Development of IoT-based Drip Irrigation System for Tobacco Crops Using Fuzzy Logic: A Case Study in Indonesian Agriculture

Agricultural sustainability is crucial for rural communities, especially in Kampung Gisi, where celery farming is a primary source of income. However, farmers face challenges in manual irrigation, which is labor-intensive, time-consuming, and inefficient in water use. This study aimed to design and implement a solar-powered automatic irrigation system to improve water efficiency and reduce the workload of farmers. The system integrates a solar panel, ESP32 microcontroller, soil moisture sensor, relay switch, and DC pump to automate watering based on real-time soil moisture data. The system was developed and tested in Kampung Gisi, where field trials showed a 30% reduction in water usage compared to manual irrigation. Farmers actively participated in the implementation, gaining technical knowledge and ownership of the system. The results demonstrated the system's effectiveness in improving irrigation efficiency, reducing labor demands, and promoting sustainable agricultural practices through the use of renewable energy. This study highlights the importance of technology in empowering farming communities and suggests future efforts to enhance accessibility and scalability for broader adoption.Agricultural sustainability is crucial for rural communities, especially in Kampung Gisi, where celery farming is a primary source of income. However, farmers face challenges in manual irrigation, which is labor-intensive, time-consuming, and inefficient in water use. This study aimed to design and implement a solar-powered automatic irrigation system to improve water efficiency and reduce the workload of farmers. The system integrates a solar panel, ESP32 microcontroller, soil moisture sensor, relay switch, and DC pump to automate watering based on real-time soil moisture data. The system was developed and tested in Kampung Gisi, where field trials showed a 30% reduction in water usage compared to manual irrigation. Farmers actively participated in the implementation, gaining technical knowledge and ownership of the system. The results demonstrated the system's effectiveness in improving irrigation efficiency, reducing labor demands, and promoting sustainable agricultural practices through the use of renewable energy. This study highlights the importance of technology in empowering farming communities and suggests future efforts to enhance accessibility and scalability for broader adoption.

The absence of a water drip rate control system in drip irrigation systems has impacted water use efficiency and normalization of soil moisture. Therefore, this research aims to develop an intelligent system using the fuzzy logic method to control the rate of water droplets in a drip irrigation system and maintain soil moisture in normal conditions. The DHT22 sensor is used to obtain temperature and humidity values, which are then used as input data and processed by the ESP32 microcontroller, which includes a fuzzy system. The Internet of Things (IoT) is also used to send data from the microcontroller to the Thingspek web server. The Blynk application is used to make it easier to monitor temperature, humidity, and water droplet rate values. The results of this research show that the temperature accuracy values produced using the MSE evaluation were 6.66667 and RMSE were 2.58199, while for temperature, the values for MSE were 0.128333 and RMSE were 0.358236. The average value of soil moisture produced in the planting medium is 44.46%; this value is within normal conditions for chili plants, where normal soil moisture conditions range between 40% - 60%

The limited availability of water on plantations at the top of the mountain becomes an obstacle when farmers want to irrigate the field. It makes farmers relying on dammed rainwater in rainfed areas. However, it has not solved the main problem because farmers need to make a proper use if their dammed water so that it could last the entire year. Thus, a new irrigation system technology is proposed. The purpose of this project is to design a drip irrigation system that could be controlled via Internet of Things (IoT) technology to raise the efficiency of water usage in plantations using solar panel energy. Design used in this drip irrigation system consists of hardware and software design, including system development. As for the method used here is IoT based. This drip irrigation system could monitor plant conditions in real-time. NodeMCU is used to send information and automatic valve controller with soil humidity as its parameter. With this tool, farmers are expected to be able to monitor soil humidity, pH and plantations conditions. Results show that soil humidity sensor has an average reading error of 0.364% and soil pH sensor has an average reading error of 0.378%. Solar panel testing shows that at 11:00 GMT the accumulator output reaches a maximum power of 8.821 Watts, while its standard output power is 480Wh. The battery can be full for 54.415 hours. The camera module can take pictures well. The drip irrigation system can run well when the humidity value is <70%.

