Monitoring Of Environmental Conditions Research Articles

Experimental Design: Implementation of IoT-based drip irrigation to enhance the productivity of Cilembu sweet potato (Ipomoea batatas) cultivation

Effective irrigation methods are crucial for meeting plant water needs while preventing excessive evaporation, soil dryness, and water shortages. Conventional irrigation methods often result in inefficient water distribution, leading to a reduced agricultural productivity. This study aimed to enhance water use efficiency and improve crop yield by employing Internet of Things (IoT)-based drip irrigation systems. The system regulates water supply and monitor environmental conditions using low-power sensor nodes that measure soil moisture, air temperature, humidity, and light intensity. Field experiments were conducted to ascertain optimal water requirements by adjusting irrigation durations to 15, 30, 60, and 120 minutes and modifying water application based on soil moisture levels below 70%. Irrigation scheduling was performed at 6 am and 6 pm. The implementation of IoT-based drip irrigation in Cilembu sweet potato field resulted in a production yield of 44 tons/ha, marking a 45% productivity compared to conventional irrigation methods. Furthermore, water savings of 65% were achieved through the application of IoT-controlled drip irrigation. These results demonstrate the potential of IoT-based systems in optimizing water use and enhancing agricultural productivity, making it a valuable approach for sustainable farming practices.

PERS: Personalized environment recommendation system based on vital signs

The integration of the Internet of Things (IoT) in healthcare has facilitated real-time monitoring of vital signs and environmental conditions. However, existing systems often lack personalized recommendations that consider the interplay between these factors. This work introduces the Personalized Environment Recommendation System (PERS), which leverages a portable device to continuously collect data on key health metrics, including pulse rate and body temperature, alongside environmental parameters. Utilizing Artificial Neural Networks, PERS analyzes the data to generate tailored health recommendations for users. Experimental results demonstrate an accuracy of 98.7%, highlighting the system’s effectiveness in enhancing patient care and supporting informed health decisions. The findings suggest that PERS can significantly improve health monitoring by providing actionable insights based on individual health profiles and environmental contexts.

Monitoring Perishable Commodities Using Cellular IoT: An Intelligent Real-Time Conditions Tracker Design

Perishable product losses can occur throughout postharvest handling. Proper monitoring of key environmental conditions during this period is essential for predicting quality losses throughout their shelf life. This paper presents the design and testing of a portable and compact datalogger for the real-time monitoring of environmental conditions throughout the food supply chain. The device developed incorporates high-precision sensors to measure temperature, relative humidity, CO2 concentration, luminosity and vibrations, as well as wireless communication capabilities for data transmission, simplifying real-time monitoring over existing multi-component systems while keeping costs affordable. Strategies to optimize power consumption allow a month of battery life, being able to cover entire periods of transport and storage, according to the results of the autonomy test performed on the device. The datalogger uses NB-IoT and relies on other wireless communication protocols if not available to send sensor data to a cloud platform. Comparative testing with commercial dataloggers has been carried out to verify correct device measurements, and field testing has validated successful real-time data transmission along an entire refrigerated transport route. The functionality and autonomy of the proposed device meet the needs of live remote monitoring to help reduce food losses.

IoT-based home control system using NodeMCU and Firebase

Integrating Internet of Things (IoT) technology in home control systems has led to a significant advancement in the control and monitoring of home appliances and environmental conditions. This study presents the development of an IoT-based home control system that uses the NodeMCU ESP8266 microcontroller to manage several home appliances. This system connects to a Firebase real-time database to store or retrieve data in real-time. The user interacts with the IoT system through a mobile application named My Home, developed using Java programming language, and this mobile application is also interfaced with the Firebase cloud server. The developed IoT system allows users to control home appliances such as lights, sockets, fans, and cookers, and it also provides real-time updates of environmental parameters, such as temperature and humidity, which are measured by a DHT11 sensor. In addition, a PIR motion sensor was integrated into the system to enhance the home's security by detecting intrusion. The system's hardware functionality is based on a written Arduino code, which establishes Wi-Fi connectivity to a predefined network, communicates with the Firebase database, handles appliance control and manages sensor data. This IoT-based home control system shows the potential of integrating microcontrollers with cloud services to create a smart, responsive and user-friendly home control platform. The developed IoT system offers a foundation for advancements, thereby making it a valuable contribution to the growing field of smart home technology.

