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This study discusses the design and testing of a growing media moisture and water level monitoring system based on ESP32-MQTT. The system was developed to support real-time monitoring of growing media conditions and water availability through a monitoring dashboard. This research used an experimental method with a design and implementation approach by developing an ESP32 circuit connected to a capacitive soil moisture sensor and a water level sensor, then sending sensor data to the dashboard through the MQTT protocol and Node-RED. Testing was carried out using a plant pot as the moisture testing medium and a water container as the water level testing medium. The results showed that the growing media moisture sensor displayed 0% in dry and very dry soil conditions, 61% in moist soil, and 89% in very moist soil. The water level sensor displayed 0 cm, 2.4 cm, and 4 cm according to the testing conditions. These results indicate that ESP32-MQTT can be used as an initial prototype for monitoring growing media moisture and water level before being applied to a complete hydroponic or aquaponic system.

Soil strength characteristics are strongly influenced by its physical and mechanical properties, one of which is shear strength. Soil shear strength is affected by cohesion (c), internal friction angle (ϕ), and soil moisture conditions. In open-pit mining conditions, soil moisture content is greatly influenced by rainfall and water seepage, which can increase the degree of saturation within the soil mass. An increase in the degree of saturation generally leads to a rise in pore water pressure, thereby reducing the effective normal stress and resulting in a decrease in soil shear strength. This study aims to determine the effect of the degree of saturation on soil shear strength. This research employs a quantitative method to analyze the influence of the degree of saturation under three conditions (natural, dry, and saturated) on soil shear strength through laboratory testing using the direct shear test. The tests conducted include soil physical properties testing in accordance with SNI 1965-2008, specific gravity testing based on SNI 1964-2008, and soil shear strength testing following SNI 3420-2016. The results indicate that the average degree of saturation under natural conditions is 64.63% with a cohesion value of 7.4 kN/m², under dry conditions is 33.18% with a cohesion value of 8.2 kN/m², and under saturated conditions is 83.08% with a cohesion value of 3 kN/m². It can be concluded that a higher degree of saturation or more saturated soil samples result in lower cohesion values, whereas a lower degree of saturation or drier soil samples lead to higher cohesion values.

Improper water management in rice cultivation can lead to water stress, which reduces productivity. Conventional monitoring has limitations on large-scale lands, necessitating more efficient remote sensing technologies. This study aims to develop a water stress identification system for rice plants in the late vegetative phase using multispectral drone imagery integrated with an Artificial neural network (ANN). The research method employs an experimental approach with six water availability levels in Karyamukti Village, Sumedang. Field reference data were obtained through soil moisture sensors converted into Available Water (AW) values. Image processing stages included orthomosaic reconstruction, leaf object segmentation, and transformation of vegetation indices (NDVI, NDRE, GNDVI, etc.) as model inputs. The results show that the ANN model with a four-hidden-layer architecture achieved training and validation accuracies of 94–95%. In the independent testing phase, the model produced an accuracy of 94.60% with an F1-Score of 93.33%. Spatial visualization of the prediction results indicates a consistent water condition distribution across rice plots. In conclusion, the integration of multispectral drones and ANN provides an accurate non-destructive solution for spatial monitoring of water availability in rice plants.

This study aims to design, implement, and test a prototype that automates three functions, namely watering, fertilizing, and pest control based on Arduino Uno with the ability to directly monitor soil moisture and pH. This system is equipped with four main types of sensors. Soil condition monitoring involves an FC-28 soil moisture sensor and a soil pH sensor, water level measurement involves an HC-SR04 ultrasonic sensor, and pest detection in the plant area involves a RIP sensor. All data obtained from these sensors is then processed by the Arduino Uno microcontroller to automatically activate actuators such as water pumps, liquid fertilizer pumps, buzzers, and DC motors according to soil conditions and plant needs. Prototype testing was conducted on simulated land with various scenarios of moisture, soil pH, and pest activity. The test results revealed that the system was proven to be able to significantly optimize water and fertilizer utilization, as well as reduce pest disturbances that could potentially damage plants.  In addition, this system also displays the operational status directly through an LCD screen, making it easy for users to monitor. The advantage of this system is its multi-function integration in a single device that is cost-effective and easy to operate. In the future, the functionality of this system can be improved through integration with Internet of Things (IoT) technology, enabling remote monitoring and control with greater efficiency. More broadly, this study is expected to support increased production and sustainable agricultural practices in Indonesia.