Water resource usage should be optimized as there is always a scarcity. This paper aims to provide an efficient way of water using sense and weather data and implementing a fuzzy decision model. An automated intelligent watering system is proposed in this paper using the internet of things and fuzzy logic. The weather data, coupled with Temperature, Relative Humidity, and soil moisture sensor data, is used to decide whether to switch on/off the motor. In-house-created prototypes of ground-moving robots have soil moisture, digital humidity, and temperature sensors implanted in them. The soil moisture sensor is attached to the Rack and pinion mechanism. The soil moisture sensor is pushed into the soil when the pinion rotates. It minimizes the use of sensors by using a distributed sensing method. Based on data obtained from sensors and meteorological information, the system will use this information to decide whether to Switch on/off the sprinkler motor. A fuzzy logic-based system decision is implemented on the input sensor and weather data, and the model will decide to switch on/off the actuator. An accuracy of 97% is achieved. The Android app is used to visualize sensor data, based on which the farmer can manually control the motor.

Electricity is one of the main sources of energy that is very important in everyday life, both for household needs, government agencies, and industry. To strengthen the implementation sustainable development goal by reducing the use of fossil fuel, the utilization of solar energy has great potential, though the efficiency of solar panels or solar energy technology is still relatively low, especially in Indonesia. Located in tropical region, the solar panel module receives solar irradiation that varies due to changes in weather or local environmental conditions, partial shading will occur, making the solar panels partially covered by shadows. This situation will result in a decrease in the output power of solar panels To overcome this issue, a battery is used to store the generated energy. To maximize the potential, the battery needs to be charged optimally which need a control algorithm to provide energy gathered from the solar panel, most of the time. Therefore, a maximum power point tracking (MPPT) is necessary to be associated with an algorithm to optimally control the performance of the solar energy harvesting scheme. In this study, both Mamdani and Sugeno Fuzzy Logic Algorithm are used in the MPPT with a buck converter at a solar panel with a battery. Buck converter is chosen to give safety charging margin to the battery since the converter's output voltage is lower than the input voltage. As for the fuzzy logic algorithm, Mamdani's Fuzzy Logic has the advantage of producing more accurate decision results than Sugeno's type. While, Sugeno's Fuzzy Logic has the advantage of using simple mathematical calculations in its design. In addition, a buck converter was also used to match the voltage generated by the solar panel to match the battery specifications. The system design and testing are carried out using Matlab R2018b Simulink. From the simulation, the Mamdani Fuzzy Logic-based MPPT has the same maximum power point tracking computation time as Sugeno's Fuzzy Logic. In the partial shading test conditions, MPPT based on Fuzzy Logic has a higher efficiency value of 94.50% when compared to MPPT without control which is only 89.56%. Under various conditions of irradiation and temperature, MPPT based on Fuzzy Logic has a higher efficiency value of 94.88% than MPPT without control which is only efficiency of 91.53%.

Managing water resources in urban areas is relatively expensive due to the costs of electricity and water distribution from wells and water companies. Therefore, water resource management for urban agricultural purposes needs to be made efficient, such as through smart irrigation technologies, one of which is the drip irrigation system that engages soil moisture sensors and the Internet of Things (IoT) to control the amount of distributed water. This study aims to apply and evaluate the performance of a drip irrigation system based on soil moisture sensors and IoT in urban agriculture. The results showed that the distribution uniformity in the system was identified at fair levels, with a Coefficient of Uniformity (CU) of 90.15% and 86.58%, respectively. Furthermore, our study also found that the IoT-assisted drip irrigation system that engaged a Deep Neural Networks (DNN) model to meet the water requirement led to better peanut yield than the irrigation system based on soil moisture as a control.

