SOLAR- POWERED IOT AQUAPONICS MONITORING WITH MACHINE LEARNING INTEGRATION

Abstract

The solar-powered loT-based sustainable aquaponics system integrates artificial intelligence (Al) and the Internet of Things (IoT) with renewable energy to enable real-time monitoring and management of fish and plant health. This innovative solution employs the YOLOv5 algorithm for machine learning, allowing non-invasive detection of diseases in fish and plants through images captured by an Rpi camera. Additionally, IoT-enabled sensors continuously monitor critical water parameters such as temperature, pH, and salinity to ensure optimal conditions. The system operates using solar energy, reducing operational costs and reliance on non-renewable resources. High-speed data transmission is achieved via a Web Socket-based platform for live sensor monitoring, while a multi-level alert system delivers timely notifications through SMS, email, and audible alarms to promote proactive management. By enhancing efficiency, cost-effectiveness, scalability, and sustainability, this comprehensive solution paves the way for the broader adoption of aquaponics in agriculture. The YOLOv5 model is deployed on the Grove Vision Al v2 module to support on-device inference and improve system responsiveness.

Specification

Al-based Fish Disease Detection: The focus is on advancing the precision of fish health monitoring systems. The Al-based Fish Disease Detection system leverages machine learning models to predict and detect diseases early by analyzing images captured by the RPi camera. The images are processed using the YOLOvS algorithm, deployed on the Grove Vision Al v2 module, allowing for real-time, accurate detection of fish diseases. This data- driven approach enhances disease management in aquaponics, enabling swift interventions and improving overall system health, potentially minimizing losses and ensuring more sustainable operations.



Integration of Solar Energy: Integrating solar energy in aquaponics reduces operational costs by providing a free and renewable energy source, leading to lower electricity bills. It enhances system sustainability. Solar energy promotes energy independence, allowing growers to operate autonomously and resiliently. Additionally, it can boost productivity by powering essential systems efficiently, ensuring optimal conditions for both fish and plants.
Overall, this integration supports environmentally friendly practices, making aquaponics a more viable and sustainable solution for food production.
• Real-time sensor data monitoring for aquaponics using WebSocket Connection :The key area of innovation is on real-time data tracking for aquaponics management. We created a website that uses the WebSocket communication to enable two-way interactive communication between the user's browser and the server. This enables smooth data flow between the sensors and the website, giving customers real-time information on vital factors like as temperature, humidity, pH, and water level. The system also includes a live graph that displays previously acquired data, providing useful insights into historical trends. This WebSocket-based technology improves aquaponics system efficiency and sustainability by removing server polling and optimizing data flow.

Al-based Plant Disease Detection for Improved Aquaponics Management: The focus is on enhancing the accuracy of plant health monitoring systems. The Al-based plant disease detection system utilizes machine learning models to predict and identify diseases at an early stage by analyzing photos captured by the RPi camera. The images are processed using the YOLOv5 algorithm, deployed on the Grove Vision Al v2 module, facilitating real-time and precise detection of plant diseases. This data-driven approach improves disease management in aquaponics, enabling timely interventions and enhancing overall system health, potentially reducing crop losses and promoting more sustainable farming practices.
• Multi-Level Alert System for Enhanced Agricultural Management: A primary focus of innovation lies in developing a robust alert system to enhance agricultural management, our alert system features three levels of notifications designed to ensure timely responses to critical sensor readings. Initially, if any sensor values exceed or drop below predefined threshold levels, a standard SMS alert is sent to the farmer responsible for that specific field using Pushbullet, effectively eliminating the need for a GSM module. In the second level, an email and SMS containing detailed sensor data is sent to the farm owner to"provide an overview of the situation. If no action is taken after these notifications, the third level activates a buzzer fixed in the field, providing an audible alert to draw the farmer's attention and prompt immediate intervention. This comprehensive alert system enhances communication and ensures proactive management of agricultural conditions.

BACKGROUND OF THE INVENTION
US9538733B2: A system and method of sustainable aquaponics that vertically integrates unique aquaponic system designs with alternative aquaculture fish feed sources, fingerling production methods, alternative aquaculture/farmed fish grow out models, and green energy sources that yield organic produce in the form of fruits and vegetables. A raceway system serves as the hub for grow-out throughout the warm and cold months. During the summer months, fish can be spawned and fed for steady growth, while during the winter months, the fish continue to grow at slower quite acceptable growth rates. Plants like legal (licensed) cannabis for medical use can be grown in plant areas near the raceways with very high yields.
US11412674B1: An automated aquaponics system and method of use comprising an autonomously operated plant growing conveyor belt system that allows the plant to germinate from seed capsules to fully grown plants that are then fully harvested for consumption while the plant roots are cut, chopped, cooked, and processed for the appropriate consumption of the aquarian species that resides within the aquaponic ecosystem.

