Araştırma Makalesi
Yıl 2025,
Cilt: 31 Sayı: 1, 71 - 79, 14.01.2025
Öz
Proje Numarası
BAP-13/137
Kaynakça
- Ahmad A L, Chin J Y, Harun M H Z M & Low S C (2022). Environmental impacts and imperative technologies towards sustainable treatment of aquaculture wastewater: A review. Journal of Water Process Engineering 46: 102553
- Ahmed M, Rahaman M O, Rahman M & Kashem M A (2019). Analyzing the quality of water and predicting the suitability for fish farming based on iot in the context of bangladesh. In 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI) (pp. 1–5).
- IEEE Akgül İ, Kaya V & Zencir Tanır Ö (2023). A novel hybrid system for automatic detection of fish quality from eye and gill color characteristics using transfer learning technique. Plos one 18(4): e0284804
- Baird R B, Eaton A D & Rice E W (2017) Standart Methods for The Examination of Water and Wastewater, 23st Edition, American Public Health Association, Washington.
- Cakir M, Yilmaz M, Oral M A, Kazanci H Ö & Oral O (2023). Accuracy assessment of RFerns, NB, SVM, and kNN machine learning classifiers in aquaculture. Journal of King Saud University-Science 35(6): 102754
- Çöteli F T (2023). Aquaculture Product Report (In Turkish). Tepge publication No: 373, Republic of Turkey Ministry of Agriculture and Forestry, Ankara.
- Dikel S & Öz M (2022). Artificial intelligence (AI) application in aquaculture. In ISPEC 10th International Conference on Agriculture, Animal Sciences and Rural Development, July 18-19, Sivas, Turkey.
- Devi P A, Padmavathy P, Aanand S & Aruljothi K (2017). Review on water quality parameters in freshwater cage fish culture. International Journal of Applied Research 3(5): 114–120
- Draper N R & Smith H (1998). Applied regression analysis. John Wiley & Sons. Du L, Lu Z & Li D (2023). A novel automatic detection method for breeding behavior of broodstock based on improved YOLOv5. Computers and Electronics in Agriculture 206: 107639
- EU 2006. Directive 2006/44/EC of the European Parliament and of the Council of 6 September 2006 on the quality of fresh waters needing protection or improvement in order to support fish life
- Firooz F, Mehdi R, Mostafa F, Alireza M & Gholamhossein N F (2012). Evaluation of physicochemical parameters of waste water from rainbow trout fish farms and their impacts on water quality of Koohrang stream –Iran. International Journal of Fisheries and Aquaculture 4(8): 170–177
- Folke C & Kautsky N (1992). Aquaculture with its environment: prospects for sustainability. Ocean & coastal management 17(1): 5-24
- Géron A (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media, Inc. Hargreaves J A (1998). Nitrogen biogeochemistry of aquaculture ponds. Aquaculture 166(3-4): 181-212
- Hu W C, Chen L B, Huang B K & Lin H M (2022). A computer vision-based intelligent fish feeding system using deep learning techniques for aquaculture. IEEE Sensors Journal 22(7): 7185-7194
- James G, Witten D, Hastie T, Tibshirani R & Taylor J (2013). An Introduction to Statistical Learning: with Applications in R. Springer, Switzerland.
- Jana B B & Sarkar D (2005). Water quality in aquaculture-Impact and management: A review. The Indian Journal of Animal Sciences 75(11)
- Kaya V (2023). A Perspective on Transfer Learning in Computer Vision. In: Kılıc G B (Ed.). Advances in Engineering Sciences, Platanus Publishing, Ankara, Turkey
- Kaya V, Akgül İ & Tanır Ö Z (2023). IsVoNet8: a proposed deep learning model for classification of some fish species. Journal of Agricultural Sciences 29(1): 298-307
- Koçer M A, Muhammetoğlu A, Emre Y, Sevgili H, Türkgülü İ, Kanyılmaz M, Yılayaz A, Uysal R, Emre N, Mefut A, Uysal G, Yalım B & Topcuoğlu Ö A (2010). Determination of the Effects of Fish Farming and Basin-Related Pollution on the Eşen Stream Ecosystem by Using Mathematical Modeling Methods and Control of Nutrient Flux to the Mediterranean (in Turkish). (TÜBİTAK Project No: 107Y084).
