The popularity of Internet-connected electronic de-vices is growing at an exponential rate, emphasizing the significance of the Internet of Things (IoT). IoT connectivity is following the 3A (Anytime, Any-one, Anything) paradigm. Tech Smart Homes & Cities, IoT Retail Shops, Farming, Smart Grids, Smart Sup-ply-chain Management, and other industries are incorporating IoT. Agriculture is one of the most prominent sectors in todays world. Farming and its associated employment play an important role in a nations economic and commercial growth. Accor-ding to FAO statistics, agriculture employs 60 percent of the worlds population, with the major number of those employed coming from South and South-East Asia.

According to IoT Total Available Market (TAM) projections, the number of IoT-connected devices in the world can increase from 7.6 billion to 24.1 bill-ion, with revenue increasing from USD 465 billion to more than USD 1.5 trillion. There is a rapid transi-tion from traditional agriculture to farm management controlled by completely different IoT companies. The adoption of advanced IoT technologies assists growers in producing higher yields from farms in response to rising demand. According to new Meti-culous research, the smart agriculture of IoT market will grow at an Annual Rate of Growth (CAGR) of 15.2 percent from the year 2020 to $32.7 billion by the year 2027. Exactness agriculture is constantly evolving in tandem with advancements in basal IoT technologies. This evolution aims to achieve several key options for increasing potency and crop yields. These characteristics include (a) information metrics for observance, (b) a higher cognitive process, (c) crop protection and managing, (d) waste reduction and operational value, (e) crop variable rate, (f) in-formation trend for many use-cases, and (g) pest control. Chargeable core features for creation and comparison Precision agriculture Key Performance Indicators (KPIs) rely on technological various factors like sensors, mobility, time monitoring, con-nectivity, and simple application deployment. Fig.1 presents a comprehensive view of the framework that takes into account the quality level of IoT data throughout the application life cycle (Tamanna et al., 2022; Fizza et al., 2022).

Fig. 1: Lifecycle of a generic smart IoT application.

Research Contribution

In that topic, a yield of a monitoring system inclu-ding a pleasing design interface, a high level of convenience and a wireless communication system has been designed by this research work. The goal of this work is to create a friendly environment for farmers so that they can easily efficiently maintain their fields. They dont need any high or mid-level technical knowledge to maintain this system because the whole system is very simple and user-friendly. Thus, when compared to other existing manual works, the entire system is cost-effective and eco-friendly, and the farmer can reliably produce more foods while also saving energy costs. The following are the main contributions of this research work

1) The solar panel to provide power to the entire system has been introduced in this work, the entire system is both cost effective and environ mentally friendly. As a result, the cost is significantly reduced.

2) An Automatic Lighting System has been attached in this work. As we all know, many plants require over 12 hours of lighting. 

3) Various sensors, like the DHT22 (Dihydro-testosterone 22) sensor for measuring temp. and counting humidity, the Soil Moisture Sensor for measuring soil moisture, the Raindrop Sensor for detecting rain, the PIR (Passive in-frared) Motion Sensor for detecting external objects in the field, and the Water Pump for irrigation.

Literature Review

The authors proposed a Smart AgroTech model in Podder et al. (2021). They considered humidity mea-surement sensor, temperature measurement sensor, and soil-moisture measurement sensor in this model. They categorize their components into five distinct sections: technology, controller, sensor, relay module, and water pump. They used a switch plat-form called Relay Module in the modern AgroTech model. They used an electric water pump motor in the final section. The relay module ensures that the conditions for turning ON/OFF the pump motor are met. Podder et al. (2021) considered a moisture level of 55 per-cent to measure moisture in the soil, and the control is dependent on a moisture level below or above 55 percent. In this model, they use the ESP8266 as a controller for the systems and sensors. To quantify their model, they attempted to determine the error rate between genuine and noticed inform-ation in a variety of situations.Some underground and above-ground sensors are included in Almalki et al. (2021). Data that comes from the sensors is collected every 1 hour and then sent to the cloud, where it is analyzed every 12 hours. The authors of Almalki et al. (2021) attempted to implement a low-cost platform using sensors and a flying drone, all of which are linked via cloud computing technology. The segments are referred to as space segment and ground segment. Their drone technology in the space segment inclu-des a camera to capture the area. There is an under-ground sensor gateway that collects and combines the data from sensors installed under-ground, and a HOBO U30 is a weather-station starter kit that collects and combines the whole data from sensors installed aboveground. Rohith et al. (2021) proposed smart irrigation-based cultivation. Watering prepar-ation was computerized in their proposed frame-work, reducing manual labor. Different plant and soil parameters, such as temperature, dampness, and mugginess, were detected using unique sensors and the Arduino UNO. Their demonstration worked well in a small field, but in a larger field, it might run into trouble. IoT-based automated greenhouse farm-ing concentrated in Tripathy et al. (2021). In that paper, the authors used numerous sensors like as tem-perature, humidity, UV light, pH measurement, CO2 level, EC value, and insecticide or pesticide mea-surement, and they provide a system called Deci-sion-Support System in short (DSS) that controls all system activity. The main goal of this paper is primarily on rose cultivation. In a data acquisition system, there are many types of different sensors and a machine learning classifier called Support Vector Machin (SVM) for specimens to pre-vent diseases. In the actuator management system, there is a Central Actuator manager who receives all DSS instructions and controls all actuators. Accor-ding to the DSS, there is an interface for the end-user to view and control everything, and the user can receive an in-app notification or SMS for any type of interaction. In their case study, they attempted to demonstrate the distinction between the trivial app-roach and their proposed "MyGreen" approach. 

