INTERNET OF THINGS: AUTOMATIC PLANT WATERING SYSTEM USING ANDROID

Abstract: The growing demand for intelligent energy management systems has led to the development of Internet of Things (IoT)-based smart meters that provide real-time monitoring, remote control, and data analysis. This paper presents the design and implementation of a smart energy meter that utilizes Wi-Fi (ESP32), Bluetooth (HC-05), and GSM (SIM800L) technologies to monitor energy consumption and control connected appliances remotely. The system leverages an energy meter for measurement, a microcontroller for processing, a relay module for control, and a user-friendly communication framework. Experimental results validate the effectiveness of the system in real-world scenarios, with applications in residential, commercial, and industrial energy monitoring. The proposed system also supports integration with cloud platforms and mobile applications to enhance accessibility and improve decision-making. Keywords: IoT, Smart Energy Meter, ESP32, GSM, Bluetooth, Energy Monitoring, Home Automation, Blynk, Real-time Monitoring, Remote Control

Rural communities in Cibodas, East Bandung, face serious challenges in aquaponic farming practices, particularly in the uneven application of conventional fertilizers. This imbalance results in suboptimal crop quality, reducing the potential for agricultural productivity in the region. To overcome this obstacle, an innovative solution is needed in the form of an Internet of Things (IoT)-based automatic liquid fertilizer irrigation system. This system is designed to distribute fertilizer evenly to all plants, ensuring that each part of the plant receives adequate nutrients, and that fertilizer is applied efficiently. The use of this system not only increases the efficiency of farmers' work, but also contributes to improving the overall quality and quantity of crop yields. The stages of activities in the implementation of this community service are the development of an automatic liquid fertilizer irrigation system based on the Internet of Things (IoT), the application or implementation of the system, and finally the socialization of the use of the system. This community service activity is a series of student community service activities carried out in October 2024. The elements involved are the community of Cibodas Village, lecturers and students of Politeknik Manufaktur Bandung. As a result of this service activity, the community understands and can apply agricultural technology, in this case IoT-based automatic liquid fertilizer spraying technology with remote control. The liquid fertilizer spraying and watering system can increase the efficiency of time and energy. It is hoped that the technology that has been developed will be one of the effective solutions to the problem of uneven spraying of plant fertilizers.

Smart farming with accurate greenhouses needs to be implemented for better farming growth management, and therefore, precision agriculture monitoring in diverse conditions is required. The Internet of Things (IoT) is a new era in computer communication that is gaining traction due to its vast range of applications in project development. The IoT provides individuals with smart and remote approaches, such as smart agriculture, smart environment, smart security, and smart cities. This is the most recent technology that is making things easier these days. The Internet of Things (IoT) has fundamentally expanded remote distance control and the diversity of networked things or devices, which is an intriguing element. The IoT comprises the hardware as well as the Internet connectivity to the real-time application. Sensors, actuators, embedded systems, and an Internet connection are the key components of the Internet of Things. As a result, we are interested in creating a smart farm IoT application. In greenhouse agriculture, this study presented a remote sensing of parameters and control system. The objective is to manage CO2, temperature, soil moisture, humidity, and light, with regulating actions for greenhouse windows/doors dependent on crops being carried out once a quarter throughout the year. The major goal is to properly regulate greenhouse conditions in accordance with plant requirements, in order to enhance output and provide organic farming. The results show that the greenhouse may be controlled remotely for CO2, temperature, soil moisture, humidity, and light, resulting in improved management.

This paper describes the development of a monitoring and controlling system for a garden using the Internet of Things (IoT). The system is designed to assist users in managing their plants and gardens remotely and effectively. Soil moisture levels are difficult to assess and manage, and achieving their target levels can be challenging. Therefore, an automatic water pump system is the optimal solution for this situation. The soil moisture sensor measures the moisture content in the soil, and the system automatically turns on the water pump when the moisture level is insufficient for healthy plant growth. A Light Emitting Diode (LED) is used to notify the user when the water pump is operating. A NodeMCU is used as the microcontroller for this project, which has a built-in Wi-Fi module, making it easier to interface with the Blynk software on a smartphone. The input is a soil moisture sensor, and the outputs are a water pump and an LED. The system successfully demonstrated real-time monitoring and automated control of soil moisture, ensuring optimal plant health. The integration of NodeMCU with Blynk enabled seamless remote access and notifications, enhancing user convenience. Experimental results confirmed the system's reliability in maintaining soil moisture levels within the desired range, reducing manual intervention, and improving water efficiency. This IoTbased approach provides a practical and scalable solution for smart gardening applications.