Integrated Multimedia Wireless Sensor Node for Comprehensive Lifelogging and Beyond: Design, Development and Applications

This paper introduces the Integrated Multimedia Wireless Sensor Node (IMWSN), a significant advancement in environmental monitoring and lifelogging within Multimedia Wireless Sensor Networks (MWSN). MWSNs, equipped with wearable sensors, are crucial for documenting personal life experiences. However, current MWSNs often lack the ability to fully integrate data across various sensor types, including environmental, visual, and medical sensors. The IMWSN addresses this gap by providing a comprehensive view of an individual's interactions and environment. The IMWSN is composed of multiple modules: a processor module that manages the overall system efficiently, a visual module designed to capture video footage of the surroundings, an environmental module that allows for real-time monitoring of environmental conditions, and a medical module dedicated to recording health-related data of individuals, a process often known as lifelogging. These components are encased in a custom-designed 3D-printed enclosure and powered by a durable 4500mAh mobile battery. System programming and monitoring are facilitated through the user-friendly Arduino IDE, making the experience accessible and customizable. Beyond its primary function in lifelogging, the IMWSN is remarkably versatile and suited for a range of applications. It can function as an action camera, assist in forest fire monitoring, support ambient assisted living environments, and monitor patients' health and daily activities rigorously. This adaptability makes the IMWSN a valuable and essential tool in fields that require extensive data collection and sophisticated analytical capabilities, highlighting its broad potential impact.

Unusual modes of cell and nuclear divisions characterise Drosophila development.

The growth and development of metazoan organisms is dependent upon a co-ordinated programme of cellular proliferation and differentiation, from the initial formation of the zygote through to maintenance of mature organs in adult organisms. Early studies of proliferation of ex vivo cultures and unicellular eukaryotes described a cyclic nature of cell division characterised by periods of DNA synthesis (S-phase) and segregation of newly synthesized chromosomes (M-phase) interspersed by seeming inactivity, the gap phases, G1 and G2. We now know that G1 and G2 play critical roles in regulating the cell cycle, including monitoring of favourable environmental conditions to facilitate cell division, and ensuring genomic integrity prior to DNA replication and nuclear division. M-phase is usually followed by the physical separation of nascent daughters, termed cytokinesis. These phases where G1 leads to S phase, followed by G2 prior to M phase and the subsequent cytokinesis to produce two daughters, both identical in genomic composition and cellular morphology are what might be termed an archetypal cell division. Studies of development of many different organs in different species have demonstrated that this stereotypical cell cycle is often subverted to produce specific developmental outcomes, and examples from over 100 years of analysis of the development of Drosophila melanogaster have uncovered many different modes of cell division within this one species.

Smart Nanocoating‐ an Innovative Solution to Create Intelligent Functionality on Surface

AbstractThe interaction of substrate surfaces with the environment (moisture, heat, pressure, chemicals) has cascade effects on their efficiency, performance, durability due to their susceptibility towards microbial adhesion, spontaneous leaching, impairment, corrosion etc. Many scientific, technological innovations such as smart coating have been discovered for the protection of these surfaces. It is a high‐tech, high value coating capable of responding to environmental impulses. Incorporation of nanofillers, nanomatrices in smart coating improve surface performance by extending service life, provide high durability, high physical coverage, adhesive strength etc. Real‐time monitoring of environmental conditions, structural health could be made possible by sensing coatings. This review predominantly highlights the essential features, design, advancements, applications of smart nanocoatings over elementary nanocoatings. To cover the whole study, drawbacks of traditional coating methods are also discussed. Finally, the research gaps, future perspectives of smart nanocoatings will be overlooked.