- Leaf A. & Weber P C (1998). Cardiovascular effects ofn- 3 fatty acids. The New England Journal of Medicine 318: 549-557
- Li D, Wang Z, Wu S, Miao Z, Du L & Duan Y (2020). Automatic recognition methods of fish feeding behavior in aquaculture: A review. Aquaculture 528: 735508 Mandal A & Ghosh A R (2024). Role of artificial intelligence (AI) in fish growth and health status monitoring: A review on sustainable aquaculture. Aquaculture International 32(3): 2791-2820
- McDaniel N K, Sugiura S H, Kehler T, Fletcher J W, Coloso R. M, Weis P & Ferraris R P (2005). Dissolved oxygen and dietary phosphorus modulate utilization and effluent partitioning of phosphorus in rainbow trout (Oncorhynchus mykiss) aquaculture. Environmental Pollution 138(2): 350-357
- Moldan B, Janoušková S & Hák T (2012). How to understand and measure environmental sustainability: Indicators and targets. Ecological indicators 17: 4-13
- Muir J (2005). Managing to harvest? Perspectives on the potential of aquaculture. Philosophical Transactions of the Royal Society B: Biological Sciences 360(1453): 191-218
- Pahlow M, Van Oel P R, Mekonnen M M & Hoekstra A Y (2015). Increasing pressure on freshwater resources due to terrestrial feed ingredients for aquaculture production. Science of the Total Environment 536: 847-857
- Pedersen C L (1987). Energy budgets for juvenile rainbow trout at various oxygen concentrations. Aquaculture 62(3-4): 289-298
- Pulatsü S & Yıldırım H B (2011) Evaluation of Outlet Water Characteristics of Land-Based Trout Farms of Different Capacities (Mugla, Fethiye) (in Turkish). Ankara University Scientific Research Project Final Report, Project no: 09B4347009
- Qin C, Xue Q, Zhang J, Lu L, Xiong S, Xiao Y & Wang J (2024). A beautiful China initiative towards the harmony between humanity and the nature. Frontiers of Environmental Science & Engineering 18(6): 1-9
- Rana M, Rahman A, Dabrowski J, Arnold S, McCulloch J & Pais B (2021). Machine learning approach to investigate the influence of water quality on aquatic livestock in freshwater ponds. Biosystems Engineering 208: 164-175
- Sezgin S S, Yilmaz M, Arslan T & Kubilay A (2023). Current antibiotic sensitivity of Lactococcus garvieae in rainbow trout (Oncorhynchus mykiss) farms from Southwestern Turkey. Journal of Agricultural Sciences 29(2): 630-642
- Sharma I & Birman S (2024). Biodiversity Loss, Ecosystem Services, and Their Role in Promoting Sustainable Health. In The Climate-Health-Sustainability Nexus: Understanding the Interconnected Impact on Populations and the Environment (pp. 163-188). Cham: Springer Nature, Switzerland.
- Subasinghe R, Soto D & Jia J (2009). Global aquaculture and its role in sustainable development. Reviews in aquaculture 1(1): 2-9
- Ubina N, Cheng S C, Chang C C & Chen H Y (2021). Evaluating fish feeding intensity in aquaculture with convolutional neural networks. Aquacultural Engineering 94: 102178
- Woynarovich A, Hoitsy G & Moth-Poulsen T (2011). Small-scale rainbow trout farming. FAO fisheries and aquaculture technical paper, (561). Food and agriculture organization of the united nations, Rome
- Yang L, Liu Y, Yu H, Fang X, Song L, Li D & Chen Y (2021). Computer vision models in intelligent aquaculture with emphasis on fish detection and behavior analysis: A review. Archives of Computational Methods in Engineering 28(4): 2785–2816
- Yilmaz M, Çakir M, Oral M A, Kazanci H Ö & Oral O (2023). Evaluation of disease outbreak in terms of physico-chemical characteristics and heavy metal load of water in a fish farm with machine learning techniques. Saudi Journal of Biological Sciences 30(4): 103625.