The integration of the Internet of Things (IoT) with intelligent automation systems has opened new opportunities in precision agriculture, particularly for small-scale and indoor farming. This study presents the design and implementation of a smart terrarium system based on the Internet of Things and fuzzy logic to autonomously regulate environmental conditions, such as temperature, humidity, soil moisture, and light intensity. The prototype employs an ESP32 microcontroller integrated with DHT11, soil moisture, and LDR sensors, which continuously monitor the microclimate and control actuators, including a cooling fan, water pump, and grow lamp. Unlike traditional threshold - based control, the fuzzy inference system enhances adaptability by processing uncertain and nonlinear sensor data, thereby improving the decision-making accuracy under dynamic environmental conditions. The system was connected to the Blynk IoT platform, which enabled real-time monitoring, remote control, and manual override functions. The experimental results demonstrate reliable system performance: the fan activates under hot conditions, the pump operates under dry soil states, and the lamp responds to low light intensity, effectively maintaining an optimal microclimate. These findings highlight the advantages of combining the Internet of Things and fuzzy logic for adaptive environmental management, reducing manual intervention, improving resource efficiency, and ensuring plant growth stability in compact urban settings. Furthermore, this study contributes to sustainable agriculture by offering a scalable model that can be extended to larger urban farming applications. Future research should explore predictive analytics, machine learning integration, and enhanced user interfaces to further strengthen system resilience, scalability, and usability.

The agriculture sector is one of the largest consumers of fresh water. Different types of irrigation systems are available, including center pivot, drip and sprinkler systems, and linear motion systems. However, the complex structure of existing irrigation systems and their high maintenance costs encourage Indian farmers to continue using these methods. Due to its ease of use and low energy consumption, surface irrigation is one of the most popular irrigation techniques. Although the main reasons for poor irrigation application efficiency are uneven irrigation water distribution and deep absorption, using a variety of technologies, countries are trying to increase the sustainability of agriculture. Automated irrigation systems contribute significantly to water conservation. The combination of automation and Internet of Things (IoT) improves agricultural practices. These technologies help farmers understand their crops, minimize their impact on the environment, and preserve resources. They also enable efficient monitoring of the weather, water resources, and soil. This research proposes an intelligent, low-cost field irrigation system. The proposed prototype can measure soil moisture, rain status, wind speed, water level, temperature, and humidity using a hardware sensor and unit. To decide whether to turn on or off the motor, a variety of sensors are used to get a range of readings and conclusions. They enable automatic watering when soil moisture levels are below a certain threshold, and if soil moisture is equal to the required moisture, then the irrigation process stops. Every few minutes, the sensors measure the environmental factors. Data are collected and stored on a ThingSpeak cloud server for analysis. To evaluate the data we collected, we used a variety of models, such as K-nearest neighbors (KNN), Naïve Bayes, random forest, and logistic regression. Compared to other Naïve Bayes and random forest models, the accuracy rate was 98.8%, the mean square error was 0.16, and the results of logistic regression, KNN, and SVM were in order: (98.3%/1.66), (99.3%/0.66), and (99.5%/0.5), respectively. In the end, an automated irrigation system run on IoT applications gives farmers access to remote monitoring and control, as well as information about the specifics of the irrigation field.

The increasing use of wireless communication technologies has made IoT and machine-to-machine (M2M) connections crucial to a wide range of industries. Smart buildings are an excellent example of an IoT/M2M application. Issues such as a lack of a convergent solution, high costs, restricted wireless transmission range, user-unfriendly interfaces, and the inability to integrate IoT and M2M technologies. Consequently, this article suggests a more effective method for for Machine-to-Machine (M2M) and The Internet of things smart building systems: heterogeneous wireless networks that include both WSNs and MCNs. The suggested system is a cost-effective embedded system that includes Several functional actuators, sensors, and modules for Arduino and NodeMCU, ESP32CAM boards allow for data collection and control across a variety of heterogeneous communication methods like Bluetooth, Wi-Fi, and global system for Mobile (GSM). All collected data is issued to the ThingSpeak platform, allowing the building system to be monitored via the Thing Speak webpage or the UR Smarthome app. One of the researchs most important results is that it can give the server precise information with extremely little delay, allowing users to quickly control and monitor remotely the proposed system that consists of several innovative services names indoor applications(fire detection and gas leakage detection, intrusion alarm by ultrasonic, medicine reminder by RTC module, light and fan control, smart door) and outdoor applications(garden irrigation by soil moisture and DHT11, air quality, smart parking ). Our free mobile application UR Smarthome, which is a local server that controls and monitors the building from a distance, is used to plan and execute all of these services that provides remote control and monitor of the building via Bluetooth/ Cellular networks and Wi-Fi access. With its customizable features, this IoT/M2M smart building system can be tailored to the demands of its users, enhancing their quality of life and safety while using less energy. In addition, identifying and lowering risks, it assists in preventing the loss of resources and human lives. KEYWORDS— Converged networks, heterogeneous networks (HetNets), Internet of Things (IoT)/machine-to-machine (M2M), mobile application, mobile cellular networks (MCNs), smart building, wireless sensor networks (WSNs)

