IoT Based Fish Pond Monitoring System to Enhance Its Productivity

Kumar, Surendra; Mohammed, Thayyaba Khatoon; Rao, Seeram Srinivasa; P, Dinesh Anton Raja; Bakhare, Ruhi; Kumar, Amrendra; Sarkar, Santu

doi:10.1109/iciptm57143.2023.10117894

Cited by 2 publications

(1 citation statement)

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“…Kumar et al [26], focused to manage the environmental conditions of a fish pond and ensure the well-being of specific fish species, this research proposes an intelligent IoT based water monitoring system. The smart system incorporates a network of real-time sensors responsible for continuous data collection.…”

Section: Literature Surveymentioning

confidence: 99%

IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support

Dhinakaran

2023

IJRITCC

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In response to the burgeoning global demand for seafood and the challenges of managing fish farms, we introduce an innovative IoT-based environmental control system that integrates sensor technology and advanced machine learning decision support. Deploying a network of wireless sensors within the fish farm, we continuously collect real-time data on crucial environmental parameters, including water temperature, pH levels, humidity, and fish behavior. This data undergoes meticulous preprocessing to ensure its reliability, including imputation, outlier detection, feature engineering, and synchronization. At the heart of our system are four distinct machine learning algorithms: Random Forests predict and optimize water temperature and pH levels for the fish, fostering their health and growth; Support Vector Machines (SVMs) function as an early warning system, promptly detecting diseases and parasites in fish; Gradient Boosting Machines (GBMs) dynamically fine-tune the feeding schedule based on real-time environmental conditions, promoting resource efficiency and fish productivity; Neural Networks manage the operation of critical equipment like water pumps and heaters to maintain the desired environmental conditions within the farm. These machine learning algorithms collaboratively make real-time decisions to ensure that the fish farm's environmental conditions align with predefined specifications, leading to improved fish health and productivity while simultaneously reducing resource wastage, thereby contributing to increased profitability and sustainability. This research article showcases the power of data-driven decision support in fish farming, promising to meet the growing demand for seafood while emphasizing environmental responsibility and economic viability, thus revolutionizing the future of fish farming.

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Section: Literature Surveymentioning

confidence: 99%

IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support

Dhinakaran

2023

IJRITCC

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Remote Monitoring, Control and Data Visualization for a Solar Water Pumping System

Ahmed,

Iqbal

2023

EJECE

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Access to clean water is a significant challenge in many regions, including Sukkur, Pakistan. The effective management of water resources is a critical challenge, particularly in areas with limited access to surface water sources. This paper presents a remote monitoring of water pumping system designed to address water distribution challenges in Sukkur, Pakistan. The system utilizes a combination of hardware components, including Arduino Uno, Raspberry Pi 2, ultrasonic sensor, and GSM modules, to enable remote monitoring, control, and data visualization. The system architecture incorporates Node-RED, a powerful flow-based programming tool, to facilitate data communication, storage, and visualization. To enable remote monitoring and control, users can send SMS commands to the Arduino Uno, connected to the GSM module, to query the system's status and control the pump's operation. Additionally, a mobile application developed using the MIT App Inventor platform allows users to interact with the system, visualize real-time and historical data, and receive updates on water levels and pump status. The Raspberry Pi 2 serves as a server and cloud storage for the system.

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IoT Based Fish Pond Monitoring System to Enhance Its Productivity

Cited by 2 publications

References 13 publications

IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support

IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support

Remote Monitoring, Control and Data Visualization for a Solar Water Pumping System

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