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This study investigated how the substrate volumetric water content (VWC) and fluctuating irrigation affect the growth and performance of variegated Ficus pumila 'Variegata' indoors using a soil moisture sensor based-automated irrigation system. The irrigation treatments consisted of three treatments maintaining a constant VWC (at 0.20, 0.40, and 0.60 m3·m-3) and one treatment with a fluctuating VWC (threshold, 0.20 m3·m-3; 0.20 F). Plant shoot growth, chlorophyll content, leaf variegation, and coloration parameters were measured at four weeks after treatment. The 0.20 and 0.20 F treatment groups had lower shoot fresh weight than the groups maintained at higher VWC thresholds. Although the 0.40 and 0.20 F treatments had similar average VWCs during the experiment, the 0.20 F group had less shoot growth than the 0.40 group, resulting in a lower irrigation amount. Physiological responses, such as leaf relative water content and Fv/Fm, were not affected by irrigation treatment. Similarly, leaf variegation and coloration were not influenced by the irrigation treatments. A higher irrigation amount was required to maintain the 0.60 treatment group compared to the 0.40 treatment group, even though there were no significant differences in growth parameters between the groups. In contrast, plants in the 20 F treatment group had high water use efficiency, as they had proper growth with low irrigation amounts. Overall, maintaining a low water content or providing intermittent irrigation at 0.20 m3·m-3 provided a slow growth of F. pumila 'Variegata' indoors, resulting in adequate plant performance and ease of management for house plants. [ABSTRACT FROM AUTHOR]

The aim of research is to reduce the utilization of water by irrigating the agricultural fields that have low moisture level through the implementation of Wireless Sensor Network (WSN). The ARM-based irrigation solution consists of a Solar Tracking System, Wireless Information Unit (WIU), WSN and Remote Access. The Solar Tracking System utilizes maximum solar energy by using Light Dependent Resistor(LDR) to track the sun. The electric energy produced is stored in the battery which powers the ARM processor. Wireless Information Unit will collect the sensor information from Wireless Sensor Network through the use of Wi-Fi. The system monitors soil-moisture and controls the flow of water using a solenoid valve depending upon the set threshold. In the tank, Ultra Sonic sensor identifies the water level controlled by the WIU. An Android application helps the farmer to monitor the water flow and soil moisture remotely. The proposed water management methodologies for agriculture optimizes the water usage and such practices maintain the crop health, keep yield intact, and avoids human intervention. In this proposed system, soil moisture is maintained appropriately to ensure that a good harvest is obtained which in turn preserves different types of nutrients.

Technology in agriculture has been widely and massively applied. One of them is automation technology and the use of big data through the Internet of Things (IoT). The use of IoT allows a process to run automatically without human intervention. Extreme weather changes and narrow land use are one of the main problems in agriculture. The development of IoT devices has been widely developed regarding this subject. One of them is a soil moisture detection system. This study aims to build an IoT soil moisture detection system. The system will use a sensor as input which is then processed in a microcontroller device and the prediction results are sent to the IoT cloud platform. Prediction results are obtained using a time series model and then its performance is evaluated using RMSE. This model was chosen because the structure of the observed soil moisture data is based on time. The results of this study indicate that the soil moisture IoT system can work well. This is supported by the results of the prediction evaluation value of the RMSE = 1.175682x10-5 model which is very small.

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. [ABSTRACT FROM AUTHOR]

Proper irrigation management, especially for tomatoes that are sensitive to water, is the key to ensuring sustainable tomato production. Using a low-cost sensor coupled with IoT technology could help to achieve precise control of the moisture content in the plant root-zone soil and apply water on demand with minimum human intervention. An IoT-based precision irrigation system was developed for growing Momotaro tomato seedlings inside a dark chamber. Four irrigation thresholds, 5%, 8%, 12%, and 15%, and two irrigation systems, surface and subsurface drip irrigation, were compared to assess which threshold and irrigation system referred the ideal tomato seedling growth. As a result, the 12% soil moisture threshold applied through the subsurface drip irrigation system significantly (p < 0.05) increased tomato seedling growth in soil composed of a main blend of peat moss, vermiculite, and perlite. Furthermore, in two repeated experiments, a subsurface drip irrigation system with 0.86 distribution uniformity used 10% less water than the surface drip irrigation system. The produced tomato seedlings were transplanted to open fields for further assessment. A low-power wide area networking Long Range Wide Area Network (LoRaWAN) protocol was developed with remote monitoring and controlling capability for irrigation management. Two irrigation systems, including surface and subsurface drip irrigations, were used to compare which system resulted in higher tomato yields. The results showed that the subsurface drip irrigation system with 0.74 distribution uniformity produced 1243 g/plant, while each plant produced 1061 g in the surface drip irrigation system treatment. The results also indicated that the LoRaWAN-based subsurface drip irrigation system was suitable under outdoor conditions with easy operation and robust controlling capability for tomato production. [ABSTRACT FROM AUTHOR]

