A method for intensive feeding of tuna in offshore net cage culture

Abstract

The application discloses a kind of offshore net cage culture tuna reinforced feeding method.The method is detailed fish body nutrient composition and food habit analysis by different specifications, season tuna is caught in the wild, simultaneously establish a variety of bait fish nutrient composition database, on this basis, establish the correlation model of tuna precision nutrition deployment system, according to the diet preference and nutritional requirement of different stages tuna, automatically match suitable bait fish and its reinforced nutrient substance, realize scientific, precision nutrition collocation, and according to real-time environment and breeding conditions, adjust bait formula.Meanwhile, with the help of fish population monitoring system and water quality monitoring system, timely find fish population abnormal behavior and water quality abnormal situation, timely send early warning, take corresponding measures to prevent and treat disease.The application can realize scientific nutrition collocation of tuna, realize efficient, precision feeding of tuna, and effectively prevent the occurrence of tuna disease, improve the breeding benefit of tuna.

Description

Technical Field

[0001] This invention belongs to the field of marine fish farming technology, specifically relating to a method for enhanced feeding of tuna in marine cage culture. Background Technology

[0002] Tuna belong to the order Perciformes, suborder Scombroidei, and family Scombridae. They are widely distributed in the tropical, subtropical, and temperate waters of the Indian, Pacific, and Atlantic Oceans. In my country, they are mainly found in the East China Sea and South China Sea. They are highly migratory fish. Tuna are globally recognized as a high-end marine economic fish and an important species for deep-sea aquaculture. However, the breeding problem of tuna remains unsolved, with reliance primarily on wild-caught fry. There is no suitable formulated feed, and baitfish are the main source of nutrition. Baitfish are prone to spoilage during transportation and storage and may carry pathogens and parasites, leading to frequent disease outbreaks. Furthermore, the feed conversion ratio is high, typically exceeding 17. Studies have found that tuna have a high demand for DHA (docosahexaenoic acid), and disinfecting and supplementing the diet of prey fish is a key step in the domestication of deep-sea fish. In addition, there are also problems in tuna farming, such as a lack of basic data, reliance on experience, high feed conversion ratio, and serious problems with blind feeding. Furthermore, it is difficult to quickly detect abnormalities in fish and prevent outbreaks of fish diseases in advance. Summary of the Invention

[0003] The purpose of this invention is to provide a method for enhanced feeding of tuna in offshore cage culture. This method enables scientific nutritional matching during tuna farming, achieves efficient and precise feeding, reduces the feed conversion ratio, and effectively prevents tuna diseases.

[0004] To achieve the above objectives, the present invention adopts the following technical solution:

[0005] A method for intensive feeding of tuna in marine cage culture includes the following steps:

[0006] (1) Selection of bait fish and selection of fortified nutrients: Detailed analysis of the nutritional composition and diet of wild-caught tuna of different sizes and seasons was conducted, and a database of nutritional components of various bait fish was established. Based on this, a correlation model was established to automatically match suitable bait fish and fortified nutrients according to the dietary preferences and nutritional needs of tuna at different stages, so as to achieve scientific and precise nutritional matching, and adjust the bait formula according to the real-time environment and aquaculture conditions.

[0007] (2) Feed preparation: After determining the appropriate bait fish and fortified nutrients, the fresh bait fish are disinfected by soaking in fresh water or potassium permanganate solution. Then, the soaked bait fish are mixed with the fortified nutrients and pressed into pellet feed.

[0008] (3) Precise feeding:

[0009] a. By using an automatic feeder in conjunction with a fish monitoring system and a water quality monitoring system, and through comprehensive analysis of aquaculture environmental parameters, image recognition and machine learning algorithms, combined with comparison of historical monitoring data, the feed intake and hunger level of the fish are monitored in real time, and the feed amount and frequency are automatically adjusted to achieve intelligent and precise feeding.

[0010] b. Based on the fish release plan, the growth trend of the fish population is predicted by analyzing historical monitoring data and aquaculture environmental parameters, and a feeding plan is arranged in advance.

[0011] (4) Fish disease prevention: Abnormal fish behavior and water quality abnormalities are detected in a timely manner through fish monitoring system and water quality monitoring system, and timely warnings are issued. Corresponding measures are taken for disease prevention and treatment, and necessary disinfection is carried out around the breeding area.

