A Smart Sensing System of Water Quality and Intake Monitoring for Livestock and Wild Animals

Tang, Wei; Biglari, Amin; Ebarb, Ryan; Pickett, Tee; Smallidge, Samuel; Ward, Monika A.

doi:10.3390/s21082885

Cited by 13 publications

(7 citation statements)

References 43 publications

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“…The main use of these sensors is for object detection and image classification algorithms. Among the sources in this review, the main application in which an RGB camera was implemented included autonomous vehicles for pedestrian and sign detection, security cameras for intruder detection, facial recognition, and employee safety monitoring, and drones for search and rescue, domestic animal monitoring [ 48 , 49 ], agricultural crops, and wildlife observation [ 50 ].…”

Section: Sensorsmentioning

confidence: 99%

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

Biglari

Tang

2023

Sensors

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Machine learning is an expanding field with an ever-increasing role in everyday life, with its utility in the industrial, agricultural, and medical sectors being undeniable. Recently, this utility has come in the form of machine learning implementation on embedded system devices. While there have been steady advances in the performance, memory, and power consumption of embedded devices, most machine learning algorithms still have a very high power consumption and computational demand, making the implementation of embedded machine learning somewhat difficult. However, different devices can be implemented for different applications based on their overall processing power and performance. This paper presents an overview of several different implementations of machine learning on embedded systems divided by their specific device, application, specific machine learning algorithm, and sensors. We will mainly focus on NVIDIA Jetson and Raspberry Pi devices with a few different less utilized embedded computers, as well as which of these devices were more commonly used for specific applications in different fields. We will also briefly analyze the specific ML models most commonly implemented on the devices and the specific sensors that were used to gather input from the field. All of the papers included in this review were selected using Google Scholar and published papers in the IEEExplore database. The selection criterion for these papers was the usage of embedded computing systems in either a theoretical study or practical implementation of machine learning models. The papers needed to have provided either one or, preferably, all of the following results in their studies—the overall accuracy of the models on the system, the overall power consumption of the embedded machine learning system, and the inference time of their models on the embedded system. Embedded machine learning is experiencing an explosion in both scale and scope, both due to advances in system performance and machine learning models, as well as greater affordability and accessibility of both. Improvements are noted in quality, power usage, and effectiveness.

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Section: Sensorsmentioning

confidence: 99%

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

Biglari

Tang

2023

Sensors

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“…Several other functions such as obstacle detection and avoidance were implemented and presented in the paper with experimental results verifying their effectiveness. In their paper, Tang et al [18] presented a water intake monitoring system for livestock based on Arduino development boards. The proposed system can monitor the water quality but also track and identify each animal in order to conduct further animal water consumption studies.…”

Section: Specific Water-quality Monitoring Applicationsmentioning

confidence: 99%

Low-Cost Internet-of-Things Water-Quality Monitoring System for Rural Areas

Bogdan

Paliuc

Crişan-Vida

et al. 2023

Sensors

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Water is a vital source for life and natural environments. This is the reason why water sources should be constantly monitored in order to detect any pollutants that might jeopardize the quality of water. This paper presents a low-cost internet-of-things system that is capable of measuring and reporting the quality of different water sources. It comprises the following components: Arduino UNO board, Bluetooth module BT04, temperature sensor DS18B20, pH sensor—SEN0161, TDS sensor—SEN0244, turbidity sensor—SKU SEN0189. The system will be controlled and managed from a mobile application, which will monitor the actual status of water sources. We propose to monitor and evaluate the quality of water from five different water sources in a rural settlement. The results show that most of the water sources we have monitored are proper for consumption, with a single exception where the TDS values are not within proper limits, as they outperform the maximum accepted value of 500 ppm.

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“…Insect Bee [67], [83], [105] Insect [50] Weevil [77] Bird Bird [32] Broiler chicken [53] Chicken [47], [73] Goose [69] Hen [68] Hummingbird [19] Laying hen [33], [71] Pigeon [49] Poultry [52] Wild bird [30], [99] Mammal Bison [107] Buffalo [95] Cattle [89], [36], [51], [94], [38], [3], [57], [43], [74], [91], [35], [76], [52], [90], [8], [18], [80], [7], [88], [42], [87], [85], [62], [58] Domestic animal [18], [61], [82] Elephant [63] Goat [57], …”

Section: Type Animals Referencesmentioning

confidence: 99%

RFID Technology for Animal Tracking: A Survey

Pereira,

Araújo,

Silva

et al. 2023

IEEE J. Radio Freq. Identif.

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The application of animal tracking holds significant importance across diverse economic domains, encompassing sectors including livestock husbandry, agricultural practices, and the conservation of wildlife populations. It aims to track and understand animal behavior, movement patterns, and health status. The predominant use of RFID technology is observed within the domains of logistics, localization, and the tracking of goods. Notably, the application of this technology in the field of animal tracking has experienced a significant surge in popularity in recent years. This paper conducts a systematic literature review focused on understanding how RFID technology is being applied in the field of animal tracking. We have conducted a stateof-the-art research regarding animal tracking solutions in the scientific literature and patents. We have analyzed these solutions targeting which animals are being tracked, which problems are addressed, operating frequency, and whether other technologies are combined with RFID for animal tracking purposes. Among the categories of problems addressed, livestock management emerged as the main area, followed by animal tracking and traceability. Mammals, especially cattle, are the most common type of animal monitored. Considering RFID technology, passive UHF tags appeared more often. Moreover, many works also employed cameras and GPS together with RFID. Finally, this work can significantly contribute to this field by systematically presenting a state-of-the-art application of RFID for animal tracking.

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A Smart Sensing System of Water Quality and Intake Monitoring for Livestock and Wild Animals

Cited by 13 publications

References 43 publications

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme

Low-Cost Internet-of-Things Water-Quality Monitoring System for Rural Areas

RFID Technology for Animal Tracking: A Survey

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