Plastic debris exposure and effects in rivers: Boundaries for efficient ecological risk assessment.

Until recently, plastic pollution research was focused on the marine environments, and attention was given to terrestrial and freshwater environments latter. This discussion paper aims to put forward crucial questions on issues that limit our ability to conduct reliable plastic ecological risk assessments in rivers. Previous studies highlighted the widespread presence of plastics in rivers, but the sources and levels of exposure remained matters of debate. Field measurements have been carried out on the concentration and composition of plastics in rivers, but greater homogeneity in the choice of plastic sizes, particularly for microplastics by following the recent ISO international standard nomenclature, is needed for better comparison between studies. The development of additional relevant sampling strategies that are suited to the specific characteristics of riverine environments is also needed. Similarly, we encourage the systematic real-time monitoring of environmental conditions (e.g., topology of the sampling section of the river, hydrology, volumetric flux and velocity, suspended matters concentration) to better understand the origin of variability in plastic concentrations in rivers. Furthermore, ingestion of microplastics by freshwater organisms has been demonstrated under laboratory conditions, but the long-term effects of continuous microplastic exposure in organisms are less well understood. This discussion paper encourages an integrative view of the issues involved in assessing plastic exposure and its effects on biota, in order to improve our ability to carry out relevant ecological risk assessments in river environments.

Advancing food safety standards through technology integration and policy development

Food safety is a critical global concern, directly impacting public health, economic stability, and international trade. With the complexity of modern food supply chains and the rising incidence of foodborne illnesses, traditional food safety measures are often insufficient to address the growing risks. This review explores how integrating advanced technologies and developing robust policies can significantly enhance food safety standards. Technological innovations, including the Internet of Things (IoT), blockchain, artificial intelligence (AI), and automation, offer promising solutions for real-time monitoring, traceability, and predictive analytics in food production and distribution. IoT and sensors enable constant monitoring of environmental conditions like temperature and humidity, while blockchain ensures transparency and traceability across supply chains. AI and machine learning are transforming food safety by predicting contamination risks and optimizing inspection processes, while robotics and automation reduce human error in food processing plants. Alongside technological advancements, policy development plays a crucial role in reinforcing food safety standards. Global harmonization of food safety regulations, stronger enforcement mechanisms, and public-private partnerships are essential for ensuring widespread compliance. Incorporating technology into regulatory frameworks is key to addressing new challenges and maintaining food safety across borders. Governments must also prioritize consumer education and awareness through policies that leverage technology platforms to promote food safety knowledge. Case studies demonstrate the successful application of these technologies in sectors like dairy and meat processing, showcasing the potential for broader industry adoption. However, challenges such as high implementation costs, regulatory lag, and data security concerns remain. This review argues that a synergistic approach combining technological integration with forward-thinking policy development is essential for advancing global food safety standards and safeguarding public health in the future.

A smart monitoring and observation framework for aquaponics ecosystem

Aquaponics is a sustainable and efficient agricultural system that integrates aquaculture with agriculture. This system integrates Internet of Things (IoT) technology in aquaponics systems with the potential to change sustainable agriculture by combining farming of fish (aquaculture) and soilless cultivation of plants (hydroponics) into a single, efficient ecosystem. This paper explores the design and development of a smart aquaponics system that allows aquaponic farming at commercial scale feasible, leveraging IoT to continuously gather data through various sensors, monitor environmental conditions, and automate control processes. The proposed system architecture is based on a three-tier model, ensuring scalability and maintainability. The proposed system consists of a three-layer architecture: the Perception Layer for measuring different environmental factors and controlling them, the Network Layer for data transmission, and the Application Layer for data storage, manipulation, and user interaction. The key components include sensors for monitoring pH, humidity, temperature, sunlight, oxygen levels, and water proximity; devices such as lights, water pumps, fans, and oxygen pumps for environmental control; and an ESP8266 microcontroller for system management. The system's software is developed using C# DOT NET CORE 8.0, React, MySQL, C, and Arduino. The system allows users the ability to maintain optimal conditions for fish and plant growth, notify users of irregularities, and allow remote control through a web application, proving it to be an intelligent, flexible, low-cost, and stable solution for large-scale aquaponics.