KR101923530B1: According to the present invention, there is provided a water- grandfather; A cultivation unit coupled to the water receiving unit so as to be disposed above the water stored in the water receiving unit and providing a space in which plants can live; A circulation unij, disposed inside the water receiving unit and supplying water stored in the water receiving unit to the regenerating unit; And a filter unit disposed inside the water receiving unit to filter suspended substances or oil in water stored inside the water receiving unit; The water stored in the water receiving portion due to the difference in water level between the water stored in the water receiving portion and the water stored inside the circulation portion flows into the circulation portion and is filtered by the filter portion, Since it is not generated, it is quiet, and it can filter and remove suspended matters or oil without using electricity. Therefore, the electricity consumption is greatly reduced and the filter can be easily replaced. Thus, a technology that can improve the convenience of the management of the aqua phonics system .
In addition, a technology that facilitates water quality management by connecting a fish tank has been suggested

US20190343091 AT A self-contained closed aquaponics system comprises an aquarium tank attached side-by-side to a water container for growing plants, having a shared side. An electrically powered water pump streams the water from the aquarium tank via a pipe to the bottom of a compartment in the water container. When the water in the water tank exceeds a pre-set water level, the water are poured back to the aquarium tank via a recess or slit in the shared side. The compartment may comprise a bio-filter that is a sponge filter, a foam cartridge filter and the undergravel filter. The aquarium tank or the water container may be rectangular or cuboid shaped. A cover adapted to cover the water container may include multiple openings for mounting plants in pot nets therein, where roots of the plants are fed from the fish excretions in the aquarium tank after being filtered by the bio-filter.
Smart Aquaponics with Disease Detection, IEEE -Roysing Barosa; -et al The Mauritian Ministry of Agro-lndustry and Food Security's initiative to develop a smart aquaponics system aims to enhance agriculture by integrating traditional aquaculture with hydroponics and utilizing the Internet of Things (IoT) for continuous environmental monitoring. While this system provides real-time feedback and alerts through a mobile application and features advanced capabilities such as live streaming and image processing for disease detection, it encounters significant limitations. Scalability issues and the complexity of deploying advanced technologies across various agricultural settings pose challenges for widespread adoption.

US9538733B2 The system's reliance on seasonal variations slows fish growth during winter months. Our proposed solution with controlled temperature environments could optimize fish growth year-round, mitigating seasonal limitations.US 11412674B1 The automated conveyor system lacks flexibility for handling diverse crop types and customization for specific plant species. Our Al-based fish and plant disease detection system offers flexibility for diverse crops through real-time monitoring using Grove Vision Al v2 module .US20190343091A1 The system relies on electrical power for water circulation, making it vulnerable to power outages and increasing operational costs. Our solution with Integrated solar energy and automatic water recirculation system using relay can ensure continuous, sustainable operation by reducing dependence on external electrical power, improving system resilience and reducing long-term costs.
KR101923530B1 The system's manual filter replacement and limited water quality management may require frequent human intervention, reducing convenience for larger setups. Our solution with Automated real-time water quality monitoring and alerts reduces manual intervention and enhances system efficiency. Smart Aquaponics with Disease Detection, IEEE, The smart aquaponics system faces scalability challenges and complexities in deploying advanced technologies like loT and image processing for disease detection across diverse agricultural environments. Our proposed solution integrates cloud-based storage and a modular IoT architecture, enabling the system to scale efficiently while reducing complexity, facilitating easier deployment in various agricultural settings.

To provide continuous real-time monitoring of key environmental parameters like temperature, humidity, pH, and water levels to ensure optimal conditions for crops and fish in aquaponics. • To increase sustainability and production, by using sensor data and past trends, farmers and farm owners can make well-informed decisions. - To use Al-based systems to identify plant and fish diseases early on, enabling prompt treatment and lowering the possibility of large losses. • To provide a multi-level alert system that warns owners and farmers of potentially dangerous situations so that prompt action can be done to reduce the risks. • To save energy and increase the sustainability of the monitoring and management process, integrate solar-powered Internet of Things solutions. • To reduce human labor in farm management by automating monitoring tasks and make the best use of available resources, such as water and nutrients. • To encourage the use of data analytics and smart technology in conjunction with sustainable farming methods to uphold
SUMMARY OF THE INVENTION
The project aims to monitor the health of aquaponics systems by collecting real-time sensor data and YOLOv5 algorithms to detect plant and fish diseases using Grove Vision Al v2 module. This method offers advantages over traditional monitoring techniques, helping to optimize resource management, enhance system health, and promote sustainability. It enables faster and more accurate monitoring, benefiting both productivity and environmental conservation in aquaponics operations.