Atmaja et al. (2021), describe the communication system between the sensor and the database. All of the sensors data will be sent to the database, and they will use the Wireless Sensor Network (WSN) to do so. As a microcontroller, they proposed the Rasp-berry Pi. They used soil pH sensors and moisture sensors in their proposed model. Water pumps and sluice gates were also used, and everything can be controlled via web and mobile apps. The collected data from the sensors will be processed by Arduino and sent to the server via ESP8266 in their research method. To evaluate Raspberry Pi, they tried it at various distances with and without a barrier to determining whether it succeeded or failed. Izzuddin et al. (2021) concentrated on urban farming methods in Indonesia. The population rate of other cities around the world is increasing daily, just as it is in developed countries. However, land area is shrink-ing. As the result, the urban farming technique is critical for meeting the food needs of a huge number of people. In that paper, they used Arduino UNO as a microcontroller, and the whole system also in-cludes many sensors like a humanity sensor, a tem-perature sensor, a soil moisture sensor, and so on. All types of sensors are wired directly to the micro-controller, which is linked to a single-board com-puter (SBC). The sensor then collected a huge amount of data and converted it into an electric signal, transforming it into a digital data unit. Solar power was used as an alternative source of electricity throughout the entire system. Collected data directly sent to the web server and SQL server, where it is reserved. The web part displays and reserves all of the collected data. The area may be irrigated based on sensor data. Sensors collect data on their own and send it to the Raspberry Pi. The data will be pro-cessed and fed into the two main components of the Raspberry Pi. The primary goal of Ariffin et al. (2020) is to cultivate mushrooms using modern agri-culture method that is linked to the IoT. Other farmers may be able to produce various types of seasonal vegetables or crops by utilizing their smart process, thereby increasing the efficiency and productivity of their farm. Optimal temperature and perfect humidity are required for mushroom cultivat-ion. A pump is installed in the house to control the temperature and adjust the humidity levels inside. 

The climate control system is automatically included in the IoT control box. The water pump, which in-cludes an exhaust fan, is powered by electricity. It must have sensors like as humidity, water level, and temperature sensors that are linked to an internet gateway via a wireless fidelity network system. This gateway is resistant to ISP connections such as Asymmetric Digital Subscriber Line (ADSL) or Long-Term Evolution (LTE). They can expertly utilize the MCU ESP8266 as a microcontroller to regulate the climatic system.The writers of Yaseen et al. (2020) addressed certain agricultural criteria like Water Administration, Climate Data, and Sun-powe-red Vitality. A temperature sensor, mugginess sen-sor, dampness sensor, a Wi-Fi module (ESP8266 module), and other electrical devices were included in their projected keen green development. When the sensors degree falls inside the physical boundaries of the surrounding environment, the analog flag is changed to a computerized flag. In Sarma et al. (2020), the NodeMCU received moisture, rate of humidity, and level of temperature readings from Arduino through a serial connection, as well as LDR values from sensors, and then transferred the data to Firebase (a Google database). The authors offered a work that could determine the real-time wetness of the soil with humidity, the level of temperature in a specific region, the lowering light intensity, the nit-rogen (N), phosphorus (P), and potassium (K) con-tent of the soil, and a climate report for the next five days.The study by Anupama et al. (2020) is pri-marily concerned with a water management sys-tem and storing a certain amount of water so that the farmer can use and reuse it as needed. The entire process is fully integrated with smart IoT. This kit includes soil, coriander seeds, and a plastic tray. Soil is well mixed and strewn into the pan. Water is being saved at the farms base. When water is required, the plants absorb it, and the excess water drains into the farms foundation. In this farm, a moisture sensor collects data and determines how much water is required in the farm air. Water supplied to the farm depends on the sensor measurement, which is referred to as a Predefined Threshold Value (PTV). The Liquid Crystal Display (LCD) is utilized for live monitoring of the farms present state. Santos et al. (2019) introduces a novel agricultural method. This research taught us how Wireless Sensor Network technologies and mobile computing platforms might assist farmers to increase their output. Santos et al. (2019) also mentioned how future planning and time liness aid in improved productivity, allowing a farmer to take the appropriate actions. The end phase of this papers prototype model is the proposed agri-cultural production method. This model can forecast the present temperature, soil, human population, and air pressure. This might mean that the entire modern agricultural model is built on a low-power WAN method and an agriculture pre-diction server. Data is transported from the low-power WAN to the Long-Range Radio (LoRa) Low-power Wide-Area Network (LPWAN) during this operation. To avoid any form of anomaly, the methodology additionally included a 24x7 alarm system and a sort messaging mechanism. 

