Wireless indoor tracking of livestock for behavioral analysis

Asikainen, Mikko; Haataja, Keijo; Toivanen, Pekka

doi:10.1109/iwcmc.2013.6583835

Cited by 17 publications

(5 citation statements)

References 7 publications

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“…In the field of agriculture, IoT devices are deployed in an open environment due to which harsh environmental conditions may cause communication failure and the humiliation of deployed sensors. Therefore, it is important to ensure the physical safety of deployed IoT devices/sensors to protect them from severe climate conditions [146].…”

Section: Reliabilitymentioning

confidence: 99%

Role of IoT Technology in Agriculture: A Systematic Literature Review

et al. 2020

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The growing demand for food in terms of quality and quantity has increased the need for industrialization and intensification in the agriculture field. Internet of Things (IoT) is a highly promising technology that is offering many innovative solutions to modernize the agriculture sector. Research institutions and scientific groups are continuously working to deliver solutions and products using IoT to address different domains of agriculture. This paper presents a systematic literature review (SLR) by conducting a survey of IoT technologies and their current utilization in different application domains of the agriculture sector. The underlying SLR has been compiled by reviewing research articles published in well-reputed venues between 2006 and 2019. A total of 67 papers were carefully selected through a systematic process and classified accordingly. The primary objective of this systematic study is the collection of all relevant research on IoT agricultural applications, sensors/devices, communication protocols, and network types. Furthermore, it also discusses the main issues and challenges that are being investigated in the field of agriculture. Moreover, an IoT agriculture framework has been presented that contextualizes the representation of a wide range of current solutions in the field of agriculture. Similarly, country policies for IoT-based agriculture have also been presented. Lastly, open issues and challenges have been presented to provide the researchers promising future directions in the domain of IoT agriculture.

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Section: Reliabilitymentioning

confidence: 99%

Role of IoT Technology in Agriculture: A Systematic Literature Review

et al. 2020

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“…End devices works consistently for a long period by depending on inadequate batteries power resources. So, a suitable programming tools and less power potential is necessary because in case of any program failure instantly battery replacement is complicated especially in a large scale open field [144].…”

Section: A Hardware Challengesmentioning

confidence: 99%

A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming

et al. 2019

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Internet of things (IoT) is a promising technology which provides efficient and reliable solutions towards the modernization of several domains. IoT based solutions are being developed to automatically maintain and monitor agricultural farms with minimal human involvement. The article presents many aspects of technologies involved in the domain of IoT in agriculture. It explains the major components of IoT based smart farming. A rigorous discussion on network technologies used in IoT based agriculture has been presented, that involves network architecture and layers, network topologies used, and protocols. Furthermore, the connection of IoT based agriculture systems with relevant technologies including cloud computing, big data storage and analytics has also been presented. In addition, security issues in IoT agriculture have been highlighted. A list of smart phone based and sensor based applications developed for different aspects of farm management has also been presented. Lastly, the regulations and policies made by several countries to standardize IoT based agriculture have been presented along with few available success stories. In the end, some open research issues and challenges in IoT agriculture field have been presented.

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“…Relatively little research has been carried out on indoor positioning. Asikainen M et al proposed a specific metric vector iterative positioning algorithm that took into account the limitations of WSN nodes in terms of computational power and energy usage, and achieved a positioning accuracy of 3 m with a lower communication overhead [2]. M. Hierold et al implemented the encounter detection and simultaneous localization of bats in their natural habitat with a low-weight wireless sensor node [3].…”

Section: Introductionmentioning

confidence: 99%

“…The dataset has a total of 8387 goats and consists of 11 goats in their natural grazing state. (2) We propose a goat target detection algorithm consisting of ShallowSE and an improved Custom_YOLO based on YOLOv4. We first added the attention mechanism to enhance the feature extraction capability of the module.…”

Section: Introductionmentioning

confidence: 99%

Detection and Localization of Albas Velvet Goats Based on YOLOv4

Guo,

Wang,

Han

et al. 2023

Animals

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In order to achieve goat localization to help prevent goats from wandering, we proposed an efficient target localization method based on machine vision. Albas velvet goats from a farm in Ertok Banner, Ordos City, Inner Mongolia Autonomous Region, China, were the main objects of study. First, we proposed detecting the goats using a shallow convolutional neural network, ShallowSE, with the channel attention mechanism SENet, the GeLU activation function and layer normalization. Second, we designed three fully connected coordinate regression network models to predict the spatial coordinates of the goats. Finally, the target detection algorithm and the coordinate regression algorithm were combined to localize the flock. We experimentally confirmed the proposed method using our dataset. The proposed algorithm obtained a good detection accuracy and successful localization rate compared to other popular algorithms. The overall number of parameters in the target detection algorithm model was only 4.5 M. The average detection accuracy reached 95.89% and the detection time was only 8.5 ms. The average localization error of the group localization algorithm was only 0.94 m and the localization time was 0.21 s. In conclusion, the method achieved fast and accurate localization, which helped to rationalize the use of grassland resources and to promote the sustainable development of rangelands.

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Wireless indoor tracking of livestock for behavioral analysis

Cited by 17 publications

References 7 publications

Role of IoT Technology in Agriculture: A Systematic Literature Review

Role of IoT Technology in Agriculture: A Systematic Literature Review

A Survey on the Role of IoT in Agriculture for the Implementation of Smart Farming

Detection and Localization of Albas Velvet Goats Based on YOLOv4

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