Application of Internet of Things in Smart Farm Watering System

Hsu, Wei-Ling; Wang, Wenkai; Fan, Wen-Hung; Shiau, Yan-Chyuan; Yang, Ming-Ling; Lopez, Dylan Josh Domingo

doi:10.18494/sam.2021.3164

Cited by 21 publications

(15 citation statements)

References 12 publications

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“…Vento application is a tool for controlling the temperature in the cage. Many authors use Arduino controllers as well as the Internet of Things system [15,16,17]. In this study, the authors monitored the temperature regulation of the cage in the chicken farming process using the Vento application [18,19,20].…”

Section: Methodsmentioning

confidence: 99%

Monitoring chicken livestock process using Vento Application at a farm

Andika

Muwardi

Yunita

et al. 2022

JIAE

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Regular temperature monitoring in the poultry industry is necessary to produce high-quality products. However, the traditional methods of these activities are still massively applied. Therefore, a modest process on poultry farms requires temperature monitoring and control. Vento is a climate controller developed by Big Dutchman that is easy to understand and user-friendly. Further, the module provides a handy installation and operation on the farm. Therefore, Vento is suitable to use in hot climates area. Vento performs temperature control by reading the input collected from the DOL114 sensor and the DOL12 sensor. The DOL114 sensor performs temperature and humidity detection in the front area of the cage. At the same time, the DOL12 was installed to obtain temperature information behind the cage. Both functions as input to operate the Heater, but only the DOL114 sensor is used to activate the cooling pad. The temperature value gathered by the DOL114 sensor and the DOL12 sensor will be processed by Vento, resulting in average temperature data. Thus, the data obtained from the sensor will be transferred to the Vento system to operate the output in the form of an exhaust fan. The exhaust fan serves as a tool that removes air and ammonia in the cage so that the cage temperature complies with a predetermined setpoint.

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Section: Methodsmentioning

confidence: 99%

Monitoring chicken livestock process using Vento Application at a farm

Andika

Muwardi

Yunita

et al. 2022

JIAE

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“…To understand the behavioral patterns of building occupants, various types of sensors are often used to collect environmental data in smart buildings. (12,13) In this study, a smart home environment control system was developed. The methods used are presented as follows:…”

Section: Methodsmentioning

confidence: 99%

Development of Smart Residential Environment Control System

Hsu¹,

Tsai²,

Yang³

et al. 2021

Sensors and Materials

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Today's world places increasing emphasis on comfort within living spaces. The growth in awareness of energy conservation and environmental preservation has gradually contributed to the widespread use of smart home control systems. In this study, data related to human comfort conditions were compiled, and the control of smart devices in home environments was explored. Specifically, information communication technology and the Internet of Things (IoT) were combined to create a smart control system that creates a comfortable, energy-conserving home environment through the concurrent monitoring of both indoor and outdoor environmental data. If the outdoor indicators are ideal (denoting a comfortable environment) but the indoor indicators are not ideal (denoting an uncomfortable environment), the system opens a window to allow the incorporation of favorable outdoor environmental factors into the indoor environment. When the outdoor environment is uncomfortable, the window is closed to prevent the infiltration of unfavorable outdoor environmental factors. When the indoor indicators are nonideal, various types of equipment are activated according to the detection values. For example, when the humidity level is overly high, the dehumidifier is turned on. When the concentration of particulate matter with a diameter of < 2.5 µm is overly high, the air purifier is turned on. When the temperature is too high or low, the air conditioner is turned on, and when the CO 2 concentration is too high, the fan is turned on for ventilation. The values detected by each sensor and the operating conditions of each device are also displayed on the screen for the reference of the user, who can adjust the threshold values to their preferences.

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“…Currently, the most common methods of AI are neural networks, deep learning, and machine learning [33]. In farming, IoT is used to detect and collect data from fields [34][35][36][37][38]. In addition, after data collection by IoT, Quiroz et al [39] applied a convolutional neural network (CNN) to classify the crop.…”

Section: Introductionmentioning

confidence: 99%

“…Rezk et al [40], Rodríguez et al [41], and Kuo et al [42] predicted or analyzed the crop yield by using wrapper partial decision tree algorithm (WPART), extreme gradient boosting (XGBoost), and support vector machine (SVM) as well as CNN models, respectively. Hsu et al [37] not only built a model for predicting yields but also developed a subsystem for detecting unauthorized entry to crop fields.…”

Section: Introductionmentioning

confidence: 99%

Artificial Intelligence in Internet of Things System for Predicting Water Quality in Aquaculture Fishponds

Yang¹,

Liao²,

Chou³

2023

Computer Systems Science and Engineering

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Aquaculture has long been a critical economic sector in Taiwan. Since a key factor in aquaculture production efficiency is water quality, an effective means of monitoring the dissolved oxygen content (DOC) of aquaculture water is essential. This study developed an internet of things system for monitoring DOC by collecting essential data related to water quality. Artificial intelligence technology was used to construct a water quality prediction model for use in a complete system for managing water quality. Since aquaculture water quality depends on a continuous interaction among multiple factors, and the current state is correlated with the previous state, a model with time series is required. Therefore, this study used recurrent neural networks (RNNs) with sequential characteristics. Commonly used RNNs such as long short-term memory model and gated recurrent unit (GRU) model have a memory function that appropriately retains previous results for use in processing current results. To construct a suitable RNN model, this study used Taguchi method to optimize hyperparameters (including hidden layer neuron count, iteration count, batch size, learning rate, and dropout ratio). Additionally, optimization performance was also compared between 5-layer and 7-layer network architectures. The experimental results revealed that the 7-layer GRU was more suitable for the application considered in this study. The values obtained in tests of prediction performance were mean absolute percentage error of 3.7134%, root mean square error of 0.0638, and R-value of 0.9984. Therefore, the water quality management system developed in this study can quickly provide practitioners with highly accurate data, which is essential for a timely response to water quality issues. This study was performed in collaboration with the Taiwan Industrial Technology Research Institute and a local fishery company. Practical application of the system by the fishery company confirmed that the monitoring system is effective in improving the survival rate of farmed fish by providing data needed to maintain DOC higher than the standard value.

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Application of Internet of Things in Smart Farm Watering System

Cited by 21 publications

References 12 publications

Monitoring chicken livestock process using Vento Application at a farm

Monitoring chicken livestock process using Vento Application at a farm

Development of Smart Residential Environment Control System

Artificial Intelligence in Internet of Things System for Predicting Water Quality in Aquaculture Fishponds

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