RESEARCH ARTICLE
Development of a sandy soil water content monitoring system for greenhouses using Internet of Things
1Department of Agricultural Machinery Engineering, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea
2Department of Smart Agricultural Systems, Graduate School, Chungnam National University, Daejeon 34134, Republic of Korea
3Farm Machinery and Post-Harvest Process Engineering Division, Bangladesh Agricultural Research Institute, Joydebpur, Gazipur 1701, Bangladesh
Abstract
Precision water management is crucial for greenhouse agriculture to maximize crop yields in sandy soil. Due to the low water holding capacity, it is necessary to monitor the water movement in different depths of sandy soil to ensure effective irrigation. Therefore, this study aimed to develop a data acquisition (DAQ) system for sandy soil water content monitoring in an experimental soil bin inside a greenhouse, utilizing the capabilities of the Internet of Things (IoT). A drip irrigation system was implemented, arranged in four pipelines, spaced 60 cm apart, with drippers placed at 30 cm intervals along the pipeline. The overall system was installed in a sandy soil testing bin. A DAQ system was comprised of three basic units: sensor interfacing and circuit board, programming and sensor data acquisition, and data storage and monitoring. A microprocessor was used by interfacing a set of soil water content sensors, ambient temperature, and humidity sensors. The water content sensors were placed in the soil at different depths of 10, 20, 30, 40, and 50 cm, respectively. A microcontroller was used to collect and send the sensor data to monitor and store in memory. During the test, the maximum and minimum average of soil water content, ambient temperature, and humidity values were observed at 33.91±2.5 to 26.95±1.3%, 21.39±2.1 to 42.84±1.7°C, and 48.73±2.3 to 99.90±0.3%, respectively. The water content percentages were varied at different depths of sandy soil due to low water holding capacity. The developed automatic DAQ system would help with remote monitoring and control of greenhouse irrigation, considering the different crop characteristics and environmental conditions.
Keywords
Sandy soil, water content monitoring, greenhouse irrigation, Internet of Things
Introduction
The desire to increase the effectiveness and long-term sustainability of food production is what is driving improvements in agricultural techniques worldwide. In this era of changing climate and declining resources, the integration of modern technologies into agriculture has become essential (Ren et al., 2023). Greenhouse farming, in particular, has gained significant attention for its potential to provide a controlled environment for crop cultivation, allowing year-round production of high-quality crops. In terms of sandy soil, experiments in greenhouses provide valuable information about soil management practices that helps farmers identify the suitable conditions and techniques for successful cultivation of growing crops in sandy soil. The focus on greenhouse conditions contributes to the development of sustainable agricultural practices to address specific challenges related to sandy soil cultivation in greenhouse environments that boost crop productivity in arid regions. However, the success of greenhouse practices depends on the precise management of environmental parameters, where soil water content being a crucial factor that significantly affects crop growth and yield. Even though sandy soils drain efficiently, greenhouse farmers face unique challenges. Due to their low water retention capacity, these soils must be monitored and managed to avoid over- or under-irrigation (DeTar, 2008; Liao et al., 2021). Therefore, there is an urgent need for technological solutions that address the particular challenges presented by sandy soils in greenhouse environments.