Abstract: The integration of Internet of Things (IoT) and Big Data analytics is revolutionizing agriculture by enabling data-driven decision-making and precision farming. This paper presents a comprehensive implementation framework for IoT and Big Data in agriculture, focusing on real-time data collection, advanced analytics, and actionable insights. The proposed system architecture comprises four layers: sensing, communication, data processing, and application, which work together to optimize resource use, enhance crop yields, and improve farm management. IoT devices such as soil moisture sensors, drones, and smart irrigation systems collect vast amounts of data, while Big Data analytics processes this information to provide predictive insights and recommendations. A case study demonstrates the practical benefits of this framework, showing a 20% increase in crop yield and a 30% reduction in water usage. However, challenges such as high implementation costs, technical complexity, and farmer adoption remain. Future enhancements, including the integration of AI, blockchain, and 5G, are discussed to address these challenges and further advance smart farming. This paper highlights the transformative potential of IoT and Big Data in agriculture, offering a roadmap for researchers, policymakers, and farmers to harness these technologies for sustainable and efficient farming practices.

An intelligent IOT sensor coupled precision irrigation model for agriculture

Automatic irrigation is not new, this method has been invented by mankind to irrigate large areas of land through drip irrigation systems. The system is implemented to reduce water wastage in irrigation. In greenhouse irrigation control, computerized control is very important to increase productivity. On the other hand, conventional irrigation control in greenhouses is not effective, because it is based on on-off or proportional control. This paper presents a solution to control irrigation time duration based on fuzzy logic method. Fuzzy logic controller (FLC) was developed using the Mamdani method. FLC is built on the NodeMCU ESP8266 board mounted with a DHT22 and soil moisture sensor. Temperature and water content in the soil parameters are used as input for fuzzy logic to determine the duration of irrigation time. The linguistic values used as fuzzy membership functions include soil moisture (water, wet, dry), temperature (cold, normal, hot), and watering time (zero, short, medium, long). Based on the membership function, 9 fuzzy rule bases are determined. The testing results on fuzzy logic built on NodeMCU ESP8266 with fuzzy logic built on MATLAB software obtained an average error of 0.59%.

In this article, the Moroccan climate is currently undergoing several changes that have negatively affected agricultural activities, especially in the field of irrigation. That's why we relied on internet of things (IoT) technology to overcome these problems. In this article, we present the design and realization of an intelligent irrigation system via a solar submersible pump. We used the ESP32 microcontroller which reads the temperature and humidity values measured by the soil moisture sensor. The communication between the blocks is ensured by the radiofrequency module. We have implemented a Web server to monitor the measured quantities. The graphical representation of the data will be ensured using the ThingSpeak platform which makes it possible to store and collect the data coming from the sensors via the hypertext transfer protocol (HTTP). Our achievement was executed and tested without any problems detected, which shows that our smart irrigation study was very successful.

Real-time sensors for precision irrigation schedulating are used for enhancing water efficiency and optimizing resource usage. Poor resource management can negatively impact traditional farming practices, particularly in regions limited by water shortages. Agriculture is susceptible due to its heavy reliance on water resources. Due to global warming and its potential impacts, there is a growing emphasis on developing strategies to ensure a steady water supply for food production and consumption. As a result, research on reducing water usage in irrigation systems needs to be implemented. While traditional commercial irrigation sensors are often too expensive for smaller farms to adopt, manufacturers are now producing affordable alternatives that can be integrated with network systems to provide cost-effective solutions for efficient irrigation and agricultural monitoring. To minimize a farmer’s efforts, an Internet of Things (IoT)-based drip irrigation system is proposed in this work. Initially, the required data is collected using the IoT sensors. The gathered data is fed into the Adaptive Residual Hybrid network (ARHN) that is developed by using the Spatial Autoencoder and Stacked CapsNet. Here, the Modernized Random Variable-based Frilled Lizard Optimization (MRV-FLO) is utilized to tune the ARHN parameters. Therefore, the required water from the pump for the crops is provided by the ARHN model. In addition, this model makes the work simpler and avoids the wastage of water in the agricultural environment. Finally, the performance of the developed framework is validated over the existing works to prove the efficiency of the recommended method. The main experimental findings of the developed model achieve 99.24% and 97.32% in terms of accuracy and RMSE. Moreover, the statistical findings of the developed model shows 41.9%, 34.9%, 36.0% and 37.1% better performance than LEA-ARHN, FDA-ARHN, AOA-ARHN and FLO-ARHN in terms of best measure. Based on this performance enhancement, the developed model can effectively reduces the farmer’s effort and improves the crop productivity in the agricultural sectors.