Water management is a key factor to optimize fruit quality and yield of processing tomatoes which are site-specific and influenced by environmental conditions e.g., soil, temperature, precipitation. The aim of this study was to evaluate the efficacity of a low-cost wireless soil moisture sensor in determining the irrigation level for optimizing the marketable yield, fruit quality and economic profit of processing tomato. A two-years (2017–2018) trial was conducted in open field, applying nine drip irrigation levels controlled by wireless soil moisture capacitance sensors. The irrigation levels were as follows: 13.2, 16.7, 25.4, 33.3, 50.0, 62.3, 82.5, 100 and 186.8% of water restitution based on soil moisture sensor readings. Because of the crop stress induced by heavy rainfalls occurring in 2018 growing season, total and marketable yields reached higher maximum values in 2017 than 2018. In 2017, total and marketable yields were maximized by supplying 92.8% and 96.2% of irrigation level, respectively. Moreover, 95.6% and 91.2% of irrigation level were necessary in 2018 to maximize total and marketable yield, respectively. In both growing seasons, marketable yield variation was due to changes of both fruit number and fruit mean weight. Total soluble solids of fruit juice linearly decreased by increasing the irrigation level with a more pronounced effect in the driest growing season (2017). Economic analysis demonstrated that 100% of irrigation level should be preferred by the Italian farmers since it maximized the operating margins of processing tomatoes in both years. To conclude, the use of the tested low-cost wireless soil moisture sensor is an effective tool to manage the level of irrigation and optimize the processing tomato yield and economic benefits for farmers. [ABSTRACT FROM AUTHOR]

With India's population growing at a rapid pace, traditional agriculture will have a tough time meeting future food demands. Water availability and conservation are major concerns for farmers. This paper aims to discuss the aspects related to designing and fabricating an automatic irrigation system using the Internet of Things (IoT) which will save the farmer's time and money significantly. Human intervention in fields will be reduced. Changes in soil moisture are detected by soil moisture sensors and irrigation is automated using IoT. The proposed system is most economical for underdeveloped places because it is very costeffective. Based on the soil moisture content, the sensor detects and sends signals to the node MCU, which activates the motor. When the plants receive enough water, the motor automatically shuts off. The user will be alerted about the soil's moisture content through his mobile phone. The proposed smart irrigation system is implemented at our campus which conserves energy and water. [ABSTRACT FROM AUTHOR]

This paper aims at proposing an augmented sensing method for estimating volumetric water content (VWC) in soil for Internet of Underground Things (IoUT) applications. The system exploits an IoUT sensor node embedding a low-cost, low-precision soil moisture sensor and a long-range wide-area network (LoRaWAN) transceiver sending relative measurements within LoRaWAN packets. The VWC estimation is achieved by means of machine learning (ML) algorithms combining the readings provided by the soil moisture sensor with the received signal strength indicator (RSSI) values measured at the LoRaWAN gateway side during broadcasting. A dataset containing such measurements was especially collected in the laboratory by burying the IoUT sensor node within a plastic case filled with sand, while several VWCs were artificially created by progressively adding water. The adopted ML algorithms are trained and tested using three different techniques for estimating VWC. Firstly, the low-cost, low-precision soil moisture sensor is calibrated by resorting to an ML model exploiting only its raw readings to estimate VWC. Secondly, a virtual VWC sensor is shown, where no real sensor readings are used because only LoRaWAN RSSIs are exploited. Lastly, an augmented VWC sensing method relying on the combination of RSSIs and soil moisture sensor readings is presented. The findings of this paper demonstrate that the augmented sensor outperforms both the virtual sensor and the calibrated real soil moisture sensor. The latter provides a root mean square error (RMSE) of 3.33 % , a virtual sensor of 8.67 % , and an augmented sensor of 1.84 % , which improves down to 1.53 % if filtered in post-processing. [ABSTRACT FROM AUTHOR]