A new method to modern agriculture method using IoT and Extreme Learning Machines (ELM) has been pro-posed by Kale et al. (2019). This research explored plant disease (PD) and nutrient deficiency (ND) prevention, as well as food preference. Some sensors capture the essential data, which is then used to determine the amount of fertilizer to be applied. Because unbalanced fertilizer reduces production value and is far more effective for human and animal health. The initial step is to capture some photo-graphs using any type of digital camera. The humi-dity and temperature sensors are then used to gather data, which is then immediately linked to the Arduino. Collected data is stored in a database. The data was then begun to be evaluated. The approach was modified to work with a high-dimensional biological dataset.

The developed system is divided into two sections: Section 1 (Automated Sensor-Based Smart Farming) and Section 2. (Automated LDR-based Lighting Sys-tem). The suggested systems heart is surrounded by the main hardware known as Node MCU ESP-8266. NodeMCU is ideal for developing open-source hard-ware projects like this work. This hardware is mostly utilized for circuit board effectiveness and is linked with a USB cable controller and a tiny set-up board containing the MCU. The DIP (Dual In-line Pack-age) configuration allows for clean prototyping on breadboards. The Node MCU is directly program-med with a one-of-a-kind program that implements the system. All sensors are directly linked to the NodeMCU. Fig. 2 represents the block diagram.

Fig. 2: Block Diagram of proposed system.

Section 1 (Automated Sensor-Based Smart Farming) collects and combines data from all sensors for the environment, which is subsequently read by the NodeMCU. When NodeMCU evaluates the rate from the Temperature sensor and finds it to be more than or equal to 35-degree calculus, the motor is on; otherwise, the motor is turned OFF. When the rain sensor senses rain, the motor pump turns OFF. Additionally, it notifies the program user that "Rain Detected." When NodeMCU analyses the PIR Mo-tion sensor data and finds an external object in the region, it notifies the application with the message "External object identified." Wi-Fi is used to operate the total system by a user of a mobile application. Furthermore, this suggested systems principal energy source is the Solar Power system. In Section 2 (Automated LDR-based Lighting System), an LDR sensor is installed to identify the sunlight. When the LDR detects high intendancy sunlight, the sun is approaching; nevertheless, the light is turned OFF at this moment. The light is ON when the LDR detects a low intendancy light, indicating that night is ap-proaching. Section 2, the auto lighting unit, repre-sents the major part of the work. Fig. 3 shows the working procedure of this proposed system.

Fig. 3: Flow-chart of proposed system.

Section 1

Automated Sensor-Based Smart Farming

This Automated Sensor-Based Smart Farming sec-tion is divided into multiple components. Each unit is described in detail below (Al Mamun et al., 2021).

Temperature and Humidity Unit

The DHT22 generates a calibrated digital receiving signal. To maintain dependability and stability, it employs proprietary digital signal collecting tech-nology as well as moisture detecting technology. Its sensor parts are linked to a single-chip computer with an 8-bit processor. Every sensor in this model is temperature adjusted and calibrated in a precision calibration chamber, and the calibration coefficient is saved in the OTP memory in the program type; when the sensor detects, the coefficient is retrieved from memory. Fig. 4(a) depicts a graphical depiction of DHT22, and Fig. 4(b) depicts pin details of DHT22.

Fig. 4: (a) Graphical representation of DHT22, and (b) Pin details of DHT22.

Soil Moisture Unit

Soil moisture sensors detect or estimate the rate of the presence of water in the soil. These sensors might be fixed or mobile, such as hand-held probes. 

