표제지
목차
요약문 5
SUMMARY 6
제1장 연구개발과제의 개요 9
제1절 연구개발의 목적 9
1. 연구개발 배경 9
2. 연구개발 필요성 9
3. 연구개발 목표 10
제2장 국내외 기술 개발 현황 11
제1절 해외 기술 개발 현황 11
제2절 국내 기술 개발 현황 13
제3장 연구개발 수행내용 및 결과 14
제1절 스마트양식 모델 개발 14
1. 해상 스마트양식장 플랫폼 개발 14
2. 육상 스마트양식장 플랫폼 개발 30
제2절 해산어의 생물학적 데이터 수집 및 분석 40
1. 실내 사육을 통한 생물학적 데이터 수집 및 분석 40
2. 해상가두리 사육을 통한 생물학적 데이터 수집 및 분석 46
제3절 담수어의 생물학적 데이터 수집 및 분석 49
1. 대상어종별 양성 데이터 수집 49
2. 사육환경 조건별 성장특성 분석 57
3. 대상어종별 최적 양성조건 규명 62
4. 스마트양식장 운영 및 결과 분석 66
제4절 스마트양식 기술 개발에 따른 경제성 분석 67
1. 대상어종별 양식업 현황 및 경영비용 조사 67
2. 개발기술의 경제성 분석 67
제5절 스마트양식 산업화 기술 개발 68
1. 어류의 먹이공급 조건별 효율성 평가 68
2. 대상어종별 양성 데이터 수집 및 분석 플랫폼 개발 70
3. 스마트양식장 운영 SW 산업화 기술 개발 72
제4장 목표달성도 및 관련분야에의 기여도 83
제5장 연구개발결과의 활용계획 84
제6장 참고문헌 85
제7장 부록 86
판권기 88
Table 1. The number of standard patents related to feeding machines 16
Table 2. The number of valid patents for feeding machines 16
Table 3. The number of standard patents related to sorting machines 16
Table 4. The number of valid patents for sorting machines 17
Table 5. Survival rate, food quantity and feed efficiency during the experiment period 23
Table 6. Primer DNA sequences 24
Table 7. Slip performance test 38
Table 8. Growth performance of indoor-reared gray mullet and red sea bream 41
Table 9. Rearing conditions of gray mullet and red sea bream 42
Table 10. Growth performance of gray mullet and red sea bream reared at different densities 43
Table 11. Growth performance of gray mullet reared at different water temperatures 44
Table 12. Growth performance of red sea bream reared at different water temperatures 45
Table 13. Environmental conditions of gray mullet reared at different DO concentrations 45
Table 14. Growth performance of gray mullet reared at different DO concentrations 45
Table 15. Environmental conditions of red sea bream reared at different DO concentrations 46
Table 16. Growth performance of red sea bream reared at different DO concentrations 46
Table 17. Water temperature (WT) and dissolved oxygen (DO) during the experiment 47
Table 18. Water temperature (WT) and dissolved oxygen (DO) during the experiment 48
Table 19. Growth performance analysis of catfish fry druing the BFT rearing experiment 50
Table 20. Breeding water quality analysis during the rearing experiment (immature catfish) 51
Table 21. Growth performance of catfish during the rearing experiment 51
Table 22. Growth performance of eel fry during the rearing experiment 53
Table 23. Growth performance of eel during the rearing experiment 55
Table 24. Growth performance of catfish during the rearing experiment 56
Table 25. Growth performance of catfish by breeding density during the BFT rearing experiment 58
Table 26. Growth performance of eel by breeding density 59
Table 27. Growth performance of eel by feeding rate 60
Table 28. Growth performance of eel by feeding method (60 days) 61
Table 29. Growth performance of catfish by feeding method (30 days) 62
Table 30. Growth performance of catfish by feeding rate 63
Table 31. Growth performance of eel by feeding rate 64
Table 32. Growth performance of eel by water temperature (60 days) 65
Table 33. Growth performance of catfish by water temperature (60 days) 66
Table 34. Yearly production amount and sales volume 67
Table 35. Cost for smart aquaculture 68
Table 36. Design of operating system for the land-based fish farm 76
Fig. 1. Major domestic automatic feeding machines 14
Fig. 2. Automatic sorting machine. A, VAKI; B, FAIVRE 15
Fig. 3. Conceptual diagram of smart aquaculture system applied with artificial intelligence 19
Fig. 4. Basic feeding algorithm for AI Application 20