The aim of research is to reduce the utilization of water by irrigating the agricultural fields that have low moisture level through the implementation of Wireless Sensor Network (WSN). The ARM-based irrigation solution consists of a Solar Tracking System, Wireless Information Unit (WIU), WSN and Remote Access. The Solar Tracking System utilizes maximum solar energy by using Light Dependent Resistor(LDR) to track the sun. The electric energy produced is stored in the battery which powers the ARM processor. Wireless Information Unit will collect the sensor information from Wireless Sensor Network through the use of Wi-Fi. The system monitors soil-moisture and controls the flow of water using a solenoid valve depending upon the set threshold. In the tank, Ultra Sonic sensor identifies the water level controlled by the WIU. An Android application helps the farmer to monitor the water flow and soil moisture remotely. The proposed water management methodologies for agriculture optimizes the water usage and such practices maintain the crop health, keep yield intact, and avoids human intervention. In this proposed system, soil moisture is maintained appropriately to ensure that a good harvest is obtained which in turn preserves different types of nutrients. [ABSTRACT FROM AUTHOR]

Central Sulawesi Province in Indonesia has great potential for horticultural commodities, namely local red onion Palu. In the current climate change, local farmers are still watering plants in the conventional way. The automatic watering system simplifies the work of local farmers. This device uses a soil moisture sensor as a soil moisture detector and Arduino as a program brain. This study aims to determine the position of soil moisture sensor, the optimal length of watering time and analyze the quality of data stored. The experiment was carried out using a Completely Randomized Design (CRD). The position of the soil moisture sensor was analyzed by Profile Analysis. The optimal length of watering time was determined by Analysis of Variance (ANOVA) and Least Significant Difference (LSD). The quality of data stored was determined by a number of missing values and frequency of watering. The results showed that in soil planting media the position of soil moisture sensor had no significant effect, while in others planting media (water and combination of water and soil) the position of the sensor had a significant effect. The optimal watering time was 3 seconds. The stored data has low quality in terms of missing values and lack of consistency.

Farming has been the most prominent and fundamental activity for generations. As the population has been multiplying exponentially, the demand for agricultural yield is growing relentlessly. Such high demand in production through traditional farming methodologies often falls short in terms of efficiency due to the limitations of manual labour. In the era of digitization, smart agricultural solutions have been emerging through the windows of Internet of Things and Artificial Intelligence to improve resource management, optimize the process of farming and enhance the yield of crops, hence, ensuring sustainable growth of the increasing production. By implementing modern technologies in the field of farming we can enable telemetry through which farmers can remotely monitor and gather real time data on the desired parameters. It also gives accurate and precise measurements when compared to traditional measurement techniques. This research paper focuses on an IoT based approach for smart monitoring using ESP WROOM 32 microcontroller that helps farmers identify real-time parameters of temperature, moisture and humidity of their field. Real-time data on temperature, moisture, and humidity enables farmers to make informed decisions about irrigation and crop protection. Furthermore, the use of smart monitoring ensures accurate and precise measurements, surpassing the limitations of traditional techniques. [ABSTRACT FROM AUTHOR]

A more accurate monitoring system needs to be developed by calculating the infiltration process of rainwater into the soil. The development of a digital infiltration profiler (DIP) is expected to be able to improve the accuracy of the early warning system, as well as to open a more considerable opportunity to study the landslide mechanism related to the infiltration process and the increase of pore-water pressure in the soil at both field-scale and laboratory scale. The performance and reliability of developed DIP was tested through laboratory and field ponding simulation. The field testing was demonstrated through a real installment procedures to be applied in the actual case. The field testing also consisted of water ponding and data reading. Numerical simulation using SEEP/W software of Geostudio packages was conducted to be compared to the field-testing data. From the laboratory ponding experiment, the sensor readings correspond well to the wetting front observation. The field ponding observation results showed good response of the DIP in detecting the wetting front. The results also implied the similarity of reducing wetting front rate that was simulated in SEEP/W and previous studies. The coupled numerical simulation and slope stability analysis of the conceptual slope model suggested the importance of multi-depth saturation measurement is necessary. The near-surface observation needs to confirm the saturation of the soil surface. The deeper sensor can continuously monitor soil saturation change in prolonged rainfall to predict the safety factor of the slope. [ABSTRACT FROM AUTHOR]

