MICROORGANISMS AS INDICATORS OF SOIL QUALITY

2021 ◽  
pp. 45-50
Author(s):  
Kosumov R.S. ◽  
Okazova Z.P.
Keyword(s):  
Environmental Monitoring ◽  
First Generation ◽  
Low Cost ◽  
Toxicity Tests ◽  
Monitoring Methods ◽  
Unicellular Algae ◽  
Integrated Devices ◽  
Eukaryotic Organisms ◽  
Biological Methods ◽  
State Of The Environment

The first bioassays for environmental monitoring were based on multicellular eukaryotic organisms, in particular fish and mammals. Because they were relatively expensive, time consuming and difficult, there was a need for alternative biological monitoring methods. It became necessary to develop and standardize toxicity tests based on prokaryotic (bacteria) or eukaryotic (protozoa, unicellular algae, yeast) microorganisms instead of higher organisms, which made it possible to quickly and inexpensively screen environmental samples for toxic and genotoxic effects. The first generation of bioassays was based on a variety of naturally sensitive microbes, while the second generation includes genetically modified microorganisms to achieve greater sensitivity and / or specificity. The next step forward was the combination of microbial cells, or parts of cells, with physicochemical detection elements, forming new integrated devices called "biosensors". The purpose of the research is to study the possibility of using microorganisms in bioindication of environmental pollution. The use of biological methods in environmental monitoring is essential to complement chemical analyzes with information on actual toxicity. Microorganisms are widely used as test objects for analyzes due to the simplicity and low cost of their cultivation. The use of microorganisms for the assessment of general toxicity or the detection of specific compounds is an important source of information on the state of the environment. Their use will significantly expand the range of environmental studies.

scholarly journals An Automated Light Trap to Monitor Moths (Lepidoptera) Using Computer Vision-Based Tracking and Deep Learning

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 343
Author(s):  
Kim Bjerge ◽  
Jakob Bonde Nielsen ◽  
Martin Videbæk Sepstrup ◽  
Flemming Helsing-Nielsen ◽  
Toke Thomas Høye
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Vision System ◽  
Low Cost ◽  
Light Trap ◽  
Automatic Monitoring ◽  
Light Sources ◽  
Monitoring Methods ◽  
Computer Vision System ◽  
Substantial Investment

Insect monitoring methods are typically very time-consuming and involve substantial investment in species identification following manual trapping in the field. Insect traps are often only serviced weekly, resulting in low temporal resolution of the monitoring data, which hampers the ecological interpretation. This paper presents a portable computer vision system capable of attracting and detecting live insects. More specifically, the paper proposes detection and classification of species by recording images of live individuals attracted to a light trap. An Automated Moth Trap (AMT) with multiple light sources and a camera was designed to attract and monitor live insects during twilight and night hours. A computer vision algorithm referred to as Moth Classification and Counting (MCC), based on deep learning analysis of the captured images, tracked and counted the number of insects and identified moth species. Observations over 48 nights resulted in the capture of more than 250,000 images with an average of 5675 images per night. A customized convolutional neural network was trained on 2000 labeled images of live moths represented by eight different classes, achieving a high validation F1-score of 0.93. The algorithm measured an average classification and tracking F1-score of 0.71 and a tracking detection rate of 0.79. Overall, the proposed computer vision system and algorithm showed promising results as a low-cost solution for non-destructive and automatic monitoring of moths.