The proposed method changes the original remote controller for home appliances become IOT (internet of things) technology-based and cross-platform control by the smart phone remote wireless control. In our research, smart phone will replace the traditional remote control operation. When the controller press mobile Web App, in addition to the circuit module will emitting infrared signals, our hardware module will also upload information to the cloud database. Such an innovative approach would be able to provide accurate monitoring by real-time monitoring status and real-time analysis. In the proposed smart home embedded system, data sent back from each sensor inside the household can be instantaneously analyzed, Furthermore, the use of Network Address Translation (NAT) technology to control remotely via the Internet. In terms of the construction process of the smart factory system, this dissertation proposes an instantaneous method that carries out the monitoring of factory area temperature, humidity and air quality using smart mobile phone. At the same time, the system detects potential flame, analyze and monitor power loading. These monitoring also include shock detection of operating machines in factory premises. The study proposes integrating ZigBee and Wi-Fi protocol smart monitoring system in the structure of the whole factory. Via ZigBee communication Protocol, the sensors in the factory transmit messages and the instantaneously detected data to the integrated regulating system. Lastly, this research study will, in depth, analyse hands-on problems generated during instantaneous integration of signal packing for various communication protocols. As well as composing the know-how of overcoming these problems using the innovative methods of this study while proposing efficient solution schemes. The above become the greatest features in the building of this integrated regulation system.

Smart Home Automation Using Google Assistant

Advanced sensor technology, especially those that incorporate artificial intelligence (AI), has been recognized as increasingly important in various contemporary applications, including navigation, automation, water under imaging, environmental monitoring, and robotics. Data-driven decision-making and higher efficiency have enabled more excellent infrastructure thanks to integrating AI with sensors. The agricultural sector is one such area that has seen significant promise from this technology using the Internet of Things (IoT) capabilities. This paper describes an intelligent system for monitoring and analyzing agricultural environmental conditions, including weather, soil, and crop health, that uses internet-connected sensors and equipment. This work makes two significant contributions. It first makes it possible to use sensors linked to the IoT to accurately monitor the environment remotely. Gathering and analyzing data over time may give us valuable insights into daily fluctuations and long-term patterns. The second benefit of AI integration is the remote control; it provides for essential activities like irrigation, pest management, and disease detection. The technology can optimize water usage by tracking plant development and health and adjusting watering schedules accordingly. Intelligent Control Systems (Matlab/Simulink Ver. 2022b) use a hybrid controller that combines fuzzy logic with standard PID control to get high-efficiency performance from water pumps. In addition to monitoring crops, smart cameras allow farmers to make real-time adjustments based on soil moisture and plant needs. Potentially revolutionizing contemporary agriculture, this revolutionary approach might boost production, sustainability, and efficiency.

The increasing concerns about environmental degradation and the proper management of liquid waste have led to the exploration of innovative technological solutions.This project presents a comprehensive approach to monitoring and controlling liquid waste utilizing Internet of Things (IoT) technology and fuzzy logic.The system aims to achieve real-time data collection, intelligent decision-making, and remote control through the aerator system by integration of sensors, microcontrollers, and the Thing Speak IoT platform.The project focuses on developing a smart liquid waste management system capable of measuring key parameters such as COD(chemical Oxygen Demand).The collected data are then processed through a fuzzy logic algorithm to make informed control decisions.The system employs a Node MCU ESP8266 microcontroller to interface with the sensors and actuators.The collected data are transmitted to the Thing Speak platform, where they are visualized and analyzed.The key advantage of integrating fuzzy logic lies in its ability to handle imprecise and uncertain data, which are often encountered in environmental monitoring scenarios.By leveraging fuzzy logic, the system can provide adaptive control decisions that respond to the dynamic nature of liquid waste conditions.Furthermore, the integration with Thing Speak enhances the system's capabilities by enabling real-time monitoring, data visualization, and remote control through mobile applications.The implementation of this project underscores the significance of IoT and fuzzy logic in addressing critical environmental challenges.The outcomes demonstrate the potential for optimizing liquid waste management practices, reducing the environmental impact, and promoting efficient resource utilization.This project contributes to the growing field of IoT-enabled environmental monitoring and offers insights into the effective utilization of fuzzy logic for decision-making in real-world applications.