Fig. 5. An example of behavioral pattern change analysis. A, at a normal status; B, during feeding 20
Fig. 6. Changes in water temperature and dissolved oxygen. A, water temperature; B, dissolved oxygen 22
Fig. 7. Monthly growth change. A, total length; B, body length; C, body depth; D, total weight 23
Fig. 8. Monthly growth rate 24
Fig. 9. Monthly hepatosomatic index 24
Fig. 10. Growth-related hormone expression level by feeding frequency. A, GH; B, IGF-1 25
Fig. 11. Changes in growth hormone concentration by feeding frequency. A, GH; B, IGF-1 25
Fig. 12. mRNA expression level by water temperature. A, GH; B, IGF-1 25
Fig. 13. Changes in growth hormone concentration by water temperature. A, GH; B, IGF-1 25
Fig. 14. Changes in stress hormone concentration by feeding frequency. A, cortisol; B, glusose 26
Fig. 15. Changes in trypsin concentration by feeding frequency 26
Fig. 16. Changes in stress hormone concentration by water temperature. A, cortisol; B, glusose 26
Fig. 17. Changes in trypsin concentration by water temperature 26
Fig. 18. Concept of sea-based smart aquaculture platform 28
Fig. 19. Configuration of sea-based smart aquaculture platform (A, control panel; B, automatic feeding machine; C, security camera; D, weather station;... 28
Fig. 20. Real-time water temperature monitoring system 29
Fig. 21. Sea-based smart aquaculture platform 29
Fig. 22. Smart aquaculture platform. A, system configuration; B, aquaculture facilities; C, control room 30
Fig. 23. Smart aquaculture operating system. A, network configuration; B, operating software 31
Fig. 24. Feeding activity measurement by using acceleration sensor. A, acceleration sensor; B, activity measurement 31
Fig. 25. Design of filling/withdrawing machine 32
Fig. 26. Implementation of filling and withdrawing machine 32
Fig. 27. Configuration of feeding system 32
Fig. 28. Implementation of feeding system. A, feeding device by using air compression; B, dust collector; C, cleaning device 33
Fig. 29. Fish behavior monitoring system. A, monitoring facilities; B, video steaming data 33
Fig. 30. Smart aquaculture operating software. A, login screen; B, main screen; C, fish tank information; D, Detailed information on the selected fish tank;... 34
Fig. 31. Aquaculture management robot (prototype). A, robot configuration; B, feeding test; C, charging test 35
Fig. 32. Driving module. A, driving module design; B, sensor deployment for autonomous driving 35
Fig. 33. Implementation of driving module. A, left side; B, right side 36
Fig. 34. Design change of aquaculture management robot 36
Fig. 35. Concept of filling machine 37
Fig. 36. Design of filling machine and control unit 37
Fig. 37. Implementation and test of filling machine 37
Fig. 38. Durability test. A, urethane wheel; B, rubber wheel 38
Fig. 39. Docking mechanism by using depth camera. A, docking performance test; B, operation SW 39
Fig. 40. Comparison of docking time. A, before improvement; B, after improvement 39
Fig. 41. Proximity warning system. A, system facilities; B, object detection, recognition and tracking; C, warning message 40
Fig. 42. Water temperature, salinity, desolved oxygen(DO) and pH during the indoor rearing of gray mullet and red sea bream 41
Fig. 43. Schematic diagram of the experimental system. A: gray mullet, B: red sea bream 43
Fig. 44. The location of smart aquaculture test field. A, red seabream; B, mullet 47
Fig. 45. Monthly changes of red seabream. A. Total length; B, body weight 47
Fig. 46. Monthly growth rates of gray mullet. A, total length; B, body weight 48
Fig. 47. Breeding water quality analysis during the rearing experiment(Catfish fry) 49