Soil water content monitoring is crucial for efficient irrigation control and agricultural land management, particularly in greenhouse conditions with sandy soil (Kassaye et al., 2020; Ali et al., 2021b; Beyá-Marshall et al., 2022). Greenhouse cultivation offers a controlled and enclosed environment that allows precise monitoring and management of soil water content levels, temperature, humidity, and light for optimizing agricultural productivity, conserving water resources, and minimizing environmental concerns (Li et al. 2019). This level of control minimizes the risks associated with adverse weather conditions and ensures the proper utilization of water where the requirement is higher. The investigation of accurate and real-time soil water content data is particularly emphasized in year-round food production, providing a solution in the context of expanding populations and increasing global food security concerns. Sandy soil greenhouse environments, where special consideration is required for efficient uses of water resources, account for a considerable share of worldwide water demand. According to the report, agricultural irrigation accounts for around 70% of water withdrawals and more than 80% of water consumption (Jagermeyr et al., 2015). However, poor use of water resources in irrigation systems causes significant issues, including water shortages, environmental damage, and economic inefficiencies. The lack of precise soil water content data in this specific environment is a significant factor, which commonly leads to either over-irrigation, wasting water and energy, or under-irrigation, and reducing crop yields. A well-designed drip irrigation system has the potential to conserve the water that would often be utilized by alternative forms of irrigation systems in greenhouses. Besides, manual irrigation approaches have proven inadequate in meeting the demands of modern agriculture, which requires real-time data to make informed decisions for irrigation scheduling and resource allocation. Therefore, there has been a growing interest in developing smart data acquisition systems capable of continuously and accurately monitoring soil water content in both greenhouse and open field irrigation settings. The development of advanced data acquisition systems for soil water content monitoring and irrigation control is essential for precision agriculture, which aims to optimize resource use while maximizing crop yields (Ali et al., 2021a; Bwambale et al., 2022).
The Internet of Things (IoT) has emerged as a powerful technology platform that has the potential to change agriculture by enabling real-time monitoring and control. As an improved automatic irrigation system could save 60% of irrigated water usage (Frenken, 2009), it would greatly benefit farmers and the environment. By using sensors to detect soil water content levels and weather conditions, the system can accurately determine when and how much water is needed for optimal plant growth. Additionally, with the ability to control and monitor the irrigation system remotely, farmers can save time and resources by efficiently managing their irrigation schedules, especially in desert environments (Navarro-Hellín et al., 2016; Salman et al., 2020). Besides, the IoT platform employing sensors and low-cost microprocessors, such as Arduino and Raspberry Pi modules, has been proposed for the design and implementation of an IoT-based irrigation monitoring system (Arshad et al., 2020; Ali et al., 2021b).
Thus, soil water content monitoring and irrigation control systems have been significant advancements in recent years. These advancements have been driven by innovations in sensor technologies, data processing algorithms, and communication protocols. Notably, a variety of sensors, such as capacitance-based, time-domain reflectometry (TDR), and neutron probe sensors, have been developed to measure soil water content accurately (Chanzy et al., 2012; Wen et al., 2018; Nguyen et al., 2017). Moreover, advances in wireless communication technologies, including LoRaWAN, NB-IoT, virtual networking systems (VNC), and cellular networks, have enabled remote data collection and control, reducing the necessity for physical presence in the field of agriculture (Islam et al., 2021; Islam et al., 2020; Iqbal et al., 2019). The advantages of capacitance-type sensors lie in their capacity to reduce the impact of ionic activities that frequently occur in cultivated soil. To effectively compute the water balance in a soil system, which is a process characterized by the multidirectional movement of soil water (Domínguez-Niño et al., 2020), it is essential to calibrate these sensors for the specific soil that will be used.
The utilization of IoT technology has enhanced the efficiency of monitoring water content in sandy soil inside the greenhouses. The proposed approach integrates IoT sensors within the greenhouse, enabling the continuous monitoring of soil water levels in real-time. The sensors are responsible for gathering data pertaining to the soil water content levels in the soil, which is subsequently transferred through wireless remote protocol means to a virtual networking system (VNC). The VNC platform is an integrated IoT solution that operates as a centralized system to allow farmers to remotely access and monitor the data. The system has the ability to make well-informed decisions pertaining to irrigation practices for crops cultivated on sandy soil for greenhouse operators, resulting in precise irrigation management and enhanced crop production.
Therefore, the purpose of this article is to conduct an investigation of data collection systems developed for monitoring the water content of sandy soil employing the Internet of Things. A soil bin was built and filled with sandy soil, and a drip irrigation system was installed to irrigate the soil. To test the water content scenario, a set of capacitive soil water content sensors was calibrated and vertically placed in five different depths of the soil bin. To provide greenhouse operators with immediate access to soil water content levels, a data acquisition platform was used for data visualization. The developed data collection technique will enable irrigation control based on the needs of the crops produced in the greenhouse environment. The experiment will also look forward to the water distribution and storage capacity of the sandy soil that would be employed for further study of sandy soil crop production in desert environments.