Expansive clay soil is soil that can expand and contract significantly in response to changes in soil moisture content. This study used an experimental method to stabilize expansive clay soil using a mixture of gypsum waste powder, which was tested using the Atterberg test, the Unconfined Compression Strength test, and the California Bearing Ratio test with mixture variations of 0%, 10%, 20%, and 30%. The results showed that the addition of gypsum waste powder could reduce the expansivity level of the soil from a very high level of 42% to a moderate level of 20%, increase the value in the Unconfined test at a maximum mixture of 10%, and increase the value in the CBR test at a maximum mixture of 30%. Based on the above description, this study aims to determine the extent of the effect of gypsum waste powder on expansive clay soil on the bearing capacity and compressive strength of expansive clay soil.  

Household waste management, especially organic waste, remains a major problem in many regions, including Pematang Guntung Village, Teluk Mengkudu District, Serdang Bedagai Regency. Improperly managed organic waste has the potential to cause unpleasant odors, environmental pollution, and become a source of disease. Therefore, an appropriate and environmentally friendly solution is needed to overcome this issue. This study aims to examine how the application of the biopore method can serve as a strategy to create a cleaner environment while simultaneously improving soil fertility. The research method used was qualitative, with data collection techniques including observation, interviews, and documentation. The data obtained were analyzed descriptively to illustrate the implementation of biopores in the community. The results showed that the application of biopores was able to significantly reduce the volume of household organic waste. Organic waste placed into biopore holes naturally decomposed into compost, which is beneficial as an environmentally friendly fertilizer. In addition, biopores improved soil water absorption capacity, which helps reduce waterlogging during the rainy season and maintain soil moisture. From a social perspective, biopores encouraged the development of new awareness among community members regarding the importance of waste management. This was demonstrated by the active participation of residents in creating, utilizing, and maintaining biopore holes sustainably. Thus, biopores function not only as a technical solution for organic waste management but also as an environmental education tool that strengthens community collaboration in maintaining cleanliness and enhancing soil fertility in their area.

The coffee farming sector in Gunungmanik Village, Indonesia, plays a significant role in the local economy. However, the monitoring and management of coffee crops remain largely manual and conventional, making it difficult for farmers to respond quickly to environmental threats such as drought, pests, or sudden temperature shifts. This research presents the development of iotgm.id, a web-based monitoring system integrated with Internet of Things (IoT) devices designed to provide real-time environmental data for coffee plantations. The system measures key parameters including temperature, soil moisture, and motion detection (as a proxy for pest activity), and delivers this data via a user-friendly web interface. It also features digital farm record management, real-time alerts for abnormal conditions, and data visualization through interactive dashboards. Field testing with local farmers showed that the system improves decision-making, speeds up responses to environmental changes, and reduces the need for direct field visits. Unlike earlier systems that often required technical expertise or focused on single parameters, this system offers multi-parameter monitoring and is accessible to farmers without advanced digital literacy. The system bridges the gap between sophisticated agricultural technologies and practical field-level application. It contributes to the adoption of precision agriculture in rural areas, offering a scalable model for broader implementation in similar contexts

This research aims to be able to meet the water supply of lettuce plants automatically by using three sensors such as soil moisture, water level, and water discharge. The goal is to provide water needs to plants automatically and regularly. The developed tool uses YL-96 sensor for soil moisture, HC-SR04 for water level and YF-S201 for water discharge. Sensor data is sent to the arduino to be processed using the fuzzy mamdani method so that these three data values affect the movement of the tap servo motor that flows to the lettuce plant. Fuzzy logic here as a decision maker from the value of 3 sensor data and then processed automatically by arduino using fuzzy mamdani to determine how many degrees the servo motor moves. The result is that the Lettuce Plant Water Needs Analysis System Automation Tool is able to maintain the water supply of lettuce plants and soil moisture ideally at 76% with a servo motor movement system success rate of 100%.