BACKGROUND TECHNOLOGY
The core technology for this project involves traditional methods such as manual monitoring and sensor-based techniques for managing aquaponics systems. While visual inspections and basic data logging have been utilized, these approaches often lack precision and efficiency. Existing monitoring systems focus on individual parameters without integrated analysis. The proposed solution leverages advanced sensor technologies and artificial intelligence, utilizing real-time data collection and machine learning algorithms to analyze critical factors such as water quality, fish and plant health. This innovative approach aims to provide a more effective, automated, and comprehensive monitoring solution for aquaponics systems.
BRIEF DESCRIPTION OF DRAWING
Fig. 1 Block Diagram of the Proposed System
This IoT-based aquaponics system employs the Grove Vision Al v2 module, which runs the YOLOv5 algorithm for early disease detection in plants and fish. An RPi camera is used solely for capturing image feeds, while the Grove Vision Al module processes these images for identifying potential issues. NodeMCU collects data from sensors monitoring temperature, pH, conductivity, and water levels in the fish tank, and controls a relay to regulate the water pump for maintaining optimal conditions. Sensor data is sent to the website using a Web Socket connection, and once the data reaches the website, it is then pushed to the cloud for storage. This setup enables continuous monitoring, with alerts and notifications helping ensure proactive maintenance, enhancing the system's sustainability and overall efficiency.


he sign-up page is the first screen of an aquaponics management app. Here, new users can create an account by entering their full name, email, and password. Once signed up, users gain access to the app's features, such as monitoring fish health, plant growth, and water quality.
The login page is designed for returning users of the aquaponics app. Users input their email and password to log in and access the main dashboard
Fig.3 Dashboard Page
The dashboard page, built with React, displays real-time data by establishing a WebSocket connection between the sensors and the website for seamless data transmission. It also visualizes historical data through graphical representations, providing insights into past trends.
Fig.4SMS Notification using Pushbullet
The Pushbullet SMS notification system sends real-time alerts to farmers' mobile devices when sensor readings exceed or fall below thresholds, enabling swift intervention. This enhances engagement and efficient management, improving decision-making and resource management for sustainable aquaponics operations. By integrating this system, owner can monitor their setup from anywhere, leading to improved decision-making and resource management for a more sustainable and productive operation.
Fig.5 Email received by the owner with sensor data
The email notification system, integrated with Adafruit, automatically sends detailed sensor data updates when readings exceed set thresholds. This allows users to efficiently monitor their aquaponics system remotely, enhancing management and responsiveness to maintain optimal conditions for plants and fish.
Fig.6 Fish-Disease Detection using YoloV5 algorithm
The YOLOv5 algorithm enables efficient disease detection in plants and animals by analyzing images captured by cameras. In this system, YOLOvS is deployed on the Grove Vision Al v2 module, which allows for on-device processing and real-time disease detection. This deployment offers significant advantages, including reduced latency, as images are processed locally rather than relying on cloud-based computations.

Existing System
Existing aquaponics systems face several significant challenges that hinder their effectiveness and sustainability. One major issue is inefficient resource use, as many systems struggle to optimize water management without automation, leading to potential overuse or inadequate supply. Additionally, excessive reliance on external fertilizers compromises the eco-friendly principles of aquaponics.
Scalability also presents challenges; traditional setups are often not designed for easy expansion, resulting in inconsistencies in water quality and crop health as operations grow. Many systems focus on a narrow range of fish and plant species, limiting biodiversity and increasing market vulnerability. "" Labor-intensive maintenance further complicates operations, as manual monitoring diverts time and- ' resources away from essential tasks. Difficulty in obtaining real-time updates due to siloed data hinders effective management and timely responses to environmental changes, negatively impacting both fish and plant health. Furthermore, inadequate disease prevention tools lead to higher mortality rates, as farmers often react only after issues become visible. Many systems struggle with self- sufficiency and face regulatory challenges that complicate compliance with food safety standards.
These obstacles underscore the need for innovative, integrated solutions that leverage technology to enhance aquaponics systems, ultimately ensuring better health and sustainability for both fish and
plants.
Outline of the proposed system
The proposed system integrates Al-based technologies, solar energy, and real-time data monitoring to enhance the efficiency and sustainability of aquaponics management. It features an Al-powered fish disease detection system that leverages an RPi camera and the YOLOv5 algorithm to analyze fish images in real-time, enabling early detection and intervention. Solar energy integration reduces operational costs and promotes energy independence, ensuring sustainable operation. Real-time sensor data monitoring, facilitated through Web Socket connections, tracks essential environmental factors such as temperature, pH, and water levels, providing users with live data and historical trends. Additionally, a multi-level alert system ensures timely responses to critical sensor readings, improving overall system health and productivity. The YOLOv5 model is deployed on the Grove Vision Al v2 module, allowing for on-device inference and enhanced system responsiveness.


