KR-102963008-B1 - A shipment right time prediction supporting method based on water thermal growth coefficient for eel in recirulating aquaculture system

KR102963008B1KR 102963008 B1KR102963008 B1KR 102963008B1KR-102963008-B1

Abstract

The present invention relates to a recirculating aquaculture system for eel farming in which historical information for predicting the growth of eels during a set period from stocking to shipment is collected through a recirculating aquaculture data collection module, and a fish growth prediction module is formed using a regression equation based on the thermal growth coefficient (T W CG) using the collected historical information. When stocking information for eels for growth prediction is input using a shipment time prediction support module linked to the recirculating aquaculture data collection module and the fish growth prediction module, the shipment time prediction support module receives input information including the individual weight of the eels at the time of stocking, the rearing period to be predicted, and the number of eels stocked in the tank in an information input step; the individual weight at the time of stocking input through the information input step is transmitted as a variable to the fish growth prediction module to calculate the thermal growth coefficient (T W CG) value, and the individual weight of the eels on the scheduled selection date is calculated based thereon; and the individual weight of the eels on the scheduled selection date predicted through the growth prediction step Based on this, it includes a prediction information provision step that provides the final biomass (W f ) derived by calculating the number of animals stocked in the tank to the system operator, and is calculated through the regression equation of the water temperature growth coefficient (T W CG) and the formula for deriving the final biomass (W f ) derived in the growth prediction step. According to this, it has the advantage of being able to adjust the timing of stocking and shipment by confirming the period to reach the marketable size based on the stocking date.

Inventors

배재현
윤지현
박정환
이재만
운성천

Assignees

주식회사 아쿠아랩
아쿠아프로(주)

Dates

Publication Date: 20260511
Application Date: 20231228

Claims (3)

In a recirculating aquaculture system for eel farming, historical information for predicting eel growth during a set period from stocking to shipment is collected through a recirculating aquaculture data collection module, and the collected historical information is used to form a fish growth prediction module using a regression equation based on the temperature growth coefficient (T W CG); and when eel stocking information for growth prediction is input using a shipment time prediction support module linked to the recirculating aquaculture data collection module and the fish growth prediction module, In the above shipment time prediction support module, An information input step in which input information including the individual weight of the eel at the time of stocking, the rearing period to be predicted, and the number of eels to be stocked in the tank is entered, and A growth prediction step in which the individual weight at the time of stocking entered through the information input step is transmitted as a variable to the fish growth prediction module to calculate the water temperature growth coefficient (T W CG) value, and the individual weight of the eel on the scheduled selection date is calculated based thereon, and It includes a prediction information provision step of providing the final biomass (W f ) derived by calculating the number of individuals stocked in the tank based on the individual weight of the eels on the scheduled selection date predicted through the growth prediction step above to the system operator; A method for supporting the prediction of the shipping time of eels based on the water temperature growth coefficient in a recirculating filtration aquaculture system, characterized in that the regression equation for the water temperature growth coefficient (T W CG) is as in (Equation 2), and the final biomass (W f ) derived in the growth prediction step is calculated through (Equation 3). ................................... (Equation 2) ..........................(Equation 3) (where constants a and b are regression model constants derived based on the dataset stored in the above-mentioned recirculating aquaculture data collection module, W i is the individual weight at stocking, T is the water temperature variable, and W f is the final biomass (W i + d = W f , where d is the rearing period to be predicted))
In Article 1, After the above growth prediction step is performed, a feeding prediction step is further performed to derive the amount of feed (FV i ) fed in the daily unit tank using the derived final biomass (W f ), and In the above-mentioned level prediction step, A method for supporting the prediction of the shipping time of eels based on the temperature growth coefficient in a recirculating aquaculture system, characterized by calculating the amount of feed (FV i ) fed to a daily unit tank according to the unsold fish and total weight predicted by the fish growth prediction module using a feeding prediction regression equation such as (Equation 4) through the recirculating aquaculture data collection module and the daily feeding amount prediction module linked to the fish growth prediction module. .........................(Equation 4) (Where FR i is the feed rate (%), and FU i is the number of fishes/kg)
In Clause 2, the prediction information provision step includes, A growth prediction map of eels according to the scheduled selection date (D) provided through the above growth prediction step, and A method for supporting the prediction of the shipping time of eels based on the water temperature growth coefficient in a recirculating filtration aquaculture system, characterized by providing the amount of feed (FV i ) to be input when the curing process is repeated without shipping after the scheduled sorting date (D) provided through the above-mentioned feeding prediction step, so that the final biomass (W f ) at the time of stocking and the corresponding shipping prediction time, the rate of increase in individual weight after the shipping prediction time, and the total amount of feed are confirmed together.