The food security program involving chili cultivation in Pentur Village is hindered by inefficiencies in water use and suboptimal plant growth, primarily due to traditional irrigation methods that fail to consistently maintain ideal soil moisture and temperature. This issue is exacerbated by unpredictable environmental shifts, such as fluctuating weather patterns, and a lack of precise irrigation control stemming from technological limitations. To address this, a system for monitoring and regulating chili plant irrigation using IoT technology was developed. This system employs humidity and temperature sensors connected to an IoT platform like Blynk, enabling real-time observation of plant and environmental conditions. Data on soil moisture, air temperature, and humidity are stored in a database, and irrigation is automated based on soil moisture levels. The goal is to enhance water efficiency, minimize risks associated with over or under-watering due to environmental variations, and improve both yield and quality of the chili crop. This IoT-based system aims to simplify chili plant management for Pentur Village farmers and significantly boost agricultural output.

Due to the busy activities outside the home, some people do not have time or forget to water their plants, so when they return home they find that the plants have died from drought. Therefore, to overcome this problem by conducting research using Internet of Things technology. The aim of this research is to design an automatic watering tool to overcome manual watering and support learning. This prototype uses a NodeMCU ESP8266 as the main controller, a soil moisture sensor is used to read soil moisture.  Soil moisture sensors are used to detect soil moisture. If the soil humidity is below the minimum limit, the watering process will be active and the watering process will be active if the NodeMCU ESP8266 receives commands from the smartphone.

Watering plants is an important activity that cannot be ignored in order to maintain sufficient air intake for optimal growth. Manual watering methods, which are commonly carried out according to a schedule, are often inefficient because they require significant allocation of time and energy. Improper watering techniques are one of the risks of plant growth factor failure, where unmaintained soil moisture can cause plants to wilt and die. This study aims to develop an automatic irrigation system based on the Internet of Things (IoT) that utilizes light energy as its power source, with the aim of being able to save time, lighten human work, and at the same time optimize the use of energy resources. This system is designed to monitor soil conditions in real-time and regulate watering automatically, supported by a sustainable light energy source. The results of this study indicate that the automatic watering system based on the Internet of Things (IoT) developed and powered by light energy is able to overcome the problem of plant drought effectively, while saving time and lightening human work in watering activities. 

This study aims to describe the application of Internet of Things (IoT) technology in optimizing soil health management through a systematic literature review. This research compares various IoT implementations for monitoring soil moisture, pH, and nutrients based on previous studies, and identifies differences in technological approaches, sensors used, and automation levels. The review results indicate that although IoT technology is proven effective for real-time soil condition monitoring and supporting precision agriculture, its implementation varies significantly between advanced systems with full automation developed internationally and simpler, local monitoring systems in Indonesia. The comparison shows that wireless inductive moisture sensors are superior in accuracy and corrosion resistance compared to conductive sensors. A TDS sensor-based hydroponic nutrient monitoring system demonstrated high accuracy with an average error of 4.7468% , while soil pH monitoring achieved an accuracy with a Mean Absolute Error (MAE) of 0.14. Furthermore, automated watering systems proved to reach a success rate of up to 93.75%. This review concludes that adapting low-cost wireless sensor system models has great potential for improving the efficiency of soil management in Indonesia, despite facing challenges in infrastructure and digital literacy.  

