Monitoring System and Temperature Controlling on PID Based Poultry Hatching Incubator

Shafiudin, Sofyan; Kholis, Nur

doi:10.1088/1757-899x/336/1/012007

Cited by 8 publications

(5 citation statements)

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“…The main objective of this system is to develop an automated system for hatching chickens that can use PID controllers to fix an incubator's temperature response so that it remains stable and in accordance with the set-point temperature value. Introduced by [26] the system uses GPRS and WSN connectivity to autonomously regulate and track environmental factors in the poultry farm. The internal environmental data of the poultry farm is available to farmers through text message on their registered cellphone number.…”

Section: Literature Reviewmentioning

confidence: 99%

Intelligent Control Shed Poultry Farm System Incorporating With Machine Learning

Ali,

Imran,

Baig

et al. 2024

IEEE Access

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The traditional Control Shed Poultry Farm (CSPF) systems are operated manually or in a semi-automatic culture. The existing CSPF system is still not free from human intervention and needs 24/7 monitoring. The existing approach is laborious, prone to caretakers bias and human error, and repeatability is difficult. The Intelligent CSPF system is based on the Internet of Things (IoT) and Machine Learning (ML). The proposed approach comprises the learning and mastering phase. In the learning phase, the system will observe or learn from user behaviour patterns regarding poultry activity operations for a specific time duration. Upon completion of the learning phase, the mastering phase will eventually automate the system and control the poultry farm's environmental parameters, such as temperature, humidity, water level, light, and hazardous gases based on the historical data. The proposed system will replicate the operator's past business control behavior. For this learning task, we have used Supervised ML techniques to analyze the performance of ML algorithms such as Random Forest (RF), Decision Tree (DT), K-nearest Neighbors (k-NN), Support Vector Machine (SVM), and Naïve Bayes (NB). DT is the successful ML classifier with the highest accuracy performed in the Intelligent CSPF System; this leads to the development of a smart CSPF culture where successful business experiences, i.e. models, will be exchanged among the community to get a collective benefit in terms of revenue.

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Section: Literature Reviewmentioning

confidence: 99%

Intelligent Control Shed Poultry Farm System Incorporating With Machine Learning

Ali,

Imran,

Baig

et al. 2024

IEEE Access

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“…The paper [14] S Shafiudin, N Kholis. designed Monitoring System and heat calculating on PID based in this work, the second-hand in PID calculating is considered to detect stand via ARX (Auto Regressive exogenous) MATLAB, which is dynamic/non-linear to find geometric classical and PID constants price that is apt to system.…”

Section: Literature Reviewmentioning

confidence: 99%

Design and Implementation Automated Egg Incubator

Ate

2021

FJES

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Artificial egg incubators are devices used for incubating and hatching eggs through preservation the required conditions for fetus development inside the egg .Generally this paper aims to design and implement an egg incubator system in order to provide adequate conditions for egg hatching and to meet the growing market needs for locally grown white meat at reasonable prices, This system use software program for controlling in the temperature and humidity using the proteus program and Arduino ide. The system of this incubator is based on two Arduino boards one of which is Arduino nano reads the temperature and humidity values inside the incubator chamber through sensor dht11 and it is displayed 16x2 display screen When this value is more than the maximum or less, then the bell give sound to be an indication of the exceeded permissible values and the controller processes the increase in the case of temperature by operating fans to expel and distribute the air. However, in the case of low temperature or humidity, the heater operates and pushes an amount of hot and steam into the inside incubator that make temperature and humidity high, it controls the motor that turns the eggs to change their position in order to prevent the embryo from sticking to the shell and. the electronic lock that makes the incubator door limited to the person responsible. The research would be a user-friendly product as it is manufactured in a limited and small size and can be transported to other locations with minimal user handling.

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“…In the research [9], [10], [11], using different controllers which is PID controllers. Those Controllers work properly, but different types of controllers are needed.…”

Section: Introductionmentioning

confidence: 99%

Egg Incubator Temperature and Humidity Control Using Fuzzy Logic Controller

Maulana¹,

Fathurrohman²,

Wibisono³

2023

J. RESTI (Rekayasa Sist. Teknol. Inf.)

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Controlling room temperature and humidity in egg incubator systems is a process that is widely used in the farm. A good temperature and humidity for standard egg hatching is between 35℃ – 40℃, with humidity in the machine ranging from 50%-60%. The main problems of our research is to find the robustness of the fuzzy logic controller, using the proper parameter. Because while the particular parameter is applicable for one case, but after using several times, the controller lost its robustness. Therefore, this study aims to create a system to control the temperature and humidity of the egg incubator with fuzzy control using the Sugeno methods. In order to get the input and output values, namely by connecting the DHT22 sensor to measure temperature and humidity to be processed into the microcontroller, the value obtained from the sensor will then be processed. The use of fuzzy control is used to make several stages, namely fuzzification, rule, and defuzzification which after processing will be used as output weights for the actuators used. In order to get the robust parameter, test was carried out 5 times with a test time of 18 minutes to get a stable value from the tool. By applying this, it can be concluded whether the system is reliable during different situation. The result shows that the average time for the system to get a stable humidity is 302 second. On the other hand, the average time for the system to get stable temperature is 342 second. The Mean Squared error for temperature is 1,715, while the Mean Squared Error for Humidity is 5,294. It can be concluded that the system controlled by fuzzy controller is robust, has a fast response and reliable.

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Monitoring System and Temperature Controlling on PID Based Poultry Hatching Incubator

Cited by 8 publications

References 0 publications

Intelligent Control Shed Poultry Farm System Incorporating With Machine Learning

Intelligent Control Shed Poultry Farm System Incorporating With Machine Learning

Design and Implementation Automated Egg Incubator

Egg Incubator Temperature and Humidity Control Using Fuzzy Logic Controller

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