Irrigation is one of the largest consumers of fresh water in the agricultural sector. Surface irrigation is one the most widely adopted method of irrigation due to its simplicity and low energy requirement. Although, it is mostly associated with poor irrigation application efficiency mainly due to deep percolation and uneven distribution of irrigation water. A study was conducted in closed-end level basin layout in loamy soil to establish and test an Internet of Things (IoT) based system through a wireless connection between the automatic check gate and soil moisture sensors which can be operated remotely based on real-time soil moisture status. An attempt had been made to identify the suitable location of the sensor in the basin layout for improving irrigation application efficiency. An aluminium automatic-check gate fitted in an iron frame was placed in the concrete water supply channel to control the water flow. Three capacitance-based soil moisture sensors were placed at 25%, 50% and 75% of field length (i.e., 15 m, 30 m and 45 m) at 37.5, 15 cm and 7.5 cm depth, respectively. A wireless communication network was established between capacitance-based soil moisture sensors and an automatic check gate through the LoRa module and GSM module. The software used for automatic control of the check gate in this study was Croplytics® by Agsmartic. The system was tested for nine irrigation events under bare soil conditions. Three different operational schedules based on different soil moisture sensor placement had been tested under 40%, 30% and 20% soil moisture deficit conditions. The results suggested that the best location of sensors for closing the system would be at 37.5 cm depth placed at 25% length from the inlet in higher soil moisture deficit condition and at 7.5 cm depth placed at 75% length in low moisture deficit condition. Overall, the irrigation application efficiency was improved up to 86.6% with automation.

Plantation methods, including hydroponics, have been extensively used in agriculture. They also employed a time-based irrigation system for the plant. The goal of this project was to create a self-sustaining indoor plantation system that uses soil moisture sensor data to control the flow of water when the sensor detects that the soil is almost dry. Soil conditions are monitored, and crops are irrigated more efficiently with the help of this new technology. Water is conserved by just watering the plants when they absolutely need it, rather than watering them continually all the time as the traditional method would require. Light-dependent resistors are used to measure the brightness of the surroundings in this project. As a result, the grow light will be activated when the ambient light level drops. With the help of a soil moisture sensor and a light-dependent resistor (LDR), one can create a system that automatically waters and lights plants. Finally, the soil moisture sensor collects data for the sprinkler system and displays it on the LCD screen, and then the appropriate measures are taken. When the soil's humidity level is high, the water that flows will be stopped. [ABSTRACT FROM AUTHOR]

The process of manual fertilization and irrigation of plants has disadvantages such as it requires human labor and is inefficient in the use of fertilizers and water sources. Therefore, the purpose of this research is to design and develop an automatic fertigation system. The method used in this research is a drip fertigation technique where the fertigation liquid that has been stirred is placed in a tank with 100 cm height from ground. With the gravitational force, the fertigation liquid is distributed through pipes, hoses and emitters around the plant. While the control system and automation of fertigation distribution to plants is carried out based on a microcontroller that need the condition of soil moisture values around the plant. If the watering time and humidity values are met, the electronic valve will open so that the fertigation liquid flows from the tank to the plants. Furthermore, in this study the tests were carried out to determine the uniformity coefficient and debit of drip fertigation. From the results, it shows that the emitter’s debit influenced by the liquid level in the tank. The uniformity coefficient obtained in the two tests is greater than 90%. From the two tests carried out from the moisture sensor 1 and 2, it shows the mean percentage error for both sensor is less than 1.7%, while the percentage error of soil moisture sensor 1 is 1.6% and the percentage error of soil moisture sensor 2 is 1%, respectively. Meanwhile, from the ultrasonic sensor testing it was obtained 0% of error. It also shows from the testing conducted that the fertigation system works very well and successfully.