Materials and methods
System design and architecture
The experiment entailed the monitoring of water content in sandy soil, ambient temperature, and humidity inside an experimental greenhouse. The entire system was designed and constructed to evaluate the sandy soil water monitoring system, as shown in Fig. 1. The figure illustrates the conceptual diagram of a comprehensive smart irrigation layout used for monitoring irrigation in an arid soil environment, facilitated by serial data communication systems. In this system, all sensor nodes were connected to a microprocessor and were capable of monitoring the condition levels of each sensor parameter using IoT. The motor regulator module was responsible for managing control over the solenoid valve, which played a crucial role in regulating both the flow and pressure of water.
The proposed system design comprised two major steps: the development of the system hardware using Raspberry Pi and Arduino Mega 2560, and the development of a monitoring system employing an online application to monitor the values of the sensors. The concept of utilizing ICT-based drip irrigation monitoring systems was conceived with the specific objective of cultivating crops in the arid soils of deserts. The overall system included several components, such as a water pump, solenoid valve, sand filter, water flow meter, drip irrigation setup, and a data acquisition (DAQ) box. Each component was carefully chosen to withstand the challenging environmental conditions of the greenhouse. Finally, a programmable Raspberry Pi (RPI) device was used to monitor and investigate soil water content characteristics.
Fig. 1. Conceptual diagram of an automatic drip irrigation system for sandy soil water content monitoring inside the fabricated soil bin. Water reservoir (a), water pump (b), valve (c), sand filter (d), pressure gauge (e), flow meter (f), data acquisition box (g), emitter (h), and end closer (j).
Capacitive soil water content sensor calibration
Sensor calibration is crucial for ensuring accuracy and reliable measurements for soil water content monitoring. The experimental capacitive soil water content sensor (SEN0193) was calibrated using the gravimetric method and operates within a voltage range of 3.3 to 5.5 V. Its output frequency varies from 520 Hz (low water content) to 260 Hz (high water content). Besides, when the sensor value falls within the range of 520 to 431, it is considered dry. If the sensor value is in the range of 431 to 351, it is classified as wet. However, when the sensor value falls between 351 and 261, it is categorized as water. The percentage of soil water content was determined according to the calibration of this sensor in different literature (Akhter et al., 2018; Radi et al., 2018). The calibration process involves two main steps: sensor initialization and measurement calibration. During sensor initialization, the sensor was placed in a known water content level environment, often referred to as a dry and wet reference point. The sensor readings were then adjusted to match the expected values at these reference points. Subsequently, measurement calibration was performed to account for any environmental factors that may affect sensor performance, such as temperature variations or soil type. Regular recalibration was necessary to maintain the measurement accuracy of the sensor over time.
System configuration and experiments
A DAQ system was fabricated to pursue real-time sensor data collection, which requires consideration of both the hardware and software components of the system. The particular environmental circumstances existing at the experimental site were considered for selecting the appropriate hardware components. Table 1 and Figure 2 provide descriptions of the major elements used in the experiment. Table 1 provides a detailed description of each major element, including their specifications and functionalities. The Figure visually represents the overall architecture of the DAQ system.
The major elements of this DAQ system included the Raspberry Pi 4 B+ model and the Arduino Mega 2560. The Raspberry Pi featured a 1.5 GHz 64-bit quad-core processor with 8GB of RAM and included circuitry comprising a microcontroller, an external power supply providing 3.3 and 5V, and an array of sensors. Soil water content sensors and soil temperature and humidity sensors are connected throuhgh different communication modes. Notably, while a common ground (GND) connection was established for all devices, each sensor received its individual power supply (VCC) from an external power source.
