Author
Listed:
- Hasan Mahmud
(Hamdard University Bangladesh)
- Md. Afzalur Rahaman
(Hamdard University Bangladesh)
- Subrata Hazra
(Hamdard University Bangladesh)
- Shariar Ahmed
(Hamdard University Bangladesh)
Abstract
Bangladeshi shrimp aquaculture has shown to be successful and efficient at earning foreign cash. The country covers 147570 square kilometers, 17% of which is roughly made up of coastal brackish water. Shrimp farming is thought to be more favorable in this larger coastal tide area, and 0.276 million hectares of land are being used for brackish water shrimp farming. The coastal area offers the best prospects for shrimp and prawn cultivation for two key reasons. The mangrove ecosystems' distinctive biodiversity is the first justification, and a shrimp-friendly habitat is the second. A large number of fishing vessels are sent out into the seas in order to mine shrimp which is undoubtedly considered a challenging job. Therefore, growing shrimp in tanks, especially in tropical regions, is more sensible for farmers. In this paper, we have developed an IoT-based solution that connects devices to collect data on shrimp farms, and sensing equipment and then sends it to a distant server to analyze and generate decisions. This smart farm offers real-time agriculture monitoring. The main components of the system are three embedded sensors to assess the temperature, turbidity, and light that impact the quality of the water. The system also incorporates of an android-based mobile application that facilitates farmers with remote monitoring capabilities to keep track of sensor readings, manage the shrimp production cycle, and check on the health of shrimp from different farms.
Suggested Citation
Handle:
RePEc:epw:comput:v:3:y:2023:i:1:id:10089
DOI: 10.24018/compute.2023.3.1.89
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:epw:comput:v:3:y:2023:i:1:id:10089. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Support Team (email available below). General contact details of provider: https://eu-opensci.org/index.php/compute .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.