Eucheuma cottonii is a type of seaweed that is cultivated in the coastal area of Nagari Sungai Pinang, Koto XI Tarusan District, Pesisir Selatan District, West Sumatra Province. Before being sold, seaweed farmers must dry their seaweed first, because what can be sold is dried seaweed. Drying the seaweed takes two to three days depending on the weather conditions. In this research a seaweed drying system has been designed with a tool that rotates the seaweed in the drying chamber. In this rotary drying system, the soil moisture sensor is used which functions to detect the moisture content of the seaweed, if the moisture content of the seaweed is still read > 30% then the microcontroller turns on the relay which forwards instructions to the heater and the DC motor starts rotating the player container. The DS18B20 sensor is used to detect the temperature of the drying chamber, if the total moisture content of the seaweed is 30%, then the state of dry seaweed has been reached. Tests have been carried out to determine the difference in time required between the seaweed drying process using the system that has been built in this study and the seaweed drying process that utilizes direct sunlight. To achieve a moisture content of 30% in the seaweed drying process with the system designed in this study it takes an average of ±50 minutes, while using direct sunlight it takes ±9 hours to dry the seaweed.

ABSTRACT Capacitive methods for determining soil moisture are extremely practical and easily applied in the agricultural environment, but are influenced by temperature, salinity and the type of soil. In this study, the aim was to verify the performance of the Capacitive Moisture Sensor for Porous Media when subjected to saline media. The sensor under evaluation was developed at the Federal University of Ceará, and provides measurements and electrical frequencies related to soil moisture. The capacitive sensor for porous media was subjected to saline solutions with different concentrations of potassium chloride (KCl), when it was seen that the sensor electrodes appear sensitive to salinity when inserted in saline aqueous solutions with an electrical conductivity of up to 4 dS/m. Adjustments to the RC (resistor-capacitor) circuit were tested as an alternative to lessen the effect of salinity on the measurements, and it was possible to generate readings related to soil moisture in electrical frequencies ranging from kHz to MHz. Tests were then carried out in a sandy soil under different conditions of moisture and salinity. No significant differences were seen when using different resistors in the RC circuit that was constructed to lessen the effects of salinity on the measurements taken by the sensor, whether in solution or in the soil.

• Cost-effective, self-sufficient soil moisture monitoring systems are crucial for sustainable agriculture. • Frequently, low-cost soil moisture systems uses Arduino microcontroller and radio frequency communication devices. • Exploring in-situ alternative energy sources is crucial for remote areas that primarily depend on solar energy and batteries for power. • Need to explore other biodegradable polymers for the design of eco-friendly soil moisture sensors. • A cost-effective, off-grid biodegradable soil moisture sensor is not yet available in the market. Soil moisture monitoring is essential for a variety of applications including agriculture, forestry, and environmental monitoring. However, soil moisture sensors may be expensive and require batteries or other energy sources, making them unsuitable for remote or off-grid locations and farmers. Improper e-waste management of short-lived sensing components can reveal the contradictions of solutions aimed at environmental sustainability, which also degrade environmental health. Therefore, the development of low-cost, off-grid, biodegradable in-situ soil moisture sensing system (SMSS) is necessary for these regions. This article provides an overview of the current state-of-the-art in low-cost, off-grid, and biodegradable in-situ soil moisture sensing. It highlights low-cost SMSS components including hardware (microcontrollers and communication modules), software, and off-grid ambient energy sources. It also highlights the current research in biodegradable polymers used for moisture sensing. The challenges in combining low-cost, off-grid, and biodegradable soil moisture sensing are identified as a research gap. Finally, the underlining question of the "perfect" choice of SMSS is explored based on the trade-offs of performance, operational feasibility, and the newly proposed aspect of biodegradability, consequently suggesting context-specific decisions by consciously managing these tradeoffs. [ABSTRACT FROM AUTHOR]