Stationary sensors are set in the field at predeter-mined locations and depths, whereas movable soil moisture probes assess soil moisture at several sites. When a crop begins to experience stress, it is critical to understand the condition of the soil water content for irrigation scheduling. Most crops begin to expe-rience stress when soil water depletion/deficiency reach 30-50 percent of Accessible Water-holding Capability (AWC). This is also called the irrigation trigger point or acceptable Management Depletion (MAD). Fig. 5(a) depicts a graphical display of the Soil Moisture Sensor in Fig. 5(b) depicts module details for the Soil Moisture Sensor.

Fig. 5: (a) Graphical representation of Soil Moisture Sensor, (b) Module details of Soil Moisture Sensor.

Rain Drop Unit

A raindrop sensor is a rain detecting instrument that always brings two modules: a rain board that detects rain and an impact module that compares the analog value and transforms it to a digital and modern value. In vehicles, a raindrop sensor is used automa-tically control the wipers. Fig. 6 depicts the raindrop sensor, which shows the metal strips which have been left exposed. This metal possesses Ohmic resis-tance that is bound. When this metal is dropped into water, its resistance changes. The more exposed it is to water, the more physical phenomena it exhibits or the less resistance it exhibits. When a raindrop falls into the sensor this time the resistance power of the sensor decreases. According to Algorithm 1, the NodeMCU immediately monitors this data and sends a short SMS to the user through Wi-Fi. When the user receives a rain notice, he or she will have clear information about his or her farming area and be able to take the appropriate actions.

PIR Motion Unit

PIRs show an electrical phenomena device in Fig. 7(a) as the spherical metal container with an oblong crystal in the middle), which can detect quantities of infrared radiation. An extremely motion detectors sensor is divided into two parts. The two parts are connected in a way that they cancel each other out. When it detects a particular level of infrared radi-ation, it may communicate data to the NodeMCU through Wi-Fi. According to Algorithm 2, the sys-tem alerts the user via a brief message on the users smartphone. Fig.7 (b) depicts the specifics of the PIR motion sensor module connection.

Water Irrigation Unit

A well-designed irrigation system serves as a blue-print for implementing a modern farming strategy. Irrigation of water is critical to the total farming pro-cess. Proper irrigation aids in the cultivation of pro-per growth, mitigating the negative effects of limited 

Fig. 6: Raindrop Sensor with Pin details.

Fig. 7: (a) PIR Motion Sensor, (b) Pin details of PIR Motion Sensor.

rainfall. This proposed work is based on the DHT22 temperature with humidity and soil moisture sensors. The DHT22 is a low-cost humidity meter that mea-sures humidity levels from 0 to 100 percent with a 3 to 5 percent accuracy rate. It contains a temperature range of -35°C to 80°C and an accuracy level of (+/)

0. 5°C. Soil moisture sensors determine the total amount of water in the soil. Both sensors are wired directly to the NodeMCU board. When the soil water level is lower than 25% and the rate of the tem-perature is upper than or equal to 35°C, the Node MCU monitors those data and displays sensor data on the smartphone. When a user sends a short signal to the relay module to activate the water valve, the DC water valve opens & irrigates the land as needed.

Power Supply Unit

The sun, as a great source of power, offers a remark-able characteristic. Sun can give coherent and extre-mely renewable energy to all humans on the planet. Solar photovoltaic (PV) modules convert solar power into electric power. We employ solar panels as the power source for the entire system to make this proposed system echo-friendly and cost-effec-tive. The solar panel is linked directly to the relay module. The total system has no exterior parts, which reduces human labor and maintenance expen-ses. Fig. 8depicts the physical voltage measurements 

of a 12V dry cell and (b) a solar panel, while Fig. 9 depicts a comparison between them.

Fig. 8: (a) Voltage measurements of a 12V dry cell, and (b) Voltage measurements of a solar panel.

Fig. 9: Comparison between 12V dry cell and solar panel.

Section 2: Automated LDR-based Lighting System

Plants require light sources for energy and life. Pho-totropism occurs when plant growth hormones begin to accumulate in the plants stem, leading the plant to face the light. Section 2 of the proposed system is divided into many components, which are described below. As a standard guide, most plants need illu-mination for 10 to 14 hours every day. However, numerous factors impact how much light is required because the sun does not give the same quantity of brightness throughout the year. As a result, mana-ging the lighting for an intelligent IoT-based intel-ligent farming system comprises an important regu-lation for the entire system. It is always noticed that darkness is also required for plant photosynthesis. 

