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¿µ¹®Ç¥Áظí Metadata Schema for Management of AI Training Data Platform in the Field of Agriculture
Çѱ۳»¿ë¿ä¾à 2023³â ÇϹݱâºÎÅÍ ³óÃÌÁøÈïû¿¡¼­´Â Çѱ¹Áö´ÉÁ¤º¸»çȸÁøÈï¿øÀÇ ÀΰøÁö´É Çãºê Ç÷§Æû¿¡ ±¸ÃàµÇ¾îÀÖ´Â ³óÃà»ê ºÐ¾ß µ¥ÀÌÅ͸¦ ³ó¾÷ºÐ¾ß ÀΰøÁö´É ÇнÀ¿ë µ¥ÀÌÅÍ Ç÷§Æû¿¡ À̰üÇϱâ·Î °áÁ¤ÇÔ¿¡ µû¶ó ÇØ´ç µ¥ÀÌÅÍ¿¡ ´ëÇÑ °øÀ¯ ¹× Ȱ¿ëÀ» À§ÇÑ ¸ÞŸµ¥ÀÌÅÍ ½ºÅ°¸¶¸¦ °³¹ßÇϱâ·Î ÇÏ¿´´Ù. ÀÌ·¯ÇÑ ¿¬À¯·Î º» Ç¥ÁØ¿¡¼­´Â ³óÃà»ê ºÐ¾ß ÀΰøÁö´É ÇнÀ¿ë µ¥ÀÌÅÍ 33Á¾¿¡ ´ëÇÑ ¸ÞŸµ¥ÀÌÅÍ ½ºÅ°¸¶¸¦ Á¦½ÃÇϰíÀÚ ¹üÁ¤ºÎ(°ø°øµ¥ÀÌÅͺ£À̽º Ç¥ÁØÈ­ °ü¸® ¸Å´º¾ó) ¹× ±â°ü(³óÃÌÁøÈïû) Ç¥ÁØ¿ë¾î¸¦ Àû¿ëÇÏ¿© ¿ä¼Ò¸í(Ä÷³¸í)¿¡ ´ëÇÑ Ç¥ÁØÈ­¸¦ ÁøÇàÇÏ¿´´Ù. Ç¥ÁØÈ­ ÁøÇà °á°ú, ³óÃà»ê ºÐ¾ß ÀΰøÁö´É ÇнÀ¿ë µ¥ÀÌÅÍ °ü¸®¸¦ À§ÇÑ ¸ÞŸµ¥ÀÌÅÍ ¿ä¼Ò´Â ÃÑ 308°³°¡ µµÃâµÇ¾úÀ¸¸ç, ÀÌ Áß À̹ÌÁöÆÄÀÏ Á¤º¸´Â 66°³, ¾î³ëÅ×ÀÌ¼Ç Á¤º¸´Â 247°³·Î ³ªÅ¸³µ´Ù. º» Ç¥ÁØÀ¸·Î Á¦¾ÈÇÑ ³óÃà»ê ºÐ¾ß ÀΰøÁö´É ÇнÀ¿ë µ¥ÀÌÅÍ °ü¸®¸¦ À§ÇÑ ¸ÞŸµ¥ÀÌÅÍ ½ºÅ°¸¶´Â ¾ÕÀ¸·Î ü°èÀûÀÎ µ¥ÀÌÅÍ °ü¸®°¡ °¡´ÉÇÒ °ÍÀÌ¸ç µ¥ÀÌÅÍ °ü¸®¸¦ ÅëÇÑ ¿¬±¸ÀÇ È¿À²¼º ¹× »ý»ê¼ºÀÌ Áõ´ëµÉ »Ó¸¸ ¾Æ´Ï¶ó ³ª¾Æ°¡¼­ ÀΰøÁö´É ÇнÀ¿ë µ¥ÀÌÅÍÀÇ °ü¸®, °øÀ¯ ¹× Àç»ç¿ë¼ºÀ» È®º¸ÇÒ ¼ö ÀÖ´Â ±Ù°Å°¡ ¸¶·ÃµÉ °ÍÀÌ´Ù.
¿µ¹®³»¿ë¿ä¾à As the Rural Development Administration has decided to transfer the agriculture and livestock data built on the AI Hub platform of the National Information Society Agency to the data platform for AI learning in agriculture from the second half of 2023, it has decided to develop a metadata schema for sharing and utilization of such data. For this reason, this standard presents a metadata schema for 33 types of data for AI learning in the agriculture and livestock sector, and standardizes element names (column names) by applying standard terms from the government (Public Database Standardization Management Manual) and institutions (Rural Development Administration). As a result of the standardization process, a total of 308 metadata elements for data management for AI learning in the field of agriculture and livestock were derived, of which 66 were image file information and 242 were annotation information. The metadata schema for data management for AI learning in agriculture and livestock, proposed as a standard, will enable systematic data management and increase the efficiency and productivity of research through data management, as well as provide a basis for securing the management, sharing, and reusability of data for AI learning.
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