- Yilmaz M, Çakir M, Oral O, Oral M A & Arslan T (2022). Using machine learning technique for disease outbreak prediction in rainbow trout (Oncorhynchus mykiss) farms. Aquaculture Research 53(18): 6721-6732
- Zhao S, Zhang S, Liu J, Wang H, Zhu J, Li D & Zhao R (2021). Application of machine learning in intelligent fish aquaculture: A review. Aquaculture 540: 736724
- Zhou C, Xu D, Chen L, Zhang S, Sun C, Yang X & Wang Y (2019). Evaluation of fish feeding intensity in aquaculture using a convolutional neural network and machine vision. Aquaculture 507: 457-465
Application of the Machine Learning Methods to Assess the Impact of physico-chemical characteristics of water on Feed Consumption in Fish Farms
Öz
Machine learning (ML) methods, which are one of the subfields of artificial intelligence (AI) and have gained popularity in applications in recent years, play an important role in solving many challenges in aquaculture. In this study, the relationship between changes in the physico-chemical characteristics of water and feed consumption was evaluated using machine learning methods. Eleven physico-chemical characteristics (temperature, pH, dissolved oxygen, electrical conductivity, salinity, Nitrite nitrogen, nitrate nitrogen, ammonium nitrogen, total phosphorus, total suspended solids, and biological oxygen demand) of water were evaluated in terms of fish feed consumption by using ML methods. Among all the measured physico-chemical characteristics of water, temperature was determined to be the most important parameter to be evaluated in fish feeding. Moreover, pH2, eC2, TP2, TSS2, S2 and NO2 parameters detected in the outlet water are more important than those detected in the inlet water in terms of feed consumption. In the regression analysis carried out using ML techniques, the models developed with RF, GBM and XGBoost algorithms yielded better results.
Anahtar Kelimeler
aquaculture feed intake artificial intelligence rainbow trout sustainability
Destekleyen Kurum
Scientific Research Projects Coordination Unit of Mugla Sıtkı Kocman University
Proje Numarası
BAP-13/137
Kaynakça
- Ahmad A L, Chin J Y, Harun M H Z M & Low S C (2022). Environmental impacts and imperative technologies towards sustainable treatment of aquaculture wastewater: A review. Journal of Water Process Engineering 46: 102553
- Ahmed M, Rahaman M O, Rahman M & Kashem M A (2019). Analyzing the quality of water and predicting the suitability for fish farming based on iot in the context of bangladesh. In 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI) (pp. 1–5).
- IEEE Akgül İ, Kaya V & Zencir Tanır Ö (2023). A novel hybrid system for automatic detection of fish quality from eye and gill color characteristics using transfer learning technique. Plos one 18(4): e0284804
- Baird R B, Eaton A D & Rice E W (2017) Standart Methods for The Examination of Water and Wastewater, 23st Edition, American Public Health Association, Washington.
- Cakir M, Yilmaz M, Oral M A, Kazanci H Ö & Oral O (2023). Accuracy assessment of RFerns, NB, SVM, and kNN machine learning classifiers in aquaculture. Journal of King Saud University-Science 35(6): 102754
- Çöteli F T (2023). Aquaculture Product Report (In Turkish). Tepge publication No: 373, Republic of Turkey Ministry of Agriculture and Forestry, Ankara.
- Dikel S & Öz M (2022). Artificial intelligence (AI) application in aquaculture. In ISPEC 10th International Conference on Agriculture, Animal Sciences and Rural Development, July 18-19, Sivas, Turkey.