[0012] Preferably, in step (1), the construction steps of the association model are as follows: by analyzing the nutritional components and diet of wild-caught tuna of different sizes and in different seasons, and at the same time analyzing the nutritional components of various bait fish, a bait fish composition database is established, and based on the dietary preferences and nutritional needs of tuna at different stages, an association model is established for tuna of different sizes and in different seasons and their pairing with different bait fish and the enhanced formula of the bait fish.

[0013] Preferably, in step (2), the disinfection by soaking in fresh water or potassium permanganate solution specifically means soaking in fresh water or 20 mg / L potassium permanganate solution for 10-20 minutes.

[0014] Preferably, in step (3), the application of the fish monitoring system is as follows: automatically monitor farmed fish through cameras, observe the feeding status and behavior of the fish, obtain data such as the average speed, acceleration and depth of the fish swimming in real time with the help of a motion target tracking algorithm, monitor the feeding situation of the fish, and calculate the changes in body shape of the fish before and after feeding through a model to estimate the appropriate amount of food for the fish.

[0015] Preferably, in step (3), the water quality monitoring system is specifically used to monitor the following water quality parameters in real time through the sensor device: dissolved oxygen, temperature, and pH value. By analyzing historical water quality data and applying machine learning algorithms, the system predicts the trend of water quality parameter changes in the future and issues early warnings to adjust the feeding strategy in a timely manner to ensure the stability of water quality in the aquaculture area.

[0016] The present invention has the following beneficial effects:

[0017] This invention proposes a novel method for fortifying the nutrition of tuna. Since the food intake plays a crucial role in changes in the fish's body composition, detailed nutritional composition and feeding habits of wild-caught tuna of different sizes and seasons were analyzed. Simultaneously, in-depth research was conducted on the nutritional composition of various prey fish. Based on this, a correlation model was established to automatically match suitable prey fish and fortifying nutrients according to the dietary preferences and nutritional needs of tuna at different stages. This technological innovation not only considers the characteristics of tuna and prey fish at different growth stages but also incorporates changes in the aquaculture environment, achieving efficient and precise feeding.

[0018] In addition to addressing other issues in tuna farming, this method also solves problems such as a lack of basic farming data, reliance on experience, high feed conversion ratios, and rampant indiscriminate feeding. By monitoring the fish's feed intake and hunger levels in real time, the method automatically adjusts the amount and frequency of feed, achieving precise feeding. Simultaneously, with the help of fish monitoring and water quality monitoring systems, abnormal fish behavior and water quality anomalies can be detected promptly, issuing early warnings and taking appropriate measures for disease prevention and treatment. The method of this invention enables efficient and precise feeding of tuna, effectively preventing tuna diseases and improving the profitability of tuna farming. Attached Figure Description

[0019] Figure 1 A roadmap for enhancing feeding methods in net cage culture of tuna. Detailed Implementation

[0020] To make the objectives, technical solutions, and advantages of this invention clearer, the technical solutions of this invention will be described in detail below. Obviously, the described embodiments are merely some embodiments of this invention, and not all embodiments. Based on the embodiments of this invention, all other implementation methods obtained by those skilled in the art without creative effort are within the scope of protection of this invention.

[0021] The method and route of the present invention for intensive feeding of tuna in net cage culture are as follows: Figure 1 As shown.

[0022] Example 1

[0023] Taking yellowfin tuna as an example, a method for intensive feeding of yellowfin tuna in marine cage culture includes the following steps:

[0024] (1) Selection of baitfish and formulation of fortified nutrients: The nutritional composition and diet of wild-caught yellowfin tuna of different sizes were analyzed (based on stomach contents, their diet mainly consisted of squid, cuttlefish, tuna, mackerel, and horse mackerel). Simultaneously, the nutritional composition of different baitfish was analyzed to establish a database of baitfish body components. Based on the dietary preferences and nutritional needs of tuna at different stages, a correlation model was established between tuna of different sizes and seasons and their paired baitfish and fortified baitfish formulations. This model was used to determine suitable baitfish and the necessary fortified nutrients.

[0025] Table 1. Amino acid composition of muscle from three sizes of yellowfin tuna (on a wet basis)

[0026]

[0027]

[0028] Note: ■ indicates essential amino acids. ▲ represents semi-essential amino acids, ▲ represents flavor-enhancing amino acids, TAA represents total amino acids, EAA represents essential amino acids, NEAA represents non-essential amino acids, DAA represents flavor-enhancing amino acids, and HEAA represents semi-essential amino acids.