Soil moisture sensors are valuable tools in irrigation management, but due to soil spatial variability it is challenging for point-based sensors to characterize the entire soil water profile. Universal multiple linear regression (uMLR) is a simple machine learning tool that analyzes the relationship between all possible subsets of observations and the prediction of interest. This study used uMLR and volumetric water content data (VWC) taken from maize field experiments at the Limited Irrigation Research Farm near Greeley, Colorado, to optimize the number and placement of soil moisture sensors to characterize the entire soil water deficit (SWD) in the root zone. The uMLR analysis quantified the accuracy of using one or two VWC measurements to represent SWD. We evaluated seven field seasons, with 12 treatments and four replicates of each, where volumetric VWC was typically measured one to three times weekly. Results indicate that the best single sensor depth is at 30 cm, and the best combination of two sensors is at 0 to 15 cm and 60 cm. To validate the experiment, a continuous VWC sensor was used at 30 cm to predict SWD results with acceptable accuracy (RMSE = 4.60 mm). This study suggests that in the sandy loam site evaluated and with varying irrigation levels, soil moisture sensor placement near the soil surface along with knowledge of soil texture can be used to characterize SWD. This technique may be transferable to other locations and soils and shows potential for optimizing irrigation management via soil moisture sensor networks with continuous SWD data. [ABSTRACT FROM AUTHOR]

This paper proposed a novel framework for soil humidity control and watering management based on the Internet of Things (IoT) and ARM. The framework is designed to minimize manual field process and transfer information to a cloud server that is accessible through a web application. The proposed scheme has advantages in reducing costs, restricting water waste as well as minimizing the physical interference. It also advocates low maintenance and environmentally sustainable irrigation. The sound, humidity and temperature sensors are combined with ARM single board computer (called the Raspberry Pi). A relay module is placed to regulate the stream of water. The sensor obtained values are stored on the cloud server and the necessary values and recommendation are provided via the web application. When the ground temperature is high and soil humidity is low, the automatic irrigation system is triggered and user notification can be performed with email. The model showed expected impacts at the various levels of humidity. [ABSTRACT FROM AUTHOR]

Increasing interest in studying the variability of soil water content and its spatial scale dependency necessitates the development of new techniques to accurately monitor soil water over a wide range of spatial scales. Distributed Temperature Sensing (DTS) techniques offer unprecedented opportunities to measure temperature with a spatial resolution of a few centimeters over several kilometers, which can be used to measure soil moisture. This study is the first of its kind that investigates under field conditions the feasibility of combining the Dual‐Probe Heat‐Pulse (DPHP) technique with the DTS technology to measure soil thermal properties and variation in soil moisture. A field experiment was conducted over a 30 m transect in the Lake Wheeler Field laboratory in Raleigh, NC. Three different DPHP sensors were constructed from combinations of different fiber optic cables and heating elements and were tested to assess their performance and the effect of their construction characteristics on their accuracy. Measurements were taken over different soil moisture conditions and the system performance was compared against independent soil water content sensors. The system was able to track changes in soil water content with a mean RMSE of 0.02 m3 m−3 using the optimal DPHP sensor. The key advantage of the tested system is that it does not need any site‐specific calibrations typically required for other DTS‐based systems. The findings of this study provide some practical information and measures that need to be taken for successful DTS‐DPHP construction and application under field conditions. Plain Language Summary: High‐resolution and accurate soil moisture measurements that can cover different spatial scales are required to support many applications including irrigation systems optimization. However, available techniques to measure soil moisture over scales that range from meters to several kilometers are lacking. This study is the first of its kind that investigates under field conditions a novel technique to measure changes in soil moisture that can bridge the gap in the spatial scales covered by the existing techniques. The novel technique utilizes the high‐resolution temperature measurements obtained using the Distributed Temperature Sensing (DTS) technology along a fiber‐optic (FO) cable and a heating element to measure the soil thermal properties and changes in soil moisture over the length of the FO cable. Three DTS‐based sensors varying in their properties were tested. The optimal combination resulted in measurements of the changes in soil moisture with a mean RMSE of 0.02 m3 m−3. The key advantage of the novel technique is that it does not need any additional information about the soil properties to give accurate measurements of changes in soil moisture. Key Points: This study is the first attempt to apply the novel Distributed Temperature Sensing‐Dual‐Probe Heat‐Pulse soil moisture sensing system under field conditionsThe technique can measure soil thermal properties and changes in soil moisture without any soil‐specific calibrationsThe system can measure changes in soil moisture with a mean Root Mean Square Error of 0.02 m3 m−3 and a mean maximum error of 0.05 m3 m−3 [ABSTRACT FROM AUTHOR]