PDF HTML阅读 XML下载 导出引用 引用提醒 太湖流域丘陵区两种土地利用类型土壤水分分布控制因素 DOI: 10.5846/stxb201405181019 作者: 作者单位: 中科院南京地理与湖泊研究所,中科院南京地理与湖泊研究所,中科院南京地理与湖泊研究所,中科院南京地理与湖泊研究所 作者简介: 通讯作者: 中图分类号: 基金项目: 国家自然科学基金(41271109, 41301234); 中国科学院南京地理与湖泊研究所"一三五"重点项目(NIGLAS2012135005) The controlling factors of soil moisture distribution under two typical land-use hillslopes in a hilly region of Taihu Lake basin Author: Affiliation: Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences,,Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences,Nanjing Institute of Geography and Limnology, Chinese Academy of Sciences Fund Project: 摘要 | 图/表 | 访问统计 | 参考文献 | 相似文献 | 引证文献 | 资源附件 | 文章评论 摘要:为探究太湖流域丘陵区典型土地利用类型(如竹林地和茶园)土壤水分的控制因素,在不同深度土壤水分定期观测的基础上,根据前7d降雨量将研究时段划分为干旱状态和湿润状态,利用分类与回归树(CART)方法得出不同干湿状态下土壤水分分布的主控因子,并借助典范对应分析(CCA)定量分析不同土地利用类型、不同土壤深度土壤水分格局与环境因子关系。结果表明:(1)高程、土地利用类型和土层厚度对土壤水分分布的相对贡献率最大,但在不同干湿状态下其影响程度存在差异; (2)干旱状态时土壤水分主要受高程、坡度、地形湿度指数(TWI)和剖面曲率等地形因素的作用,而土层厚度和粘粒也分别为0-20 cm和20-40 cm深度土壤水分的主控因子; (3)在湿润状态下,茶园0-20 cm土壤水分的主控因素为地形因子,在20-40 cm则以土壤性质为主,竹林地两个深度的土壤水分受地形和土壤性质的作用都很强,其中20-40 cm深度土壤水分与环境因子的关系较0-20 cm深度更为复杂。 Abstract:Soil moisture is one of the most important factors in terrestrial ecosystems. Adequate knowledge of its spatio-temporal variability is critical to many scientific and practical applications. We explored the factors controlling moisture distribution in soils under typical land-use types (i.e., bamboo forest and tea garden) in a hilly region of Taihu Lake basin. In situ soil moisture measurements were made at different depths, and these were classified as dry or wet conditions, based on the precipitation during the previous 7 d. The main controlling factors were identified by using the classification and regression tree (CART) method. Canonical correspondence analysis (CCA) was then applied to quantitatively analyze the relationships between soil moisture and environmental factors under different land-uses at different soil depths. The results show that: (1) the relative influences of elevation, land use, and soil thickness on soil moisture distribution were larger than those of other environmental factors in most cases. Together these accounted for more than 50% of the total variation in soil moisture. However, their relative contributions differed between dry and wet conditions. (2) In dry conditions soil moisture was mainly affected by topographic indices such as elevation, slope, topographic wetness index (TWI) and profile curvature. Soil thickness and clay content also significantly affected soil moisture at 0-20 and 20-40 cm depths, respectively. The main factors controlling soil moisture in dry conditions varied with soil depth, but not with land-use type. This finding was different from that of a previous study, in which the study area had similar landforms and climatic conditions. This may be due to the effects of spatial scale. (3) In wet conditions, topographic indices and soil properties were the main factors controlling soil moisture in tea gardens at 0-20 and 20-40 cm depths, respectively. Both topographic indices and soil properties were the main controlling factors of soil moisture in a bamboo forest at these two depths. It is noted that the relationship between soil moisture and environmental factors at 20-40 cm depth is more complicated than that at 0-20 cm depth in bamboo forest. In contrast to dry conditions, both soil depth and land-use type had a significant impact on the environmental factors that control soil moisture distribution. This study is of great relevance to the sustainable utilization of water and soil resources and to the management of water and fertilizer in agriculture in this region. 参考文献 相似文献 引证文献