[0029] Table 2 Nutritional evaluation of essential amino acids in the muscle of three sizes of yellowfin tuna

[0030]

[0031]

[0032] Note: * It is the first limiting amino acid; ** It is the second limiting amino acid.

[0033] The differences in body composition among fish of different sizes are influenced by many factors, generally attributed to ingested food, metabolic rate, and activity level. Ingested food plays a crucial role; a proper balance of protein and amino acids in the feed is essential for promoting fish growth and enhancing their immunity. A deficiency in certain essential amino acids can lead to stunted growth and loss of appetite. By measuring the body composition of yellowfin tuna of different sizes (see Table 1) – small (4.2±1.2) kg, medium (22.5±2.5) kg, and large (50.8±3.9) kg – we identified the key nutritional components of tuna and supplemented them in the feed.

[0034] Using AAS and CS scores (Table 2), according to the AAS score, the first limiting amino acid for all sizes of yellowfin tuna was valine, while the second limiting amino acid was threonine for small and medium-sized groups, and leucine for large-sized groups. Using the CS score, the first and second limiting amino acids for small and medium-sized groups were tryptophan and phenylalanine + tyrosine, respectively, while the first and second limiting amino acids for large-sized groups were phenylalanine + tyrosine and valine, respectively. Therefore, limiting amino acids should be added to the feed for nutritional fortification when preparing yellowfin tuna feed.

[0035] Table 3 Fatty acid composition of muscle from three sizes of yellowfin tuna

[0036]

[0037]

[0038] Note: SFA is total saturated fatty acids; MUFA is total monounsaturated fatty acids; PUFA is total polyunsaturated fatty acids; n-3PUFA is n-3 series polyunsaturated fatty acids; n-6PUFA is n-6 series polyunsaturated fatty acids; h / H is cholesterol-lowering fatty acids / cholesterol-raising fatty acids.

[0039] By measuring the fatty acid content of muscle in three sizes of yellowfin tuna (Table 3), the DHA:EPA ratio of yellowfin tuna was 7.30–8.71, which is higher than that of most fish species studied. The relatively high DHA and DHA:EPA ratio may be a characteristic of tuna species. In most feed formulations of fish oil, the DHA:EPA ratio rarely exceeds 2. Marine fish have a limited ability to convert EPA into DHA. These factors may affect the formulation of artificial nutrition feeds for tuna, and DHA fatty acids need to be added to the feed for fortification.

[0040] (2) Feed preparation: After determining the appropriate bait fish and fortified nutrients, the fresh bait fish are disinfected by soaking in fresh water or 20 mg / L potassium permanganate solution for 10-20 minutes. Then, the soaked bait fish are mixed with fortified nutrients (such as fish meal, multivitamins, fish oil, etc.) and pressed into pellet feed.

[0041] (3) Precise feeding:

[0042] a. An automatic feeder is used in conjunction with an underwater fish monitoring system and a water quality monitoring system. By analyzing aquaculture environmental parameters, using image recognition and machine learning algorithms, and comparing historical monitoring data, the feed intake and hunger level of the fish are monitored in real time, and the feed amount and frequency are automatically adjusted to achieve precise feeding.

[0043] b. Fish growth prediction: Based on the fish release plan, the growth trend of the fish is predicted by analyzing historical monitoring data and aquaculture environmental parameters, and a feeding plan is arranged in advance.

[0044] Applications of fish monitoring systems: These systems automatically monitor farmed fish using cameras, observing their feeding status and behavioral habits. Moving target tracking algorithms are employed to acquire data such as the average speed, acceleration, and depth of the fish swimming. Simultaneously, the systems monitor the fish's feeding activity and use models to calculate changes in body size before and after feeding, thus estimating the appropriate amount of food for the fish.

[0045] Application of Water Quality Monitoring Systems: Deep-sea cage aquaculture water quality monitoring systems use sensors and other equipment to monitor water quality parameters in real time, such as dissolved oxygen, temperature, and pH. These parameters are crucial for the stability of the aquaculture environment and the healthy growth of farmed organisms. By analyzing historical water quality data and applying machine learning algorithms, the systems predict trends in water quality parameters over a future period and issue early warnings. This helps aquaculture operators adjust feeding strategies in a timely manner to ensure water quality stability in the aquaculture areas.