It is an automated agricultural mobile robot designed and developed to monitor and control various parameters such as temperature, humidity, light intensity and soil moisture. The microcontroller used for this work is AT89s51 which belongs to the family of 8051 microcontrollers. Humidity sensor, temperature sensor, light sensor, and soil moisture sensor are four sensors used in this work to measure various parameters that contribute to monitoring the environment. In the designed prototype, soil moisture content was measured for three different types of soils which are Loam soil, Clay soil and Sandy soil (these three soils are widely used for agricultural purposes, which is the reason why they were used in this work), a LCD-display was used to observe the recorded values of these soils. This particular designed prototype will help reduce the manual efforts of the farmers and give the optimal values of the parameters. [ABSTRACT FROM AUTHOR]

In agriculture production, soil moisture management is one of the parameters for the sustainable use of natural resources. Soil tensiometers are the instruments used for measuring soil matric potential to monitor soil moisture content. The tensiometer contains a vacuum gauge for the visual indication for irrigation managers to monitor field moisture within the maximum allowable deficit. The vacuum gauge has the limitation that it does not provide its output in the form of an electrical signal, which is used as input for an automatic irrigation system. So, the present work was carried out to develop and evaluate a piezoresistive-based soil moisture sensors system for precise irrigation scheduling through automation devices. The experiment was carried out in the Centre for Protected Cultivation Technology, IARI, New Delhi, and it contains sandy loam soil. The development of a piezoresistive-based sensor for automatic irrigation scheduling can be done by using a piezoresistive sensor, XBee transmitter and receiver, microcontroller, water meter, and relay unit. Once development was completed, it was tested and compared with a conventional tensiometer at different chrysanthemum and tomato crop depths. The relative root mean square error (RRMSE) of soil moisture content for tensiometer and piezoresistive-based soil moisture sensor varied from 9.47 to 19.38 and 7.44 to 18.28, respectively. The coefficient of determination for tensiometer and piezoresistive-based soil moisture varies from 0.71 to 0.72 and 0.82 to 0.84, respectively. The paired t-test was used to compare the developed and conventional tensiometer, and it was found that both sensors were significantly different at a 5% significance level. [ABSTRACT FROM AUTHOR]

In Indonesia, agriculture is required to be able to develop growth in the region and be able to produce agricultural products that have high competitiveness while also empowering the community. IoT, or commonly known as the Internet of Things (Internet of Things), allows us to connect to various things. For example, we can use a smart farm. This research leads to the implementation of a plant sprayer that uses the Cognitive internet of things that can be integrated into farmers and factories whose raw material uses corn. This irrigation system uses a NodeMCU microcontroller, automatic discharge using a DC water pump, a soil moisture sensor using a capacitive soil moisture v.2 sensor. The system used uses fuzzy logic algorithms as data processing. The sensor data shows the 70% soil moisture value displayed on the smartphone so that the system will water or not automatically until the soil moisture value is as needed. Soil moisture in this system is in 3 criteria, namely dry, normal, and wet. Watering produced by this system with an average time duration of 8 seconds. This farming system is designed to increase productivity and predict problems in agriculture. This analysis illustrates that the Cognitive internet of things has great potential in agricultural technology because it can increase and facilitate corn production for farmers. [ABSTRACT FROM AUTHOR]