RAP: A Software Framework of Developing Convolutional Neural Networks for Resource-constrained Devices Using Environmental Monitoring as a Case Study

2021 ◽  
Vol 5 (4) ◽  
pp. 1-28
Author(s):  
Chia-Heng Tu ◽  
Qihui Sun ◽  
Hsiao-Hsuan Chang
Keyword(s):  
Neural Networks ◽  
Environmental Monitoring ◽  
Convolutional Neural Networks ◽  
Model Building ◽  
Software Framework ◽  
Training Procedure ◽  
Monitoring Methods ◽  
Resource Constrained ◽  
Resource Constrained Devices ◽  
Constrained Devices

Monitoring environmental conditions is an important application of cyber-physical systems. Typically, the monitoring is to perceive surrounding environments with battery-powered, tiny devices deployed in the field. While deep learning-based methods, especially the convolutional neural networks (CNNs), are promising approaches to enriching the functionalities offered by the tiny devices, they demand more computation and memory resources, which makes these methods difficult to be adopted on such devices. In this article, we develop a software framework, RAP , that permits the construction of the CNN designs by aggregating the existing, lightweight CNN layers, which are able to fit in the limited memory (e.g., several KBs of SRAM) on the resource-constrained devices satisfying application-specific timing constrains. RAP leverages the Python-based neural network framework Chainer to build the CNNs by mounting the C/C++ implementations of the lightweight layers, trains the built CNN models as the ordinary model-training procedure in Chainer, and generates the C version codes of the trained models. The generated programs are compiled into target machine executables for the on-device inferences. With the vigorous development of lightweight CNNs, such as binarized neural networks with binary weights and activations, RAP facilitates the model building process for the resource-constrained devices by allowing them to alter, debug, and evaluate the CNN designs over the C/C++ implementation of the lightweight CNN layers. We have prototyped the RAP framework and built two environmental monitoring applications for protecting endangered species using image- and acoustic-based monitoring methods. Our results show that the built model consumes less than 0.5 KB of SRAM for buffering the runtime data required by the model inference while achieving up to 93% of accuracy for the acoustic monitoring with less than one second of inference time on the TI 16-bit microcontroller platform.

scholarly journals Development of an IoT System for Environmental Monitoring: Software

2021 ◽  
Author(s):  
Benjamin Secker
Keyword(s):  
Environmental Monitoring ◽  
Low Cost ◽  
Proof Of Concept ◽  
Embedded Devices ◽  
Data Loggers ◽  
Environmental Monitoring System ◽  
Use Of The Internet ◽  
High Level ◽  
Monitoring Software ◽  
The Internet Of Things

Use of the Internet of Things (IoT) is poised to be the next big advancement in environmental monitoring. We present the high-level software side of a proof-of-concept that demonstrates an end-to-end environmental monitoring system,<br><div>replacing Greater Wellington Regional Council’s expensive data loggers with low-cost, IoT centric embedded devices, and it’s supporting cloud platform. The proof-of-concept includes a Micropython-based software stack running on an ESP32 microcontroller. The device software includes a built-in webserver that hosts a responsive Web App for configuration of the device. Telemetry data is sent over Vodafone’s NB-IoT network and stored in Azure IoT Central, where it can be visualised and exported.</div><br>While future development is required for a production-ready system, the proof-of-concept justifies the use of modern IoT technologies for environmental monitoring. The open source nature of the project means that the knowledge gained can be re-used and modified to suit the use-cases for other organisations.

A Preliminary Assessment of Low-Cost Bridge Scour Monitoring Methods and Tools

2021 ◽  
Author(s):  
Eftychia Koursari ◽  
Stuart Wallace ◽  
Panagiotis Michalis ◽  
Manousos Valyrakis ◽  
Scott Paton
Keyword(s):  
Climate Change ◽  
Low Cost ◽  
Scour Depth ◽  
Bridge Scour ◽  
Monitoring Methods ◽  
Preliminary Assessment ◽  
Medium Scale ◽  
Scour Monitoring ◽  
Surface Shaping ◽  
Scale Structures