Algorithm of Proposed System
You Only Look Oncc(YOLOvS)
YOLOv5 is lightweight and alsomone of the real time object detection algorith and optimized for edge devices, offering different model sizes to balance between performance and computational requirements. Its ability to detect objects quickly makes it ideal for applications requiring fast and accurate image processing.
Communication protocol of Proposed solution Websocket
A communication protocol called WebSocket enables full-duplex, real-time communication via a single, persistent connection between a client (such as a web browser) and a server. With WebSocket, two-way communication is continuous and the server may push updates instantaneously, unlike with regular HTTP, where the client needs to request updates frequently.
Because of this, it's perfect for apps that need to share data in real time, such chat apps, online games, and real-time monitoring systems.

We Claim: 1. The Solar-Powered loT Aquaponics Monitoring with Machine Learning Integration
comprising
i. A solar-powered aquaponics system equipped with IoT sensors connected to a NodeMCU for collecting data on water parameters such as salinity, pH, and
temperature; ii. A camera integrated with the Grove Vision Al v2 module for, real-time monitoring of plant and fish health; iii. A processing unit utilizing the YOLOv5 algorithm deployed on the Grove Vision Al v2 module for detecting potential diseases in plants and fish. iv. a communication module that can send information to a user interface via WebSocket communication; v. and an alert system set up to send out emails, SMS, and aural alarms in response to situations lhaL are delected 2. The Solar-Powered IoT Aquaponics Monitoring with Machinc Learning Integration of claim5 1, consist of sensors such as a temperature sensor (DS18B20), float sensor, pH sensor and conductivity sensor for collecting the real-time data from the aquaponics system. 3. A method for monitoring and managing a smart aquaponics system comprises real-time monitoring of plants and fish using an RPi camera, with the detection process taking place on the Grove Vision Al v2 module. 4. The method of claim 1 uses the YOLOv5 algorithm, with the machine learning model deployed on the Grove Vision Al v2 module, to detect both plant and fish diseases in real-time. 5. The Solar-Powered IoT Aquaponics Monitoring System with Machine Learning Integration of claim 1 utilizes a cost-effective NodeMCU microcontroller for system control, combined with the Grove Vision Al v2 module for real-time processing, instead of relying on a Raspberry Pi and cloud services. 6. The Solar-Powered IoT Aquaponics Monitoring with Machine Learning Integration, provides a user-friendly webpage featuring authenticated login and signup functionality for both the owner and the farmer, enabling secure access to real-time monitoring data and management tools for the aquaponics system. 7. The Solar-Powered IoT Aquaponics Monitoring with Machine Learning Integration of claim 6, comprising a user-friendly webpage that allows both the owner and the farmer to log in to their respective pages for remote monitoring of their aquaponics system, where the owner's page provides a comprehensive list of all farms, including their locations and the contact details of the farmers managing each farm, enabling the owner to navigate to specific farms and access relevant information from anywhere.

8. The Solar-Powered loT Aquaponics Monitoring with Machine Learning Integration of claim 6, wherein the farmers' page is designed to allow farmers to view only the details of the farms they manage, along with access to the owner's contact information. 9. The method of claim 1, wherein SMS alerts are sent to the farmer using Pushbullet, and email notifications are sent to the owner via the Adafruit platform, eliminating the need for a GSM
module
10. The Solar-Powered IoT Aquaponics Monitoring with Machine Learning Integration of claim 1, wherein if no action is taken after SMS and email alerts, a buzzer on the farm is triggered for audible notification and accompanied by a displayed alert message on the OLED display which is fixed in the farm, Eventually relay module automatically regulates the water level in the aquaponics system based on real-time sensor data.

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