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All Journal Journal of Robotics and Control (JRC) Journal of Applied Data Sciences
Mardeni, Mardeni
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Aerospace Engineering Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Electrical & Electronics Engineering Mechanical Engineering

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Smart Aquarium Design Using Raspberry Pi and Android Based Khairunisa, Khairunisa; Mardeni, Mardeni; Irawan, Yuda
Journal of Robotics and Control (JRC) Vol 2, No 5 (2021): September (Forthcoming Issue)
Publisher : Universitas Muhammadiyah Yogyakarta

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Abstract

For aquarium owners, sometimes their daily activities are busy with other busy activities was studied by nusantara[1]. With this density of activity it often makes it difficult for fish aquarium owners to provide fish with the feeding process, which is usually done manually when at home was studied by pasha[2]. From this problem, a smart aquarium device was designed to feed aquaculture fish automatically, namely Smart Aquarium Design Using Android-Based Raspberry Pi, designed to provide convenience in the process of maintaining fish in an aquarium. This aquarium can perform several actions such as fish feeding automatically can be done using Android via the internet network and control the aquarium decorative lights. To move the fish feeding valve, it uses a servo motor to drive the fish feeding valve and also uses a relay as an on / off aquarium decorative light. Fish feed machines can feed fish on a scheduled basis if the user forgets to feed fish. Smart aquarium is also equipped with a water filter so that aquarium water does not need to change water.
Temperature Monitoring System for Egg Incubators Using Raspberry Pi3 Based on Internet of Things (IoT) Purwanti, Siti; Febriani, Anita; Mardeni, Mardeni; Irawan, Yuda
Journal of Robotics and Control (JRC) Vol 2, No 5 (2021): September (Forthcoming Issue)
Publisher : Universitas Muhammadiyah Yogyakarta

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Abstract

The incubator is made as a substitute for hatching naturally at the same time. The success of the hatch machine is largely determined by the temperature stability in the incubator. In the use of small-scale hatcheries for native chicken hatching, they are still faced with the problem of low hatchability due to one of the obstacles, namely the power outage during the hatching process. To improve the monitoring performance of egg incubators, the writer wishes to conduct research "Monitoring to Control and Monitor Temperature in Egg Incubators" using a webcam camera to monitor temperature conditions and hatch eggs. The working system is the DHT11 sensor will detect the temperature, the webcam camera in real time will monitor the state of the eggs then the raspberry pi3 will automatically control the temperature and electrical energy on the incubator, the smartphone monitors and can also control the temperature with the state of the eggs in realtime, 12V battery as a replacement energy when the PLN goes out. From the results of the tests carried out, the authors can draw conclusions, namely: The webcam camera can display the condition of the eggs in the incubator room. Android smartphones can receive information with a webcam camera during the hatching process. The data logger can display recapitulated temperature and humidity data.
Improved Hybrid Machine and Deep Learning Model for Optimization of Smart Egg Incubator Febriani, Anita; Wahyuni, Refni; Mardeni, Mardeni; Irawan, Yuda; Melyanti, Rika
Journal of Applied Data Sciences Vol 5, No 3: SEPTEMBER 2024
Publisher : Bright Publisher

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47738/jads.v5i3.304

Abstract

This research develops a Smart Egg Incubator that integrates IoT technology, fuzzy logic, and the YOLOv9-S Deep Learning model to enhance the efficiency and accuracy of hatching chicken eggs. The system automatically regulates temperature and humidity, maintaining temperature between 34.3°C and 39.5°C and humidity between 57% and 68% with a fuzzy logic success rate of 90%. The YOLOv9-S model enables realtime chick detection and classification with mAP50 of 93.7% and mAP50:95 of 71.3%. Efficiency improvements are measured through the success rate of fuzzy logic and improved detection and classification accuracy. This research also uses CNN for high-accuracy object classification, with model optimization performed using SGD to accelerate convergence and improve accuracy. The results indicate significant potential in improving the egg hatching process. The high accuracy and robustness of the YOLOv9-S model enhance real-time monitoring and decision-making in hatcheries, leading to higher hatching success rates, reduced chick mortality, and increased operational efficiency. Future designs can leverage these technologies to create more intelligent, automated systems requiring minimal human intervention, enhancing productivity and scalability. Additionally, IoT and deep learning integration can extend to other poultry farming areas, such as broiler production and disease monitoring, providing a comprehensive approach to farm management. Future research could focus on integrating the YOLOv10 model for even higher accuracy and efficiency, exploring diverse data augmentation techniques, optimizing fuzzy logic algorithms, and integrating additional sensors like CO2 and advanced humidity sensors to improve environmental regulation. These advancements would benefit not only smart incubator applications but also broader poultry farming areas.
Co-Authors Anita Febriani Khairunisa, Khairunisa Melyanti, Rika Purwanti, Siti Refni Wahyuni Yuda Irawan