The Wind Power Watch, "Guarding" for Safety - Data-Driven Intelligent Deterrence System for Monitoring Ships in Offshore Wind Farms

This study successfully developed a ship monitoring and repulsion system for offshore wind farms aimed at ensuring the safety and stable operation of the wind farms. The system is capable of real-time monitoring of ship activities and environmental conditions, quickly identifying and eliminating potential safety risks. The research content includes various aspects such as ship detection, path tracking, repulsion decision-making, and data processing. The system integrates multi-sensor data, Geographic Information Systems (GIS), Automatic Identification Systems (AIS) for ships, artificial intelligence algorithms, and advanced communication technologies, constructing an efficient and intelligent monitoring and repulsion framework. Field test results indicate that the system exhibits stable performance and efficient monitoring capabilities under harsh weather conditions, while the intelligent voice repulsion function has shown significant effects.

An ultraviolet light data logger for studies of organismal ecology and physiology

Abstract Monitoring environmental conditions over time is essential for understanding how species regulate those conditions to satisfy their physiological needs. Variation in ultraviolet (UV) light exposure, for instance, is crucial for numerous taxa, influencing behaviour as well as essential physiological processes like vitamin D3 synthesis. Yet, despite these considerations, no commercially available data loggers exist to record UV exposures over time, preventing inquiry into this important but neglected fitness demand. To overcome this challenge, I designed the first UV data logger, custom built on the open‐source Arduino platform. This device logs UV exposures (in voltage or converted to UV index) and date‐time information for each UV reading, at user‐defined intervals. The integration of a low‐power timer into the assembly, and its weather‐resistant housing, enables long‐term use (at least several months). The cost of one device (including housing) is under £45/$55 USD. Calibration and performance tests reveal that this device is reliable under various weather conditions and produces accurate UV readings under both artificial lighting and sunlight. Both the device and its customised housing can be easily replicated and expanded upon (e.g. inclusion of additional sensors). Overall, this device represents a crucial first step in our ability to better characterise UV exposure patterns in habitats for physiological ecology studies. Exposure to UV light is adaptive for many types of organisms, both in the wild and in captivity (e.g. zoos and private hobbyists), and so I expect a broad array of scientific and public users alike to benefit from using this device.

Optimalisasi Waktu Produksi Jamur Tiram Melalui Implementasi Sistem Penyiraman Berbasis IoT di UD Zaida Jamur Tiram

The optimization of oyster mushroom production time through the implementation of an Internet of Things (IoT)-based irrigation system at UD Zaida Jamur Tiram aims to enhance efficiency and production quality. This program integrates IoT technology to automate the irrigation process, allowing real-time control and monitoring of environmental conditions. Methods include the design and installation of an automatic irrigation system, technical training for operators, and performance evaluation through production data analysis. Results show a significant increase in time efficiency and consistency in oyster mushroom quality. The implementation of this IoT system is expected to support the sustainability and competitiveness of the oyster mushroom industry at UD Zaida.

IoT - Toxic Gas Detection System in Sewage

The application of IoT and machine learning in gas detection systems for sewage offers an innovative approach to real-time gas detection and enhances environmental safety in industrial and urban environments. This system uses advanced technologies such as the Internet of Things (IoT), machine learning algorithms, and high-performance sensors like the MQ135 and the DHT11 to achieve the best results in the gas concentration measurement and collection. It identifies abnormalities and determines which emission control measures are most effective for specific release points and similar situations. The design of the device includes sensor nodes that are primarily responsible for data collection and a central microcontroller (MCU) that operates a machine learning algorithm for efficient anomaly detection and predictive maintenance. The system uses the IoT connection to regularly send data to cloud platform (Blynk), enabling real time monitoring of gas levels and environmental conditions. The system generates a visual image of the captured data that can be accessed online. Distinctive attributes like accurate gas detection, continuous monitoring, predictive maintenance, remote assessment, and comprehensive data visualization, all contribute to smart decision-making for environmental safety.