Soil moisture is a crucial factor in agriculture that affects plant growth and crop productivity. In modern agricultural systems, accurate soil moisture monitoring is essential for optimizing water usage and enhancing the efficiency of automatic irrigation systems. This study aims to develop an Internet of Things (IoT)-based soil moisture monitoring system and evaluate the performance of three types of soil moisture sensors: Soil Moisture FC-28, Capacitive Soil Moisture Sensor, and Soil Moisture Hygrometer Module Sensor. The evaluation compares the accuracy and effectiveness of each sensor in measuring soil moisture. The research methodology involves measuring soil moisture in ten different soil samples using the three sensors simultaneously. The system is based on the ESP32 microcontroller, where data from the sensors are processed and displayed on an LCD Liquid Crystal I2C 20x4. Data analysis is conducted using the one-way ANOVA statistical method to determine whether there are significant differences among the measurement results of the three sensors.The results indicate that each sensor exhibits different measurement characteristics based on its working principle. The Soil Moisture FC-28 sensor, which operates on resistance, shows high sensitivity to changes in soil moisture but is susceptible to corrosion. The Capacitive Soil Moisture Sensor is more durable as it does not have direct contact with the soil, yet it requires more precise calibration. Meanwhile, the Soil Moisture Hygrometer Module Sensor provides more stable results under various environmental conditions. The one-way ANOVA analysis reveals no significant differences in the measurement results among the three sensors.

Land degradation due to intensive agricultural practices is a serious issue threatening food security and ecosystem balance. Monoculture farming systems often lead to soil fertility decline, increased erosion, and reduced soil biodiversity. As an alternative, agroforestry has been developed to enhance agricultural productivity without compromising soil health. Agroforestry integrates agricultural crops with trees or other woody plants in a mutually beneficial system. Several studies have shown that agroforestry improves soil organic matter content, retains soil moisture, and reduces erosion and nutrient loss. This study employs a literature review method to analyze the effectiveness of agroforestry in improving soil health and agricultural productivity. Agroforestry has been found to enhance agricultural productivity through crop diversification, efficient land use, and climate change mitigation. Diversification not only strengthens food security but also increases farmers’ income by minimizing crop failure risks and enhancing nutritional value. Additionally, agroforestry optimizes land use by improving yield per unit area and reducing environmental degradation through natural resource conservation. Agroforestry also plays a crucial role in mitigating climate change by increasing ecosystem resilience, sequestering carbon, and reducing extreme weather impacts. However, the adoption of agroforestry faces challenges, including a lack of farmers’ knowledge, limited policy support, and varying environmental conditions. Addressing these barriers through improved education, supportive policies, and sustainable resource management can accelerate agroforestry adoption. With broader implementation and strong policy support, agroforestry can drive agricultural sustainability and enhance community well-being.

This study aims to design and develop a prototype of an Internet of Things (IoT)-based land watering monitoring system using the ESP8266 module and soil moisture sensor. This system is designed to help farmers manage watering automatically based on soil moisture conditions, so that they can save water and labor. The process at this time begins with a literature study, hardware and software design, to prototype testing on two soil conditions, namely dry and wet. Data from the sensor is sent in real-time to the IoT platform, if the soil is detected dry, the system automatically activates the water pump. The results of the test show that the sensor works accurately in distinguishing soil conditions, and the system is able to water automatically according to needs. Furthermore, this system can also be monitored and controlled remotely via the internet. The conclusion of this study shows that the use of IoT technology is very potential to increase efficiency and effectiveness in agricultural land management.

The integration of solar powered IoT irrigation systems in precision agriculture offers a sustainable solution to address water scarcity and enhance crop productivity. By leveraging real time data from soil sensors, weather APIs, and machine learning algorithms, these systems optimize irrigation schedules and improve water use efficiency. This research explores the potential of integrating renewable energy sources, such as solar power, with edge computing in smart irrigation systems to promote sustainable agricultural practices. The study aims to evaluate the performance of the proposed system in terms of water savings, crop yield, energy efficiency, and adaptability to varying climate conditions. Literature Review: Previous studies highlight the importance of smart irrigation systems in reducing water waste and improving crop yield through real time monitoring and automated decision making. However, existing systems often lack the integration of renewable energy and edge computing, which are critical for ensuring sustainability and operational efficiency in rural agricultural settings. The combination of renewable energy with IoT devices offers a promising solution to reduce energy costs and carbon emissions, while edge computing enhances real time data processing, ensuring prompt and accurate irrigation adjustments. Materials and Method: The proposed system integrates solar powered IoT devices, soil moisture sensors, weather data APIs, and edge computing devices to manage irrigation. Machine learning algorithms and evapotranspiration models are used to predict irrigation needs and optimize scheduling based on real time data. The system's performance is evaluated through metrics such as water savings percentage, crop yield improvements, and energy consumption, with a comparative analysis against traditional irrigation methods. Results and Discussion: The results indicate that the system successfully reduces water usage by 30% to 40%, increases crop yield by 25%, and operates with energy autonomy, powered entirely by solar energy. The system's adaptability to varying climate conditions ensures optimal crop growth, even under environmental stresses. The integration of renewable energy and edge computing significantly enhances the sustainability and efficiency of irrigation systems.