[0046] (4) Prevention of fish diseases: Early warnings are issued promptly for abnormal fish behavior and water quality issues through fish monitoring and water quality monitoring systems. Preventive and treatment measures are taken, and the area around the feeding area is disinfected. Specifically:

[0047] a. Disease early warning: The fish monitoring system automatically monitors farmed fish and uses a motion target tracking algorithm to obtain data such as the average speed, acceleration and depth of the fish swimming. By analyzing the fish monitoring data, abnormal fish behavior is detected and an early warning is issued in a timely manner, so that measures can be taken to prevent and treat the disease.

[0048] b. Water quality anomaly detection and early warning: By analyzing water quality monitoring data, abnormal water quality can be detected in a timely manner and early warnings can be issued promptly. This helps aquaculture personnel to take remedial measures quickly to prevent possible accidents from occurring.

[0049] c. Disinfection around the feeding area: Disinfect the area around the feeding area of ​​deep-sea farmed fish as needed during feeding. Appropriate disinfectants, such as bleaching powder (or anthelmintics), can be used. Administer the medication via a pump around the feeding area during feeding. This medication treatment effectively prevents and controls diseases in farmed fish and improves their health.

[0050] The above are merely preferred embodiments of the present invention. It should be noted that the above preferred embodiments should not be considered as limitations on the present invention, and the scope of protection of the present invention should be determined by the scope defined in the claims. For those skilled in the art, several improvements and modifications can be made without departing from the spirit and scope of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for intensive feeding of yellowfin tuna cultured in offshore net cages, characterized in that, Includes the following steps: (1) Development of a fortified formula based on the nutritional characteristics of yellowfin tuna muscle: Nutritional analysis was performed on the muscle tissue of wild-caught yellowfin tuna of different sizes to obtain the ratio of docosahexaenoic acid to eicosapentaenoic acid in the muscle, and the limiting amino acids were determined by amino acid scoring or chemical scoring, with at least valine among the limiting amino acids. Establish a database of nutritional components for bait fish; Based on the analysis results, a dynamic association model is constructed, which is used for: a) Based on the ratio of docosahexaenoic acid to eicosapentaenoic acid in the muscle of yellowfin tuna, and considering that the ratio of yellowfin tuna is naturally between 7.30 and 8.71 and is significantly higher than that of conventional feed, it is determined that the feed needs to be targeted fortified with docosahexaenoic acid. b) Based on the identified limiting amino acid, determine which limiting amino acid needs to be targeted fortification; c) Output the selection of bait fish and the types of nutrients that need to be targeted for enhancement for yellowfin tuna at the current breeding stage; The types of nutrients that require targeted fortification include at least docosahexaenoic acid (DHA) and the limiting amino acids identified in step b). (2) Feed preparation: According to the formula output in step (1), the bait fish are soaked in a 20 mg / L potassium permanganate solution for 10-20 min for disinfection, then mixed with the nutrients that need to be targeted fortification and pressed into pellet feed. (3) Precision feeding: a) An automatic feeder is used in conjunction with a fish monitoring system and a water quality monitoring system. Through image recognition and machine learning algorithms, the feeding status of the fish is monitored in real time, and the amount and frequency of feed are automatically adjusted. b) Based on the fish release plan, analyze historical monitoring data to predict the growth trend of the fish and arrange the feeding plan in advance; (4) Fish disease prevention and control: The fish monitoring system and water quality monitoring system are used to monitor abnormalities in fish behavior and water quality, issue early warnings in a timely manner and take prevention and control measures.

2. The method according to claim 1, characterized in that, In step (1), the nutritional composition analysis of the muscle tissue of wild-caught yellowfin tuna of different sizes is specifically performed on three sizes of yellowfin tuna: small size 4.2±1.2 kg, medium size 22.5±2.5 kg and large size 50.8±3.9 kg.

3. The method according to claim 1 or 2, characterized in that, In step (1), the identified limiting amino acid is determined based on the evaluation results of amino acid scoring or chemical scoring.

4. The method according to claim 1, characterized in that, In step (1), the bait fish targeted for establishing the bait fish nutritional composition database include at least one of the following: squid, cuttlefish, trevally, mackerel, and horse mackerel.

5. The method according to claim 1, characterized in that, In step (3) a), the fish monitoring system obtains the average speed, acceleration and depth data of the fish swimming by the moving target tracking algorithm, and uses it to estimate the amount of food consumed.

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