- Devi P A, Padmavathy P, Aanand S & Aruljothi K (2017). Review on water quality parameters in freshwater cage fish culture. International Journal of Applied Research 3(5): 114–120
- Draper N R & Smith H (1998). Applied regression analysis. John Wiley & Sons. Du L, Lu Z & Li D (2023). A novel automatic detection method for breeding behavior of broodstock based on improved YOLOv5. Computers and Electronics in Agriculture 206: 107639
- EU 2006. Directive 2006/44/EC of the European Parliament and of the Council of 6 September 2006 on the quality of fresh waters needing protection or improvement in order to support fish life
- Firooz F, Mehdi R, Mostafa F, Alireza M & Gholamhossein N F (2012). Evaluation of physicochemical parameters of waste water from rainbow trout fish farms and their impacts on water quality of Koohrang stream –Iran. International Journal of Fisheries and Aquaculture 4(8): 170–177
- Folke C & Kautsky N (1992). Aquaculture with its environment: prospects for sustainability. Ocean & coastal management 17(1): 5-24
- Géron A (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. O'Reilly Media, Inc. Hargreaves J A (1998). Nitrogen biogeochemistry of aquaculture ponds. Aquaculture 166(3-4): 181-212
- Hu W C, Chen L B, Huang B K & Lin H M (2022). A computer vision-based intelligent fish feeding system using deep learning techniques for aquaculture. IEEE Sensors Journal 22(7): 7185-7194
- James G, Witten D, Hastie T, Tibshirani R & Taylor J (2013). An Introduction to Statistical Learning: with Applications in R. Springer, Switzerland.
- Jana B B & Sarkar D (2005). Water quality in aquaculture-Impact and management: A review. The Indian Journal of Animal Sciences 75(11)
- Kaya V (2023). A Perspective on Transfer Learning in Computer Vision. In: Kılıc G B (Ed.). Advances in Engineering Sciences, Platanus Publishing, Ankara, Turkey
- Kaya V, Akgül İ & Tanır Ö Z (2023). IsVoNet8: a proposed deep learning model for classification of some fish species. Journal of Agricultural Sciences 29(1): 298-307
- Koçer M A, Muhammetoğlu A, Emre Y, Sevgili H, Türkgülü İ, Kanyılmaz M, Yılayaz A, Uysal R, Emre N, Mefut A, Uysal G, Yalım B & Topcuoğlu Ö A (2010). Determination of the Effects of Fish Farming and Basin-Related Pollution on the Eşen Stream Ecosystem by Using Mathematical Modeling Methods and Control of Nutrient Flux to the Mediterranean (in Turkish). (TÜBİTAK Project No: 107Y084).
- Leaf A. & Weber P C (1998). Cardiovascular effects ofn- 3 fatty acids. The New England Journal of Medicine 318: 549-557
- Li D, Wang Z, Wu S, Miao Z, Du L & Duan Y (2020). Automatic recognition methods of fish feeding behavior in aquaculture: A review. Aquaculture 528: 735508 Mandal A & Ghosh A R (2024). Role of artificial intelligence (AI) in fish growth and health status monitoring: A review on sustainable aquaculture. Aquaculture International 32(3): 2791-2820
- McDaniel N K, Sugiura S H, Kehler T, Fletcher J W, Coloso R. M, Weis P & Ferraris R P (2005). Dissolved oxygen and dietary phosphorus modulate utilization and effluent partitioning of phosphorus in rainbow trout (Oncorhynchus mykiss) aquaculture. Environmental Pollution 138(2): 350-357
- Moldan B, Janoušková S & Hák T (2012). How to understand and measure environmental sustainability: Indicators and targets. Ecological indicators 17: 4-13
- Muir J (2005). Managing to harvest? Perspectives on the potential of aquaculture. Philosophical Transactions of the Royal Society B: Biological Sciences 360(1453): 191-218