Impact of Smart Greenhouse Using IoT for Enhanced Quality of Plant Growth

Greenhouses play a crucial role in manipulating environmental conditions for optimal plant growth. While existing greenhouses enhance control over environmental factors, manual controls such as watering and humidity regulation often lead to suboptimal production and increased costs. This study proposes the development of a smart greenhouse with an automatic control system using fuzzy logic, specifically fuzzy Sugeno, to regulate watering and lighting based on soil moisture, temperature, and light intensity. The system's architecture involves sensor inputs, microcontroller processing, and the activation of actuators, such as UV lights and water pumps. Fuzzy logic is applied to interpret soil moisture and temperature inputs and determine optimal irrigation durations. The system's functionality is tested and validated through functional testing, Blynk application testing, and fuzzy Sugeno testing. Results indicate the successful implementation of the proposed smart greenhouse system. Functional testing demonstrates accurate sensor readings, including temperature and soil moisture. The Blynk application enables real-time monitoring and control of environmental conditions. Fuzzy Sugeno testing validates the irrigation control system, with an average error rate of 1.3%, affirming the system's alignment with desired specifications. Plant testing in different conditions showcases the effectiveness of the smart greenhouse in supporting plant growth and development.

Harvesting Knowledge: Data Science and Machine Learning Techniques for Evaluating Pesticide Impact in Vegetable Organic Farming

Abstract: The integration of data science and machine learning is revolutionizing the assessment of pesticide impact in organic vegetable farming. This review explores methodologies, applications, and research examples showcasing the transformative potential of data-driven approaches. Remote sensing, including satellite imagery and drones, is essential for monitoring crop health and detecting pesticide impacts on vegetable crops like tomatoes, lettuce, and red peppers. By synthesizing research and trends, the review underscores technology's significance in informed decision-making for sustainable vegetable organic farming practices. Spectral analysis and vegetation indices quantify changes in crop health, informing pesticide efficacy and environmental impact. Sensor networks and IoT devices allow real-time monitoring of environmental conditions and pesticide dynamics, optimizing application practices to minimize contamination while maximizing yield. Machine learning, particularly decision tree-based models like random forests, predicts and mitigates pesticide impacts by analyzing complex datasets. Incorporating variables such as soil type and climate, these models accurately forecast pesticide fate, aiding in targeted mitigation strategies. Deep learning, such as convolutional neural networks (CNNs), identifies pesticide stress symptoms from digital images of vegetable leaves, facilitating rapid intervention. Challenges like data integration and model interpretability persist, yet ongoing research addresses these through data fusion and explainable AI. This review emphasizes the progress in leveraging data science and machine learning for pesticide impact evaluation in organic vegetable farming. By synthesizing research and trends, it offers insights for future sustainable agriculture applications.

Assessment of Surface Water Quality of Igarapé de Santo Antônio in Santo Antônio do Tauá, Pará

Objective: Analyze the surface water quality of the Santo Antônio stream in Santo Antônio do Tauá, in the state of Pará, based on the current environmental legislation of CONAMA nº 357, 274 and Ordinance GM/MS nº 888. Theoretical Framework: The research uses contributions from the literature regarding normative guidelines on water quality standards essential for understanding and monitoring environmental conditions and related problems, through specific laws and regulations. Method: This is an experimental, descriptive, cross-sectional study, with a qualitative-quantitative approach, developed from water samples from the study site. The microbiological and physicochemical analyzes were determined by the procedures and recommendations described in the Standard methods for the examination of water and wastewater (APHA). Results and Discussion: The results proved that the physical-chemical and microbiological parameters of surface water are in accordance with the standards established in legislation regarding water classification and bathing (CONAMA nº 357 and 274), being in disagreement only with regard to potability standards (Portaria GM/MS nº 888), at one of the points of analysis. Research Implications: The data and observations allowed a response to potential environmental problems, contributing to the quest to preserve water resources and serving as a model for other communities facing similar challenges. Originality/Value: The study provides unprecedented data regarding the water quality of the Santo Antônio stream, identifying variations and trends that correlate with local environmental management.