This study aims to develop and implement an air quality monitoring system using Internet of Things (IoT) technology and Mamdani fuzzy logic. The system integrates sensors to detect PM2.5, PM10, and CO concentrations. Real-time data is processed using fuzzy logic to generate an easily understandable Indeks Standar Pencemar Udara (ISPU). Testing showed 95% accuracy in ISPU measurement, 2-second response time, and 99.5% uptime over 30 days. The Mamdani fuzzy logic effectively handles uncertain data, providing accurate air quality interpretations. The system classifies air quality into different ISPU categories (Good, Medium, Unhealthy, Very Unhealthy, Dangerous) in real-time. The study concludes that integrating IoT and fuzzy logic yields a high-performing, reliable air quality monitoring tool, significantly aiding pollution mitigation and public health. Further research is recommended to enhance algorithms and integrate additional technologies for improved functionality and accuracy.

Agriculture is vital in human evolution and was the first activity to be emphasized ever since the beginning of time. With the population growing constantly, there are inventions of new means in the production of food to cater for those demands. Improvement in a variety of technologies is one of such effort conducted for the cause. Robotics or chemical technologies may not be the only improvements that could be exercised. Internet of Things (IoT) technology is one of an application widely used currently. The study aims to establish a less manpower plantation in smart city with the use of IoT technology to improve the crop cultivation. In preliminary, a wireless soil moisture monitoring and irrigation system was developed. The system aims to monitor the moisture and properties of soil for plants. At the same time, with a self-sufficient and self-organized irrigation system based on the water-control algorithm. The developed system covered the three layers in IoT architecture: perception layer, network layer and application layer. In perception layer, a microcontroller, soil moisture sensors and solenoid valves acted as the sensors, transducers and actuators. Wireless networking technology (WiFi) was used as the communication for data transmitting and receiving. Through the developed application, humidity and irrigation volume were collected, recorded and analyzed. These preliminary results help in visualizing the concept of a less manpower plantation in smart city.

In 2024, air pollution levels in Jakarta were recorded as the highest in Southeast Asia, based on various air quality monitoring reports. This condition has become an increasingly alarming environmental issue, with pollution levels frequently exceeding the safe threshold set by the World Health Organization (WHO). One of the efforts by the Jakarta Provincial Government to address this problem is a free plant distribution program for the public. Plants play a crucial role in absorbing carbon dioxide, producing oxygen, and reducing airborne pollutant particles. However, plant maintenance—especially watering—poses its own challenges. Inefficient watering can cause plants to experience stress, wilt, or even die. With the advancement of technology, an automatic plant watering system based on the Internet of Things (IoT) presents a potential solution to improve the efficiency and sustainability of plant care. This study aims to develop a smart plant watering system application based on IoT that can automatically control watering based on real-time soil moisture levels. The system was applied to spider plants (Chlorophytum comosum) grown in pots with a diameter of 15 cm. By using an ESP32 microcontroller, a soil moisture sensor (Capacitive Soil Moisture Sensor v1.2), an air temperature and humidity sensor (DHT11), and a water pump, the system automatically activates watering when the soil moisture is below 55% and stops when it exceeds 65%. Sensor data is stored in a database and displayed through a web-based application for remote monitoring.

This drip irrigation system is a special water supply system in the root area.so that when carrying out irrigation activities, you can use water with a small discharge because it is only needed in the root area, in addition, technology greatly facilitates humans in various fields, one of which is agriculture. Therefore, with the internet of things (IOT) method, the drip irrigation system can be modified into automatic drip irrigation where the importance of this automatic drip irrigation system is in agriculture for sustainable agriculture in areas with limited water sources.The research method is experimental with a microcontroller as a control system, a relay as an LCD automatic switch as an output system and a sensor as a soil moisture reader. the results showed significant results where experiment 1, experiment 2, experiment 3, experiment 4, experiment 5 soil moisture sensor at 10-90% water percentage the pump status was on and off at 100% water percentage or soil saturated with water conditions.