- Pahlow M, Van Oel P R, Mekonnen M M & Hoekstra A Y (2015). Increasing pressure on freshwater resources due to terrestrial feed ingredients for aquaculture production. Science of the Total Environment 536: 847-857
- Pedersen C L (1987). Energy budgets for juvenile rainbow trout at various oxygen concentrations. Aquaculture 62(3-4): 289-298
- Pulatsü S & Yıldırım H B (2011) Evaluation of Outlet Water Characteristics of Land-Based Trout Farms of Different Capacities (Mugla, Fethiye) (in Turkish). Ankara University Scientific Research Project Final Report, Project no: 09B4347009
- Qin C, Xue Q, Zhang J, Lu L, Xiong S, Xiao Y & Wang J (2024). A beautiful China initiative towards the harmony between humanity and the nature. Frontiers of Environmental Science & Engineering 18(6): 1-9
- Rana M, Rahman A, Dabrowski J, Arnold S, McCulloch J & Pais B (2021). Machine learning approach to investigate the influence of water quality on aquatic livestock in freshwater ponds. Biosystems Engineering 208: 164-175
- Sezgin S S, Yilmaz M, Arslan T & Kubilay A (2023). Current antibiotic sensitivity of Lactococcus garvieae in rainbow trout (Oncorhynchus mykiss) farms from Southwestern Turkey. Journal of Agricultural Sciences 29(2): 630-642
- Sharma I & Birman S (2024). Biodiversity Loss, Ecosystem Services, and Their Role in Promoting Sustainable Health. In The Climate-Health-Sustainability Nexus: Understanding the Interconnected Impact on Populations and the Environment (pp. 163-188). Cham: Springer Nature, Switzerland.
- Subasinghe R, Soto D & Jia J (2009). Global aquaculture and its role in sustainable development. Reviews in aquaculture 1(1): 2-9
- Ubina N, Cheng S C, Chang C C & Chen H Y (2021). Evaluating fish feeding intensity in aquaculture with convolutional neural networks. Aquacultural Engineering 94: 102178
- Woynarovich A, Hoitsy G & Moth-Poulsen T (2011). Small-scale rainbow trout farming. FAO fisheries and aquaculture technical paper, (561). Food and agriculture organization of the united nations, Rome
- Yang L, Liu Y, Yu H, Fang X, Song L, Li D & Chen Y (2021). Computer vision models in intelligent aquaculture with emphasis on fish detection and behavior analysis: A review. Archives of Computational Methods in Engineering 28(4): 2785–2816
- Yilmaz M, Çakir M, Oral M A, Kazanci H Ö & Oral O (2023). Evaluation of disease outbreak in terms of physico-chemical characteristics and heavy metal load of water in a fish farm with machine learning techniques. Saudi Journal of Biological Sciences 30(4): 103625.
- Yilmaz M, Çakir M, Oral O, Oral M A & Arslan T (2022). Using machine learning technique for disease outbreak prediction in rainbow trout (Oncorhynchus mykiss) farms. Aquaculture Research 53(18): 6721-6732
- Zhao S, Zhang S, Liu J, Wang H, Zhu J, Li D & Zhao R (2021). Application of machine learning in intelligent fish aquaculture: A review. Aquaculture 540: 736724
- Zhou C, Xu D, Chen L, Zhang S, Sun C, Yang X & Wang Y (2019). Evaluation of fish feeding intensity in aquaculture using a convolutional neural network and machine vision. Aquaculture 507: 457-465
Toplam 39 adet kaynakça vardır.
Ayrıntılar
| Birincil Dil | İngilizce |
|---|---|
| Konular | Hayvan Besleme, Hayvan Yetiştirme, Balık Yetiştiriciliği |
| Bölüm | Makaleler |
| Yazarlar | |
| Proje Numarası | BAP-13/137 |
| Yayımlanma Tarihi | 14 Ocak 2025 |
| Gönderilme Tarihi | 17 Nisan 2024 |
| Kabul Tarihi | 31 Temmuz 2024 |
| Yayımlandığı Sayı | Yıl 2025 Cilt: 31 Sayı: 1 |
Kaynak Göster
Journal of Agricultural Sciences is published as open access journal. All articles are published under the terms of the Creative Commons Attribution License (CC BY).