Tanaman merupakan tumbuhan yang dibudidayakan agar dapat diambil manfaatnya oleh sebab itu memerlukan perawatan yang intensif berupa penyiraman dan pemupukan yang teratur sesuai dengan resistensi media tanam dan karakteristik dari tanaman. Diberbagai daerah petani masih melakukan penyiraman dan pemupukan secara manual cara tersebut kurang efektif karena debit air tidak sesuai dengan kebutuhan dan menghabiskan tenaga dan waktu yang cukup lama. Maka dari itu petani untuk segera beralih ke system otomatisasi penyiraman dan pemberian pupuk cair pada tanaman. Pemberian air dan pupuk pada tanaman secara otomatis yang dikendalikan oleh mikrokontroller arduino sebagai pusat kontrol dan beberapa komponen sebagai alat pendukung dari sistem otomatis berdasarkan data hasil pengukuran menjadi solusi untuk meringankan pekerjaan dan meningkatkan produktivitas tanaman. System otomatis bekerja berdasarkan informasi beberapa sensor mengenai kondisi tanah pada tanaman dengan pengontrolan mikrokontroller arduino. Penyiraman tanaman dilakukan ketika sensor soil moisture membaca kelembaban tanah sesuai dengan set point yang telah ditentukan yaitu pada range >700, dan penyiraman akan berhenti ketika tanah pada tanaman dalam keadaan basah dan sesuai dengan set point yang telah ditentukan pada range >650. Dan untuk pemberian pupuk cair pada tanaman menggunakan RTC yang berfungsi sebagai penjadwalan pemberian pupuk cair yang terlebih dahulu dilakukan setting hari dan waktu pada program sesuai dengan set point yang telah dilakukan.

Proper irrigation management, especially for tomatoes that are sensitive to water, is the key to ensuring sustainable tomato production. Using a low-cost sensor coupled with IoT technology could help to achieve precise control of the moisture content in the plant root-zone soil and apply water on demand with minimum human intervention. An IoT-based precision irrigation system was developed for growing Momotaro tomato seedlings inside a dark chamber. Four irrigation thresholds, 5%, 8%, 12%, and 15%, and two irrigation systems, surface and subsurface drip irrigation, were compared to assess which threshold and irrigation system referred the ideal tomato seedling growth. As a result, the 12% soil moisture threshold applied through the subsurface drip irrigation system significantly (p < 0.05) increased tomato seedling growth in soil composed of a main blend of peat moss, vermiculite, and perlite. Furthermore, in two repeated experiments, a subsurface drip irrigation system with 0.86 distribution uniformity used 10% less water than the surface drip irrigation system. The produced tomato seedlings were transplanted to open fields for further assessment. A low-power wide area networking Long Range Wide Area Network (LoRaWAN) protocol was developed with remote monitoring and controlling capability for irrigation management. Two irrigation systems, including surface and subsurface drip irrigations, were used to compare which system resulted in higher tomato yields. The results showed that the subsurface drip irrigation system with 0.74 distribution uniformity produced 1243 g/plant, while each plant produced 1061 g in the surface drip irrigation system treatment. The results also indicated that the LoRaWAN-based subsurface drip irrigation system was suitable under outdoor conditions with easy operation and robust controlling capability for tomato production.

Water management is a key factor to optimize fruit quality and yield of processing tomatoes which are site-specific and influenced by environmental conditions e.g., soil, temperature, precipitation. The aim of this study was to evaluate the efficacity of a low-cost wireless soil moisture sensor in determining the irrigation level for optimizing the marketable yield, fruit quality and economic profit of processing tomato. A two-years (2017–2018) trial was conducted in open field, applying nine drip irrigation levels controlled by wireless soil moisture capacitance sensors. The irrigation levels were as follows: 13.2, 16.7, 25.4, 33.3, 50.0, 62.3, 82.5, 100 and 186.8% of water restitution based on soil moisture sensor readings. Because of the crop stress induced by heavy rainfalls occurring in 2018 growing season, total and marketable yields reached higher maximum values in 2017 than 2018. In 2017, total and marketable yields were maximized by supplying 92.8% and 96.2% of irrigation level, respectively. Moreover, 95.6% and 91.2% of irrigation level were necessary in 2018 to maximize total and marketable yield, respectively. In both growing seasons, marketable yield variation was due to changes of both fruit number and fruit mean weight. Total soluble solids of fruit juice linearly decreased by increasing the irrigation level with a more pronounced effect in the driest growing season (2017). Economic analysis demonstrated that 100% of irrigation level should be preferred by the Italian farmers since it maximized the operating margins of processing tomatoes in both years. To conclude, the use of the tested low-cost wireless soil moisture sensor is an effective tool to manage the level of irrigation and optimize the processing tomato yield and economic benefits for farmers.