Optimal Placement of Laying Hen House Temperature Sensors Based on Genetic Algorithm

Hu, Xiaoyue; Gao, Liai; Hou, Limin; Li, Lihua; Er, Mengwei

doi:10.1109/access.2022.3141090

Cited by 6 publications

(5 citation statements)

References 19 publications

(17 reference statements)

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“…Wang et al combined the IoT, cloud services, and differential time series models to establish a greenhouse environment monitoring and temperature prediction system based on the WeChat platform [55]. Hu et al combined the distributed IoT, sensors, and cloud computing technology to establish an intelligent monitoring system for chicken coop facility breeding considering the requirements of heterogeneous data integration [56]. Prasetyo et al developed a geographical space-based information system for prompt detection, monitoring, and evaluation of plant diseases [57].…”

Section: Aei Modelling Methodsmentioning

confidence: 99%

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A review on basic theory and technology of agricultural energy internet

Zhang,

Fu,

Xue

et al. 2023

IET Renewable Power Gen

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In the context of modern agricultural production mode and domestic energy consumption, profound changes have taken place in agricultural and rural energy consumption, resulting in the demand for new technology development in various sectors of source, network, and load in rural energy systems. Agricultural energy internet (AEI) has promoted the development of renewable energy and agricultural electrification in villages. The construction of the AEI is crucial for achieving the synergistic development of agriculture, energy, and environment. The basic theory and key technologies of AEI are investigated and the prospects for the direction of agricultural energy technology are conducted. The research investigation shows that the AEI framework proposed by China Agricultural University is of great significance for realizing agricultural electrification and reducing agricultural carbon emissions. The article describes in detail the key data and characteristics of agricultural electrification loads. Digital twin technology and virtual power plant technology will be crucial to the construction of advanced AEI.

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Section: Aei Modelling Methodsmentioning

confidence: 99%

“…Hu et al. combined the distributed IoT, sensors, and cloud computing technology to establish an intelligent monitoring system for chicken coop facility breeding considering the requirements of heterogeneous data integration [56]. Prasetyo et al.…”

Section: Aei Modelling Methodsmentioning

confidence: 99%

A review on basic theory and technology of agricultural energy internet

Zhang,

Fu,

Xue

et al. 2023

IET Renewable Power Gen

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“…137 The sensors should be placed at the optimum places to cover the whole poultry arena and reduce the installation cost. Hu et al 138 have proposed a layout optimization method with the help of genetic algorithm for temperature sensors. However, placing multiple sensors at optimum locations remains an open area for researchers.…”

Section: Proposed Poultry Health Management Frameworkmentioning

confidence: 99%

A Literature Review on the Role of Internet of Things, Computer Vision, and Sound Analysis in a Smart Poultry Farm

Goyal,

Yadav,

Mukherjee

2024

ACS Agric. Sci. Technol.

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The rapid growth of human population and frequent changes in environmental conditions pose challenges in providing nutrient-rich food to the current population. The demand for poultry products is increasing exponentially as they are a good source of low-cost proteins. Ensuring the well-being of birds and delivering nutritious poultry products is essential for both current and future food security needs and sustainable agriculture. This article is motivated by the potential of emerging digital technologies in agriculture to provide innovative solutions to challenges faced by farmers, particularly in poultry farming. It aims to shed light on various issues encountered by farmers like diseases on poultry farms and offers valuable insights for researchers seeking to address these challenges using the advancements in digital technologies. Precision farming in poultry involves leveraging technologies such as the internet of things, artificial intelligence, and edge computing to enhance animal health management. A smart poultry farm maintains the farm environment and detects diseases at early stage in the chickens. The farm resources are utilized optimally in the smart poultry farm. This research work provides a systematic literature review of intelligent systems designed for (i) poultry birds' health and welfare management using technologies like the internet of things (IoT), artificial intelligence, cloud computing, and edge computing and (ii) early disease detection, estimation of weight, and feeding behavior of birds with the use of computer vision and vocalization analysis. This article also explores various sensors employed in the development of IoT infrastructure. It has been demonstrated in the literature that modern digital technologies automate the management operations of poultry farms. As a result, the farmer's income increases, and high-quality food products are available to people at low cost.

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“…In the context of genetic algorithm-based sensor placement, previous research has explored various strategies. For example, Hu et al [14] used a dual-structure coding genetic algorithm to optimize sensor combinations in chicken houses, using a grey correlation degree-based objective function. Ajani et al [15] introduced a genetic programming-based method for optimal sensor placement in greenhouse environmental monitoring and control.…”

Section: Introductionmentioning

confidence: 99%

Three-Way k-Means Model: Dynamic Optimal Sensor Placement for Efficient Environment Monitoring in Pig House

Li,

et al. 2024

Animals

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Sensors were of paramount importance in the context of poultry and livestock farming, serving as essential tools for monitoring a variety of production management parameters. The effective surveillance and optimal control of the swine facility environment critically depend on the implementation of a robust strategy for situating the optimal number of sensors in precisely the right locations. This study presents a dynamic sensor placement approach for pigsties using the three-way k-means algorithm. The method involves determining candidate sensor combinations through the application of the k-means algorithm and a re-clustering strategy. The optimal sensor locations were then identified using the Joint Entropy-Based Method (JEBM). This approach adjusts sensor positions based on different seasons (summer and winter) to effectively monitor the overall environment of the pigsty. We employ two clustering models, one based on particle swarm optimization and the other on genetic algorithms, along with a re-clustering strategy to identify candidate sensor combinations. The joint entropy-based method (JEBM) helps select the optimal sensor placement. Fused data from the optimal sensor layout undergo a fuzzy fusion process, reducing errors compared to direct averaging. The results show varying sensor needs across seasons, and dynamic placement enhances pigsty environment monitoring. Our approach reduced the number of sensors from 30 to 5 (in summer) and 6 (in winter). The optimal sensor positions for both seasons were integrated. Comparing the selected sensor layout to the average of all sensor readings representing the overall pigsty environment, the RMSE were 0.227–0.294 and the MAPE were 0.172–0.228, respectively, demonstrating the effectiveness of the sensor layout.

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Optimal Placement of Laying Hen House Temperature Sensors Based on Genetic Algorithm

Cited by 6 publications

References 19 publications

A review on basic theory and technology of agricultural energy internet

A review on basic theory and technology of agricultural energy internet

A Literature Review on the Role of Internet of Things, Computer Vision, and Sound Analysis in a Smart Poultry Farm

Three-Way k-Means Model: Dynamic Optimal Sensor Placement for Efficient Environment Monitoring in Pig House

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