This research develops a prototype of an automatic watering device based on the Internet of Things (IoT) and a soil moisture sensor to improve the efficiency of watering crops in agricultural fields. The main problem addressed is the consistency and efficiency of watering in large agricultural fields, where traditional management is prone to human error and inefficient water use. The developed prototype uses an ESP8266 microcontroller (NodeMCU) as the brain of the system connected to a Wi-Fi network, allowing remote monitoring and control via the Blynk application. A soil moisture sensor is used to detect soil conditions in real-time, triggering watering only when needed. This helps optimize water use and increase agricultural productivity sustainably. This research outlines the methodological steps from design, manufacturing, to implementation of the prototype in the field. Evaluation of the results shows that the system successfully regulates watering effectively based on soil conditions, with positive responses from farmers as end users.

Fruit cultivation plays a crucial role in the agricultural economy, where seed quality significantly impacts productivity. Watering and fertilizing fruit plant seedlings are key aspects of ensuring healthy growth. To meet the demands of modern agriculture, an automated solution has been developed utilizing messaging application-based technology, such as Telegram. This device enables remote control of watering and fertilizing processes via the Telegram bot feature, ensuring precision and convenience. Advantages include time efficiency, optimal fertilization, and precise watering, supported by light sensors and soil moisture sensors to enhance the process. Experimental results show that this technology successfully addresses challenges related to irregular watering and fertilization. Consequently, this automated system has the potential to enhance productivity and efficiency in fruit cultivation.

In the era of agricultural digitalization, dependence on Internet of Things (IoT) technology is increasing, yet supporting infrastructure remains vulnerable to connectivity disruptions. This research aims to develop an alternative protocol for IoT-based agricultural systems that can operate during internet connection failures, focusing on implementing a failsafe mode using NodeMCU ESP8266. The research methodology includes developing a monitoring system using soil moisture sensors and ultrasonic sensors for water level detection, as well as performance evaluation based on decision-making accuracy parameters, mode transition response time, and consistency in water management. Implementation results showed a 40,1% reduction in water consumption compared to traditional irrigation methods. The failsafe mechanism demonstrated sustained operations with 98% reliability in maintaining optimal soil moisture levels during 72-hour offline periods. This research contributes significantly to the development of smart agriculture through a cost-effective and scalable solution, particularly for areas with limited infrastructure that only require basic internet connectivity and minimal maintenance.

This research aims to describe and compare the morphological characteristics of spores in Thallophyta (lichens) and Bryophyta (mosses). The research was conducted at the Biology Laboratory of UIN Sunan Gunung Djati Bandung for one week. This study employed a descriptive method with macroscopic and microscopic observations. Samples of lichens and mosses were analyzed using a light microscope. The results showed that Thallophyta spores have asymmetrical morphology, are pale green, have a count of 15 spores, and are adaptive to extreme conditions. In contrast, Bryophyta spores are round and flat, with a reddish-brown capsule, and a larger number of spores, namely 67. These differences reflect the unique adaptation strategies of the two groups to their respective environments. Lichens, which are a symbiosis between fungi and algae, serve as indicators of environmental quality and grow on various substrates, including polluted areas. Meanwhile, mosses play an important role in maintaining soil moisture, preventing erosion, and supporting water and carbon cycles.