&lt;p&gt;Scour is a major cause of bridge collapse worldwide.&lt;/p&gt;&lt;p&gt;Climate change has resulted in flood events increasing both in frequency and in magnitude. Climate change, together with the current uncertainty about maximum scour depth around structures, make scour and other hydraulic actions some of the most important challenges for engineering going forward.&lt;/p&gt;&lt;p&gt;This study offers a preliminary assessment of bridge scour monitoring methods considering scour as a dynamical earth surface shaping process, and discusses how these methods can be used to improve predictive models for bridge scour depth.&lt;/p&gt;&lt;p&gt;Current methods used to monitor scour are mostly reactive. A vast amount of research has been carried out, aiming towards the implementation of various approaches to assist in the monitoring of scour; however, most methods used are either still reactive, or extremely costly and therefore not practical to be used for small to medium scale structures. This study aims in addressing major challenges faced by establishing a new, innovative framework for the monitoring of scour, while considering relevant approaches in literature. It discusses the development of an innovative, sustainable and low-cost framework, that can be used for small to medium scale structures. This will ensure a proactive response in the event of catastrophic scour occurring, safeguarding infrastructure and the travelling public.&lt;/p&gt;

Organic Agriculture as a Tool to Make Economy Green

2015 ◽  
pp. 196-218 ◽  
Author(s):  
Mykhailo Guz ◽  
Ivan Ivolga
Keyword(s):  
Food Security ◽  
Organic Agriculture ◽  
Food Production ◽  
Low Cost ◽  
Environmental Safety ◽  
The State ◽  
Production Unit ◽  
Traditional Technologies ◽  
State Of The Environment ◽  
Variable Quality

The main questions that trouble ecologists are concentrated on the state of the environment per square unit, while concerns of food security supporters are concentrated on a cost of production unit (which is bigger in comparison with standard for organic agriculture). Economists, in turn, are concerned about the low cost of decision for achievement of food security in comparison with environmental safety. The point, discussed in the chapter, is related to implementation of organic and traditional technologies of farming. It is expedient to estimate the changes of environment per units of production, if there is a set of food production and soils of a variable quality.

Organic Agriculture as a Tool to Make Economy Green

2016 ◽  
pp. 221-245
Author(s):  
Mykhailo Guz ◽  
Ivan Ivolga
Keyword(s):  
Food Security ◽  
Organic Agriculture ◽  
Food Production ◽  
Low Cost ◽  
Environmental Safety ◽  
The State ◽  
Production Unit ◽  
Traditional Technologies ◽  
State Of The Environment ◽  
Variable Quality

The main questions that trouble ecologists are concentrated on the state of the environment per square unit, while concerns of food security supporters are concentrated on a cost of production unit (which is bigger in comparison with standard for organic agriculture). Economists, in turn, are concerned about the low cost of decision for achievement of food security in comparison with environmental safety. The point, discussed in the chapter, is related to implementation of organic and traditional technologies of farming. It is expedient to estimate the changes of environment per units of production, if there is a set of food production and soils of a variable quality.

A Low Cost Pupillometry Approach

2015 ◽  
Vol 6 (4) ◽  
pp. 49-61
Author(s):  
Sergios Petridis ◽  
Theodoros Giannakopoulos ◽  
Constantine D. Spyropoulos
Keyword(s):  
Elderly Population ◽  
Emotional State ◽  
Low Cost ◽  
The Elderly ◽  
Monitoring Methods ◽  
Continuous Increase ◽  
Pupil Center ◽  
Web Camera ◽  
The Face ◽  
Source Of Information

The need for low-cost health monitoring is increasing with the continuous increase of the elderly population. In this context, unobtrusive audiovisual monitoring methods can be of great importance. More particularly, the diameter of the pupil is a valuable source of information, since, apart from pathological cases, it can reveal the emotional state, the fatigue and the ageing. To allow for unobtrusive monitoring to gain acceptance, one should seek for efficient methods of monitoring using common low-cost hardware. This paper describes a method for monitoring pupil sizes using a common, low-cost web camera in real time. The proposed approach detects the face and the eyes area at first stage. Subsequently, optimal iris and sclera location and radius, modeled as ellipses, are found using efficient spatial filtering. As a final step, the pupil center and radius is estimated by optimal filtering within the area of the iris. Experimental results show both the efficiency and the effectiveness of our approach.

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