Table 1. Specification of the major components used in the experiment.
|
Item |
Model |
Specifications |
|
Raspberry Pi (processor) |
Raspberry Pi 4 B+, Raspberry Pi Foundation, Cambridge, England, UK |
CPU type/speed: Quad-core Cortex-A72, 1.5 GHz |
|
RAM size: 8GB LPDDR4-3200 |
||
|
Connection: 802.11ac wireless, Bluetooth 5 |
||
|
Connector: Standard 40-pin GPIO Storage: Micro-SD card Power: 5 V DC Operating temperature: 0〝50﹉C |
||
|
Arduino (micro-controller) |
Arduino Mega 2560, Arduino S.R.L., Ivrea, Italy |
Dimensions (length ▼ width): 101.52 ▼ 53.3 mm |
|
Microcontroller: ATmega2560 |
||
|
Digital input/output pins: 54 |
||
|
Analog input pins: 16 |
||
|
Flash Memory: 256 kB |
||
|
Raspberry Pi monitor |
Raspberry Pi Foundation, Cambridge, England, UK |
Screen dimensions: 194 ▼ 110 ▼ 20 mm |
|
Power requirement: 200 mA @ 5 V |
||
|
LCD display size: 800 ▼ 480 mm |
||
|
Capacitive soil water content sensor |
SEN0193- DFRobot, Shanghai, China |
Dimension: 98 ▼ 16 mm (3.9 ▼ 0.63 inch) |
|
Operating Voltage: 3.3 – 5.5 VDC |
||
|
Output Voltage: 1.2 – 2.5V |
||
|
Interface: PH2.0-3P |
||
|
Weight: 15g |
||
|
Operating temperature: Not specified |
||
|
Temperature and humidity sensor |
DHT22, Aosong Electronics Co., Ltd., Guangzhou, China |
Operating voltage: 3.5 to 5.5 V |
|
Sensing period: 2 s |
||
|
Operating current: 0.3 mA |
||
|
Resolution: 16-bit |
||
|
Temperature range: -40 to + 80﹎ |
||
|
Humidity range: 0 to 100% |
||
|
Accuracy: ▽ 0.5﹎ and ▽ 1% |
||
|
Water pump |
FL-43, Guanzhou White Whale Mechanical Co., Ltd., Guangdong, China |
Self-priming pump Pumping rate: 12.5 l/m |
|
Material: Rubber-elastic |
||
|
Pressure 40 psi (2.8 bar) |
||
|
Voltage supply: 220 VAC |
||
|
Pump power regulator |
Tangxi, Hangzhou, China |
Voltage: AC220V |
|
Max. power: 1,000W |
||
|
Dimension: 7.48 x 5.12 x 3.15 inches |
Fig. 2. A data acquisition system (A) and greenhouse experiment on sandy soil bin (B). Raspberry Pi display screen (a), USB hub (b), sensor connectors (c), electric port and charger (d), Raspberry Pi power supply (e), DAQ box (f), USB cable connector (g), Arduino Mega 2560 (h), voltage controller (i), and circuit breaker (j).
The DAQ system was developed to acquire and monitor sensor data effectively. It consisted of three main units: sensor connection and circuit board unit, programming and pump control unit, and electronic components and power supply unit. The data acquisition system was deployed to measure soil and climatic status and establish communication between sensor nodes. The developed system allowed for real-time monitoring of farm information on-site and remotely. The sensor nodes were connected to the Raspberry Pi single-board computer to collect the sensor data. The collected data was later transmitted and stored on an external memory device attached to the Raspberry Pi.
A data acquisition system, along with the necessary sensors was placed in the Chungnam National University experimental greenhouse located at longitude 26°22’08” N; and latitude 127°21’15” E, situated in Daejeon, Republic of Korea. The overall system was implemented to facilitate the real-time monitoring of the sandy soil water content and greenhouse environmental data with ease and convenience. The greenhouse, covering 90 m2, was prepared to evaluate the soil water movement within the sandy soil environment. Four soil bins were fabricated and placed inside the greenhouse, where a soil bin (3×3 m) was used for conducting the experiment. A drip irrigation system was employed with laterally arranged dripper lines at 0.6-meter intervals and 0.3-meter spacing between water emitters ensuring effective water distribution throughout the experimental duration. Figure 2 shows a soil bin with an installed DAQ box, necessary sensors, and associated components inside the greenhouse. Twenty-five soil water content sensors were placed into five different depths of 10, 20, 30, 40, and 50 cm, respectively, to investigate the water percentages in those particular depths in the experimental sandy soil. Besides, the temperature and humidity sensor was installed to check the temperature and humidity pattern inside the greenhouse.