Fig. 48. Breeding water quality analysis during the rearing experiment (immature catfish) 50
Fig. 49. Water quality analysis during the catfish rearing experiment. A, water temperature, desolved oxygen(DO), pH; B, NH₄⁺-N, NO₂⁻N; C, Total suspended solid (TSS) 51
Fig. 50. Changes in total length and body weight during the rearing experiment. A, total length; B, body weight 52
Fig. 51. Water quality analysis during the eel rearing experiment. A, water temperature, DO, pH (immature eel); B, water temperature, DO, pH (mature eel);... 52
Fig. 52. Changes in body weight (g) during the eel rearing experiment. A, immature eel; B, mature eel 53
Fig. 53. Internal and external infection condition during the catfish and eel rearing experiment. A, catfish; B, eel; C, dissection picture 53
Fig. 54. Water quality analysis during the eel rearing experiment. A, water temperature, dissolved oxygen, pH (BFT); B, NH₄⁺-N, NO₂⁻-N, NO₃⁻-N (BFT);... 54
Fig. 55. Changes in body weight (g) during the eel rearing experiment. A, BFT; B, CRS 55
Fig. 56. Water quality analysis during the catfish rearing experiment. A, water temperature, DO, pH (BFT); B, NH₄⁺-N, NO₂⁻-N, NO₃⁻-N (BFT); C, water temperature,... 56
Fig. 57. Changes in body weight (g) during the catfish rearing experiment. A, BFT; B, CRS 57
Fig. 58. Water quality analysis by breeding density during the catfish rearing experiment. A, 0.5kg/m²; B, 1kg/m²; C, 5kg/m²; D, 10kg/m²; E, 20kg/m² 58
Fig. 59. Changes in body weight (g) of eel by breeding density 59
Fig. 60. Water quality analysis by feeding method during the eel rearing experiment. A, water temperature, dissolved oxygen, pH; B, NH₄⁺-N, NO₂⁻-N, NO₃⁻-N 60
Fig. 61. Water quality analysis by feeding method during the catfish rearing experiment. A, water temperature, dissolved oxygen, pH; B, NH₄⁺-N, NO₂⁻-N, NO₃⁻-N 61
Fig. 62. Changes in body weight (g) by feeding method. A, eel; B, catfish 62
Fig. 63. Water quality analysis by feeding method during the catfish rearing experiment. A, water temperature, DO, pH; B, NH₄⁺-N, NO₂⁻N, NO₃⁻N 63
Fig. 64. Water quality analysis by feeding method during the eel rearing experiment. A, water temperature, dissolved oxygen, pH; B, NH₄⁺-N, NO₂⁻-N 63
Fig. 65. Changes in body weight (g) by feeding method. A, eel; B, catfish 64
Fig. 66. Changes in body weight (g) by water temperature. A, eel; B, catfish 66
Fig. 67. User manual. A, SW manual; B, HW manual 66
Fig. 68. Swimming behavior at normal state 69
Fig. 69. Swimming behavior at feeding start (left) and feeding end (right) 69
Fig. 70. Vision-based body length measurement 69
Fig. 71. Feed loss rate analysis. A, intermittent feeding; B, continuous feeding; C, feed loss rate by feeding method 70
Fig. 72. Data collection and analysis system. A, fish behavior monitoring system; B, data analysis system 71
Fig. 73. Time series data prediction with LSTM method. A, water temperature; B, dissolved oxygen; C, pH 71
Fig. 74. Vision-based fish length measurement. A, Edge extraction; B, YOLO; C, Image connected componet labeling; D, Mask R-CNN 72
Fig. 75. Vision-based feeding activity measurement 72
Fig. 76. Data gathering system 77
Fig. 77. Data exchange among system components 78
Fig. 78. Standard document for smart aquaculture data types (draft). (black) 78
Fig. 79. Land-based smart aquaculture system 79
Fig. 80. Cage-based smart aquaculture system 80
Fig. 81. Data gathering and analysis 81
Fig. 82. Data structure for land-based fish farm 81
Fig. 83. Smart aquaculture system. A, sea-based smart aquaculture platform; B, land-based smart aquaculture platform; C, aquaculture management robot;... 83