web¤òÍѤ¤¤ÆÇÀ¶È¥í¥Ü¥Ã¥È¤ò±ó³Ö´ÉÍý¤¹¤ë¥·¥¹¥Æ¥à
ÄÄ»×kai: ¥¹¥Þ¡¼¥ÈÇÀ¶È¼Â¾ÚÊà¾ì¤Î¾ðÊó¥Þ¥Ã¥×
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ÍûÍÌ: Semantic segmentation of rice field environment based on RGB and depth images
|
| 1/15
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ÂÀÅÄ: Judging the proper stage for harvesting of Shiitake Mushroom in mushroom bed by R-CNN
²ÃÆ£: Detecting lack of seedling in the rice field for analysis of unmanned aerial vehicle (UAV) image
ÌÚɶ: Deer parts detection with YOLOv3
¾®½Ð: Identification of deer faces using VGG16
¿¹: The analysis of working time in paddy rice production systems using smart agricultural technologies
|
| 1/22
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¥¥ç¥ó: °ð¼ý³Ïºî¶È¤Ë¤ª¤±¤ë[3D-RIDER¤Ë¤è¤ë]È¿¼ÍÆÃÀ¤òÍѤ¤¤¿¾ã³²Êª¸¡½Ð
Cheng: Human Detection for a Combine using YOLO Algorithm and Panoramic Camera
|
| 1/29
| (¤ä)¸å´ü»î¸³´ü´Ö
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| 2/5
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| 2/12
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¤¹»³: Development of obstacle detection method and autonomous driving system on the firm road for a robotic agricultural vehicle
¤¢¤ê¤µ¤µ¤ó: Yield prediction of rice using drone
¤Ï¤ä¤ª: progress and research plan
£¸£°¤·¤Þ: ¡¡optimal estimation of topdressing by using smartphone app
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| 2/19
| ´ÏÀȯɽ²ñÎý½¬ 1
B-07 ¿¹Éö»Ò: ¥¹¥Þ¡¼¥ÈÇÀ¶Èµ»½Ñ¤òÍѤ¤¤¿¿å°ðºîÂηϤˤª¤±¤ëºî¶È»þ´Ö¤ÎʬÀÏ
B-08 ²ÃÆ£·òÂÁ: UAV²èÁüʬÀϤˤè¤ë¿å°ð¤Û¾ìÃæ¤Î·ç³ô¸¡½Ð
B-16 ÌÚɶÎÇ: YOLOv3¤òÍѤ¤¤¿¥·¥«¤ÎÉô°Ì¸¡½Ð
B-17 ¾®½Ð±ÑÍý: ¥Ç¥£¡¼¥×¥é¡¼¥Ë¥ó¥°¤Ë¤è¤ë¥·¥«¤Î´é¼±ÊÌ
C-19 ÂÀÅļÂΤ: Mask R-CNN¤òÍѤ¤¤¿¶Ý¾²¥·¥¤¥¿¥±¤Î¼ý³ÏŬ´üȽÄê
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| 2/26
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