The experimental soil was sandy loam (78% sand, 14% silt, and 8% clay). The analysis of texture was conducted in soil testing laboratory at Chungnam National University. To avoid sensor erosion, the head of the capacitive sensor was wrapped with tape and put into the sandy soil following the 10, 20, 30, 40, and 50 cm depths. Five sensors were placed horizontally at each depth at 15 am intervals. Thus, a total of twenty-five soil water content sensors were interfaced with the MCU (microcontroller unit), and the program was run in open-source Arduino IDE (integrated development environment). The 9600 baud rate and the 7-second interval data acquisition rate were fixed in the program. The sensor value in dry and wet conditions was kept fixed, and the sensor value was classified compared with the calibrated value. Three Arduinos were used to collect the data that was interfaced with the 8, 8, and 9 sensor points, respectively (Fig. 3). A USB (universal serial bus) hub was used to connect with the microprocessor (Raspberry Pi), and each hub connected to the Raspberry Pi port individually. A Python-based programming language was used to collect the Arduino-retrieved data and showed it on the data monitoring device. A Raspberry Pi terminal was executed to display the sensor data. By identifying the port number, the data was saved under a different name in CSV file format. In the meantime, the data was automatically saved in the Raspberry Pi memory for further data processing. The communication protocol was simple and concentrated on getting data over the serial peripheral interface. A DAQ system was fabricated for monitoring soil and environmental parameters in the experimental greenhouse. Twenty-six sensor nodes (twenty-five soil water contents, ambient temperature, and humidity) were linked to Arduino, and Arduino was connected to the Raspberry Pi port to obtain sensor data. The processing and control of these systems relied on the responsibilities assigned to the Raspberry Pi. In the system, the data was treated, transmitted, and stored on a Raspberry Pi. Additionally, its compatibility with different operating systems and programming languages made it versatile and easy to integrate into existing systems.
Fig. 3. An IoT-based data acquisition process for monitoring the sandy soil water content.
Results and Discussion
Sandy soil water content data storage and monitoring
The water content sensors played a crucial role in assessing soil water content levels at various depths within the sandy soil. The operation of the irrigation pump was regulated based on the sensor responses to ensure precise water management in the sandy soil. Throughout the investigation, the data storage component of the IoT-based soil water content monitoring system performed well. The system efficiently collected and stored all the sensor readings, allowing for detailed analysis and comparison of soil water content levels over time and making them accessible for remote monitoring by authorized users through VNC (virtual network computing) applications. This not only provided valuable information on the water requirements of the sandy soil but also allowed for the optimization of irrigation schedules. With the help of this IoT-based system, precise water management was achieved, resulting in ensuring the water requirements for crop production. Figure 4 provides an overview of the entire data acquisition and monitoring system. The IoT-based monitoring system for sandy soil water levels in greenhouses operates continuously to gather data. The findings demonstrated that by connecting soil water content sensors to the Raspberry Pi interface and placing them up to 50 cm deep in the soil, it could monitor the temporal, and vertical distribution of irrigated water in sandy soil at 10 cm intervals. Concurrently, temperature and humidity sensors recorded environmental data inside the greenhouse. This comprehensive monitoring approach enabled precise irrigation control, which could ensure that crops received the optimal amount of water. Furthermore, the analysis of the data collected by the sensors may help producers recognize patterns and make informed decisions regarding irrigation schedules and water consumption.
Fig. 4. Soil water content data acquisition and monitoring system: automatic CSV data file saved in Raspberry Pi folder (a), real-time data saved in Raspberry Pi (b), Raspberry Pi screen with soil water content data for real-time monitoring (c).