The design of a Soil moisture monitoring system with the Smart Farming Concept for IoT-based Chili Cultivation is a system design tool used to monitor Soil moisture in cayenne pepper cultivation. The monitoring system can determine Soil moisture conditions by monitoring Soil moisture information using IoT-based Microcontroller technology so that farmers can monitor the growth of cayenne pepper plants without the need to come to the Greenhouse location to monitor. The design of this monitoring systemuses a Soil moisture sensor. Where the Soil moisture sensor works is to monitor in real-time the state of Soil moisture in a Greenhouse. The classification of Soil moisture on soil quality so that the growth of cayenne pepper plants does not experience problems for dry soil and 61% to 100% for wet soil. The optimal Soil moisture content for cayenne pepper plant growth is in the range of 60% to 70% moist. The program flow to create a Soil moisture monitoring system for IoT-based chili cultivation goes through 3 main stages, namely the initialization, setup, and loop stages so that it can produce a display on the Soil moisture monitoring system application in real time. The results of the design of a Soil moisture monitoring tool based on trials show the optimal Soil moisture value at an average humidity of 63.5% and automatic watering will be carried out by the system when the Soil moisture level decreases from 60% so that it can be concluded that the IoT- based Soil moisture monitoring system can work well.

With the expansion in the use of IoT, increasing the efficiency of these networks has become even more significant. Objects need reliable communications at suitable speed to be able to reach the expected performance. In a heterogeneous network of IoT, the objects can include users and related devices. Despite the hybrid Li-Fi and Wi-Fi networks, IoT needs were somewhat met. However, the efficiency of these networks depends on the choice of access points at the network level. In this study, a new algorithm was proposed based on the access point selection model, considering the strategic behavior of the objects and users. In this algorithm, inspired by access point selection model, a new adaptive algorithm was selected by choosing the access point by Markov game to enhance the load balancing and efficiency in IoT networks based on Wi-Fi and Li-Fi combination. According to the simulation results, it is seen that the proposed method could greatly increase the efficiency of IoT network and better distributes network load between access points with different technology. Overall network throughput was estimated at an average of at least 10% compared to the load-balancing method with fuzzy logic approach proposed in before research.

ABSTRACTEffective irrigation management is crucial for optimizing crop production, particularly in water‐scarce regions. This study evaluated the performance of an Arduino‐based system designed to monitor and control soil moisture in a greenhouse setting, focusing on its impact on tomato plant growth, fruit yield, and fruit size under two different irrigation treatments. Treatment 1 (T1) involved low moisture with significant fluctuations (55%–85% soil moisture), while Treatment 2 (T2) maintained optimal and stable moisture levels (70%–85%). Soil moisture dynamics revealed that in T1, moisture levels oscillated significantly, dropping to 55% before irrigation restored them to 85%. This cyclical pattern indicates a stress‐response mechanism triggered by the system, which is essential for mitigating plant stress and ensuring optimal growth. Conversely, the optimal moisture treatment maintained more stable soil moisture levels between 70% and 85%, promoting healthy plant development and physiological functions. The correlation between sensor readings and gravimetric measurements was analyzed using a 45° diagonal correlation approach, demonstrating strong agreement between the two methods and reinforcing the reliability of sensor‐based irrigation. Physiological assessments indicated that seedlings under optimal irrigation experienced a 30% increase in fresh weight, a 6% increase in dry weight, a 16% increase in plant height, and a 25% higher SPAD values compared to T1 at the young stage. At maturity, T2 plants exhibited a 52% increase in fresh weight, a 78% increase in dry weight, and a 121% increase in plant height. Fruit yield increased by 47% in T2, with an average of 56 fruits per plant compared to 45 in T1, and the average fruit weight was 85 g in T2 compared to 56 g in T1. Future research should explore the integration of advanced sensors, machine learning algorithms, and predictive models to further optimize irrigation strategies, with an emphasis on scalability and environmental impact. By refining these technologies, agriculture can achieve more sustainable and productive outcomes in the face of increasing environmental challenges.