Data Conceptual Model for Smart Poultry Farm Management System

Bumanis, Nikolajs; Arhipova, Irina; Paura, Līga; Vītols, Gatis; Jankovska, Liga

doi:10.1016/j.procs.2022.01.249

Cited by 12 publications

(10 citation statements)

References 24 publications

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“…A incorporação de tecnologias digitais permite manter registros zootécnicos, monitorar variáveis ambientais, de manejo e desempenho como balanço da emissão de gases, temperatura e umidade, produção de esterco, ganho de peso, produção, consumo, por exemplo. Esse monitoramento permite elaborar análises em tempo real (BUMANIS et al, 2022), assim como formulação de dietas precisas, minimizando a contaminação ambiental e garantindo a gestão racional dos processos (BAILEY, 2020).…”

Section: Criação Agroecológica De Avesunclassified

Criação Animal Agroecológica

Filho

Agudelo²,

Pereira³

et al. 2023

Rev. Bras. de Agroecologia

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Os sistemas agrícolas convencionais têm promovido desmatamento, emissão de gases de efeito estufa, perda da fertilidade de solos, dependência de insumos com consequente aumento de custos, o êxodo e a masculinização do meio rural. A alardeada produtividade tem servido mais para satisfazer os interesses do mercado, do que para atender às necessidades alimentares das pessoas. Os sistemas agrícolas que adotam práticas agroecológicas favorecem a provisão de serviços ecossistêmicos e alimentos de qualidade. A agroecologia é uma ciência multi, inter e transdisciplinar, capaz de sustentar sistemas agrícolas, incluindo a criação animal, reduzindo impactos ambientais, com produção de alimentos de qualidade, e rentabilidade econômica. Essa revisão objetiva contextualizar os desafios para a criação animal agroecológica e suas possibilidades de integração com a produção vegetal para equilibrar diversos benefícios nos âmbitos ambiental, sanitário e econômico.

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Section: Criação Agroecológica De Avesunclassified

Criação Animal Agroecológica

Filho

Agudelo²,

Pereira³

et al. 2023

Rev. Bras. de Agroecologia

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“…A powerful chicken house control software package [ 5 ] was designed to meet a variety of needs, consisting of a variety of control application modules, which can flexibly regulate the chicken house environment; a wireless sensor detection device for rabbit farm odor was developed [ 6 ], which can be used not only to monitor the breeding environment but also to analyze the health status of livestock and poultry-based on odor, health status, etc. To reduce the occurrence of diseases such as foot-and-mouth disease in rabbits, Bumanis et al [ 7 ] prevented the occurrence of diseases by installing wireless sensor networks in their poultry houses to analyze and compare the environmental parameters collected in real-time. In the literature [ 8 ], in response to the problem that most current greenhouse monitoring system solutions are limited to small-area temperature control system applications and do not apply their routing functions to extend to large-area applications, a solution design of wireless greenhouse monitoring system based on multi-level routing is proposed to solve the problem of monitoring large-area greenhouses for agricultural use.…”

Section: Related Workmentioning

confidence: 99%

Network Architecture for Intelligent Identification of Faults in Rabbit Farm Environment Monitoring Based on a Biological Neural Network Model

Zhang

Qian

2022

Computational Intelligence and Neuroscience

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Currently, livestock and poultry farming is gradually developing towards modernization and scale, and closed livestock and poultry farms are widely used for poultry feeding management, but at the same time, the farming risks of large-scale farms are increasing. In this paper, based on the study of wireless sensor networks and biological neural network models, the environmental factors that mainly affect the growth of domestic rabbits are analyzed as an example, and the technology is used to design and implement an environmental monitoring system for modern farms. The design of the system is divided into three main parts: hardware design of each node, software design, and upper computer monitoring software design. The hardware part of the system uses coordinator nodes, router nodes, sensor nodes, and control nodes to form a wireless sensor network in the farm, carries out the hardware circuit design of each node, and based on the protocol stack, designs the software program of each node to realize the collection, transmission, and regulation of environmental information in the farm. In the upper computer part, the design and development of the upper computer monitoring software interface are used to complete the real-time display of environmental data, historical query, database storage, and curve drawing, and to design a remote client data query system based on the architecture to realize the query of environmental data of the farm by remote users and to carry out monitoring fault intelligent identification alarm. At the same time, the paper investigates the optimal deployment of wireless sensor network nodes and searches for the optimal location of sensor nodes through an improved biological neural network algorithm to maximize the network coverage and reduce the coverage of blind areas, and conducts simulation experiments with the coverage rate of a rabbit farm as the optimization target.

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“…Akhund, et al melakukan tujuan untuk membuat sistem peternakan unggas pintar berbasis IoT, yang dimana power supply dikembangkan dengan menggunakan energi terbarukan terutama dengan energi surya dan nanohidro [5]. Peneliti Bumanis, et al melakukan cyber-physical model diusulkan sebagai dasar untuk pengembangan sistem manajemen peternakan unggas pintar yang dapat disesuaikan dengan kebutuhan produksi tertentu, beberapa sensor CO2 (karbon dioksida) dan NH3 (amonia) dipasang untuk mengumpulkan data [6]. Corkery, et al memberikan ikhtisar pemantauan dan teknologi sensor kinerja dala m produksi unggas.…”

Section: Pendahuluanunclassified

“…Fungsinya untuk mengurangi "over-fitting" data dan meminimalkan efek generalisasi yang buruk. Risiko empiris didefinisikan sebagai (6).…”

Section: Algoritma Prediksiunclassified

Pendekatan Algoritma Tree dalam Prediksi Populasi pada Smart Poultry

Nugroho¹,

Ramadhan²,

Wibowo³

et al. 2022

bits

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Intelligent systems for monitoring poultry in kennels are experiencing an increasing trend in several studies. Monitoring poultry is very important in the cage so that you can find out the chickens' condition and environment in the cage. Conditions that can be monitored include the weight of the chickens, whether or not there is enough water in a day, CO2 levels in the cages, air temperature, and humidity in the cages. Several studies have been conducted studies on monitoring poultry cages using IoT-based sensors. However, people have yet to predict the poultry population for tomorrow. So this study aims to predict the number of poultry populations in kennels based on related parameters. The prediction method used in this research is a decision tree and Support Vector Machine (SVM) to see which prediction method is better. The results evaluation techniques used in this study are Mean Square Error (MSE), Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and R2. The experimental results show that using the decision tree method, and the results are MSE 61987.202, RMSE 248.972, MAE 85.086, and R2 0.969. Overall the results of the decision tree method are superior to SVM.

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Data Conceptual Model for Smart Poultry Farm Management System

Cited by 12 publications

References 24 publications

Criação Animal Agroecológica

Criação Animal Agroecológica

Network Architecture for Intelligent Identification of Faults in Rabbit Farm Environment Monitoring Based on a Biological Neural Network Model

Pendekatan Algoritma Tree dalam Prediksi Populasi pada Smart Poultry

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