Description

A method supporting shipment right time prediction based on water thermal growth coefficient for eels in recirulating aquaculture system The present invention relates to a method for supporting the prediction of the time of shipment of eels based on a water temperature growth coefficient in a recirculating filtration aquaculture system, which estimates the growth state of the eels to predict the time of shipment. Since the aquaculture industry relies mostly on wild glass eel resources for eel fry, a major species, the price of fry is determined by the annual catch volume. Meanwhile, eel farming is conducted using either the pond system or the recirculating filtration system, and as farming takes place within limited space, rearing types are classified to ensure economic viability. First, in the eel rearing type where the eels are raised and sold within one year, sales are made within one year of introducing the fry into the farm. Generally, fry are introduced in December or January, sales are completed by November or early December, and eels with poor growth are disposed of. In addition, in the eel farming type where the eel is raised for 1 year to 1 year and 6 months before being sold, the fry are introduced into the farm and raised for more than 1 year before sales begin, and are raised for up to 1 year and 6 months before sales begin. The fry are mainly introduced after March, and most shipments are made around May of the following year, and sales end in the autumn. In addition, in the eel rearing type where the product is sold after being raised for more than two years, the fry are overstocked and grown for more than two years, and the product is sold as the fastest-growing individuals. However, this rearing type has the problem of a high probability of disease and sluggish growth due to overcrowding, but it has the advantage of increasing profitability by stocking in large quantities when fry prices are low. Meanwhile, the weight range per eel is set according to the classification criteria for sales size, and during the farming process, there are individuals with stunted growth that fail to meet the set sales size. Since the aforementioned individuals with poor growth significantly affect aquaculture profits, a decision must be made whether to dispose of them in bulk at a fixed number per kg or to continue farming; however, aquaculture farms using conventional technology face difficulties in operation because there is no clear basis for making such a decision. Furthermore, in the case of eel farming, the timing for stocking fry following the capture of glass eels is limited, resulting in the concentration of harvesting times at farms during specific periods; consequently, it is difficult to generate significant profits relative to farming costs during such times. In this regard, Figure 1 is a diagram showing the past trends in shipment volume and shipment trends by size of eels, and it was confirmed that the main consumption size is 2 or 3 eels, and shipment volume is high during the summer season when the preference for health food is high. As mentioned above, when shipments are concentrated at a specific time, sales competition among eel farmers intensifies, and middlemen may take advantage of this situation to cause problems such as price manipulation, which may lead to consumer dissatisfaction regarding eel prices. Meanwhile, an examination of the cost composition of recirculating filtration eel farms reveals that fry costs account for the largest share, followed by feed costs, depreciation, and labor costs. Therefore, the determination of the stocking and shipping times can serve as the most significant factor in ensuring economic viability for eel farmers; however, conventional technology relies on results based on experience for such decisions, thus requiring the provision of more accurate information to ensure economic viability. Figure 1 is a diagram to show the past trends in shipment volume and shipment trends by size of eels. Figure 2 is a diagram illustrating the process of eel farming, which involves repeating stocking, rearing, and selection. FIG. 3 is a drawing showing an embodiment of a system configuration for providing a method to support the prediction of the shipping time of eels based on the water temperature growth coefficient in a recirculating filtration aquaculture system according to the present invention. FIG. 4 is a drawing showing an example of a growth prediction module according to the present invention and a data sheet for deriving the same. FIG. 5 is a diagram showing the process of providing shipment time prediction support information derived through a growth prediction module based on water temperature growth coefficient according to the present invention. The method for supporting predicted shipping time information based on the water temperature growth coefficient of eels in a recirculating filtration aquaculture system according to the present invention is most