SKIN HEALTH PARAMETER MONITORING SYSTEM

Skin conditions, influenced by both internal bodily factors and external environmental elements, affect millions worldwide. Among these, environmental factors play a vital role, impacting skin health in many ways. In response to this issue, the prototype system provides real-time data on environmental parameters crucial for skin wellness. It integrates sensors to monitor temperature, humidity, UV radiation, and skin moisture, all connected to a microcontroller. This data is displayed on an LCD screen and transmitted to a website and mobile application (APK). Users receive alerts when sensor readings exceed predefined thresholds, suggesting necessary precautions. Additionally, the system features an IP webcam for capturing skin images, which are analysed to provide insights and skincare recommendations. This innovative approach bridges the gap between environmental factors and skin health, promoting proactive measures against environmental stressors. By providing real-time data and actionable insights, the prototype empowers users to make informed decisions about their skincare regimen. The system represents a significant advancement in skincare technology, offering comprehensive monitoring and management of environmental conditions and skin health. With its real-time functionality, recommendations, and proactive alerts, the system has the potential to greatly enhance users’ skincare routines and overall skin health. Key Words: Skin health, environmental monitoring, real time data, sensor integration, image analysis, proactive measures

Live Data Monitoring in Industry

Abstract: In contemporary industrial environments, real-time monitoring of critical parameters is vital for ensuring safety, efficiency, and compliance with regulations. This thesis details the development and implementation of an advanced live data monitoring system designed to track temperature, humidity, fire presence, and oxygen levels. Key components of the system include a DHT sensor for temperature and humidity measurement, a fire sensor for fire detection, an oxygen sensor for monitoring atmospheric oxygen, and a buzzer for immediate auditory fire alerts. Central to the system is the ESP8266 WiFi module, which facilitates the wireless transmission of sensor data to ThingSpeak, a cloud-based data collection and visualization platform. This setup allows for continuous, remote monitoring of environmental conditions, providing critical insights and alerts to industrial operators. The project delves into the hardware configuration, sensor calibration, data acquisition processes, and wireless communication protocols essential for effective real-time monitoring. It also addresses the challenges of integrating multiple sensors and ensuring reliable data transmission in an industrial setting. The results demonstrate the system's capability to provide accurate, timely data, enhancing safety protocols and operational efficiency. By utilizing IoT technology, the proposed system offers a scalable solution for industrial environments aiming to implement advanced monitoring and alert systems. This work contributes to the field of industrial automation by illustrating a practical application of IoT in improving monitoring and safety measures, highlighting both the technical achievements and practical implications of the implemented system.

Revolutionizing Agriculture with Nanotechnology: Advances, Applications, and Sustainability Considerations

The integration of nanotechnology into agriculture has garnered significant interest due to its potential to revolutionize agricultural practices. This paper explores the application of nanotechnology in crop protection, nutrient delivery, soil management, and environmental sustainability. Nanopesticides and nanofertilizers represent notable advancements, offering enhanced efficacy, reduced environmental impact, and improved targeted delivery of active ingredients. Nanomaterials such as nanoparticles and nanocapsules encapsulate nutrients and agrochemicals, ensuring gradual release and optimized plant uptake. Nanosensors, based on materials like carbon nanotubes and quantum dots, enable real-time monitoring of soil health, crop growth, and environmental conditions, facilitating precision agriculture. Nanomaterials also play a role in soil remediation and pollution control by degrading pollutants and enhancing soil fertility. Additionally, nanobiotechnology offers eco-friendly solutions for pest and disease management through nanoscale delivery systems for biocontrol agents and plant vaccines. Despite the transformative potential of nanotechnology in agriculture, considerations of safety, regulatory oversight, and ethical implications are essential. Interdisciplinary collaboration and stakeholder engagement will be crucial to harness the full benefits of nanotechnology for sustainable agriculture.