Soil water content variations at different depths in sandy soil
The monitoring of soil water content levels at five different depths inside sandy soil bins located in the greenhouse is shown in Figure 5. The maximum and minimum output values of soil water content were observed at different depths with a sandy soil profile, with values of 43, 31, 32, 29, 29.5, and 25% at depths of 10, 20, 30, 40, and 50 cm, respectively. The results showed that the trend in soil water content variance was steadily rising across all depths during the entirety of the monitoring period. These characteristics were constant for all the depths of the sandy soil bin. During this time period, there was a significant drop in the levels of water content (%), with values going from 41% to 27%, 30% to 26.5%, 32% to 25%, 28% to 23%, and 28% to 20%, respectively. This decrease in soil water content at various depths during the experiment may be related to particular environmental or climatic conditions that deserve additional examination in order to have a better understanding of the dynamics of water availability in the greenhouse soil during that time period for the sensors position of SP1, SP2, SP3, and SP4, respectively. With the position of SP5, it is noticed that the water content level suddenly decreased. This could happen due to the variation in soil compaction that can create localized zones of reduced water retention. Besides, an uneven water distribution might have affected water levels at that specific point. Further investigation is necessary to determine the factors contributing to the decrease in soil water content. Understanding these dynamics will provide valuable information on managing water availability in greenhouse soil during similar time periods in the future. Additionally, this research could help inform strategies for optimizing irrigation practices and improving overall water efficiency in greenhouse agriculture.
Fig. 5. Soil water content variation monitoring in five different depths in sandy soil bins inside the greenhouses.
Temperature and humidity characteristics inside the greenhouse
The IoT-based data acquisition system recorded valuable information on temperature and humidity characteristics inside the experimental greenhouse. The system recorded significant changes in temperature and humidity levels over the period of 24 hours. The system had the ability to record temporal data, leading to an investigation of the influence of water content variations. Figure 6 shows the characteristics and circumstances of the average ambient temperature and relative humidity inside the greenhouse during the experiment. Starting at 10:00 AM, the temperature and humidity were 40.30°C, and 55.25%, respectively, indicative of a warm and moderately humid environment. However, as the day continued, both temperature and humidity decreased gradually. By 6:00 PM, the temperature dropped to 27.92°C, while the humidity was raised substantially to 90.07%, creating a cooler but more humid atmosphere. As the night advanced, temperature and humidity continued to decrease steadily, reaching their lowest points at 5:00 AM with 21.41°C and 99.90% humidity, respectively. Notably, the climate in greenhouse started to stabilize and improve after sunrise, with temperature and humidity rising steadily from 6:00 AM onwards. These observations emphasize the need for effective climate control measures within the greenhouse, such as ventilation and heating, to maintain optimal conditions for plant growth and health throughout the day-night cycle.
Fig. 6. Average temperature, and humidity results during the experiment inside the greenhouse.
Conclusions and recommendations
In this study, a sandy soil water content monitoring system was successfully developed utilizing the Internet of Things (IoT) technology, specifically designed for greenhouse applications. The primary goal was to develop an efficient and reliable system to assist greenhouse operators in optimizing water usage, enhancing crop yields, and contributing to sustainable agriculture practices. The following significant conclusions and recommendations were reached through the experimentation:
* The IoT-based system effectively monitored soil water content levels in sandy soil, proving significantly beneficial in greenhouses for precise water resource control.
* The system offered real-time data access and analysis, enabling greenhouse operators to make informed decisions, promoting proactive water management, minimizing resource wastage, and improving crop growth.
* To maximize the benefits of soil water content monitoring, greenhouse operators should consider integrating IoT systems with automated irrigation systems. This integration can enable automatic watering based on real-time data.
* Implementing advanced data analytics and machine learning algorithms can help predict optimal watering schedules based on historical data and weather forecasts. This can improve water management further and lead to even better crop outcomes.
* Regular sensor calibration and maintenance are essential to ensure the accuracy of soil water content measurements over time.
* This approach has the potential to transform the agricultural field significantly, fostering sustainability and enhancing agricultural productivity through the utilization of passive monitoring and automation techniques. Further research and enhancement are required to explore and enhance its potential in various agricultural contexts.
Acknowledgments
This work was supported by the Korea Institute of Planning and Evaluation for Technology in Food, Agriculture and Forestry (IPET) through (Open Field Smart Agriculture Technology Short-term Advancement Program), funded by Ministry of Agriculture, Food and Rural Affairs (MAFRA) (322042-3).
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