Table 1 shows how the light varies depending on any type of plant.

Table 1: Lights for various types of plants.

Lighting Unit

We have concentrated on plants that need above than 12 hours of illumination for healthy development. Farmers in the modern world aim to cultivate a var-iety of crops or plants where light is always needed the major of the time. Some plants, such as dragon fruit, orchids, and Cactaceae, require just a small amount of darkness to blossom, and the rest of the time, constant illumination is necessary. To provide 24-hour illumination, we developed a new system that combines an LDR (Light Dependent Resistor) sensor with some additional user-friendly aspects. Section 2s working technique is described in Algo-rithm 4.

Experimental Setup for Implementation

This section describes how to put this proposed sys-tems design process into action. This suggested sys-tems setup is separated into two sections: Section 1 and Section 2. A Blynk software has been created to let users view.

Device Setup of Section 1

Fritzing software has been used to create Section I, which is divided into numerous segments. Fig. 10 depicts the Section I device configuration (Automa-ted Sensor-Based Smart and modern Farming).

Fig. 10: Device configuration of Section I.

Device Setup of Section 2

Design of Section 2 has been created with the big support of the Proteus software, which is divided into numerous sections. A Section 2 device config-uration is seen in Fig.11. (Automated LDR-based Lighting System).

Fig. 11: Device configuration of Section 2.

Blynk Software Development

Blynk is an IoT platform that facilitates the creation of updated IoT devices. It can read, store, and dis-play sensor data as well as remotely operate devices. It also functions as a control panel for seeing and manipulating devices and it is available for both An-droid and iOS. The program has a fairly functional UI and a variety of widgets for various uses. It operates on its currency, known as energy. Blynk always contains two parts: the Blynk Server and the Blynk Library. Through its server, Blynk Server provides a safe, responsive, and centralized cloud service that facilitates communication between de-vices. Blynk Server is also open source; it also al-lows users to create their servers.

In Fig.12, this work attempt to depict the entire sys-tem in green. A simulated agricultural setting, com-plete with sensor and auto lighting systems has been created. The solar panel worked as the systems power source. Furthermore, this work utilizes Node-MCU as a microcontroller, which is likewise ins-talled. This microcontroller is directly linked to each sensor. The system determines whether the pump should be turned ON or OFF by analysing the real data. However, when the sunshine is coming, the LED light is turned OFF, and when there is dark-ness, the LED light is ON.

Fig.12: Implementation of proposed system.

Fig. 13 illustrates a graphical representation of the temperature changes in the different time zone of a day. It signifies that the temperature is low in the morning. Furthermore, the temperature is nearly at its peak in the evening. Fig. 14 attempts to depict graphically how the humidity changes at different times of the day. It denotes that the humidity is low in the morning. However, it is gradually expanding. Furthermore, the temperature is close to high in the evening period, and the humidity is also at its peak.

Fig. 15(a) depicts the sensors detected data. The water pump may control by turning it ON and OFF depending on the soil-moisture value. When the value of the soil moisture rises, it will display a noti-fication, which is seen in Fig. 15(b). Similarly, if an external item is detected, it will trigger a notice in this Blynk Software via the PIR Motion Sensor, as seen in Fig. 15(c).

Fig. 13: Temperature reading from DHT22.

Fig. 14: Humidity reading from DHT22.

Fig. 15: (a) Blynk software interface, (b) Notification when soil is dry, and (c) Notification when external object detect.

Table 2: Comparison between proposed systems with other existing works.

Comparison with Similar Approaches

This section compares the system to other systems that are already in existence. Table 2 shows the com- parative analysis based on different parameters. These comparison tables display that the suggested system outperforms the existing models.

This work attempted to integrate an innovative technology known as the IoT Technology into this suggested system, with several fundamental elements of modern farming (IoT). This research work has developed some new methods while avoiding the influence of animals and other exterior objects on their farms. A farmer executes this technology to monitor rain conditions by employing a rain detect-ion sensor. The authors are also concerned with certain key concerns such as preventing future food crises, watering the land, improving the plants growth value, reducing manpower, employing new ways of soil mixing, auto-regulating by mobile app, conserving electricity, and providing 24-hour illumin ation. The proposed systems major goal is to make farming more intelligent, eco-friendly, and user-friendly in a systematic manner. The authors hope that, shortly, this suggested approach will follow the way for all other individuals throughout the world to make agricultural practices more accessible and efficient.

This work was supported in part by the “Center for Research Innovation and Transformation (CRIT)” of Green University of Bangladesh.

All authors declare that they have no conflicts of interest to disclose.