Soil Humidity Sensor Research Articles

Automated Irrigation System Using Temperature Sensor and Soil Humidity Sensor

Automated Irrigation System Using Temperature Sensor and Soil Humidity Sensor

Self-driven dual reinforcement model with meta heuristic framework to conquer the iot based clustering to enhance agriculture production

In agriculture, irrigation is essential for providing water to crops based on the type of soil they are grown in. To achieve success in farming, it is important to evaluate soil fertility, temperature, rainfall and set irrigation schedules. In this work, to enhance agricultural production, an IoT-based hydration system that utilizes soil moisture and humidity sensors to keep an eye on soil conditions and water crops precisely is developed. This system effectively manages water usage in farming, resulting in an efficient conservation of water resources. The proposed model is categorized into four main phases: (a) Pre-processing; (b) Clustering; (c) Feature extraction; (d) Classification. Initially, the collected raw data is pre-processed via a data-cleaning approach. From the pre-processed data, DB scan is used to cluster the data. From the clustered data, the important feature is extracted by using statistical features like mean, standard deviation, kurtosis, and skewness. Subsequently, from the extracted features, the most optimal features are selected via a new hybrid meta-heuristic optimization model referred to as Cuckoo search-based Levy adolescent identity search algorithm (CLAIS). The projected CLAIS model is the conceptual amalgamation of the standard Cuckoo search optimization (CSO) and adolescent identity search algorithm (AISA), respectively. CLAIS-based deep Q network (CLAIS-DQN) classifier is used to classify the optimal features. DQN is an efficient solution to offload the request optimally which improves the overall performance of the network. The proposed model is implemented using the PYTHON platform. The proposed model has recorded the highest detection accuracy as 96%.

Ragi Pest Control

The rise of global population has put increasing pressure on the agriculture industry to meet the demand for food. However, the growing use of pesticides and insecticides in conventional farming practices has caused significant harm to the environment and human health. Thus, there is a growing interest in using sustainable agriculture practices that reduce the use of these harmful chemicals. One such practice is pest detection, which enables farmers to detect pests in their crops before they cause significant damage. In this context, this project aims to develop a pest detection system using IoT and Arduino. The system will be designed to detect pests in crops through a combination of sensors and machine learning algorithms. The system will consist of an Arduino microcontroller, soil moisture sensors, temperature and humidity sensors, infrared sensors or camera modules, and a Wi-Fi or Bluetooth module. The sensors will collect data on soil moisture levels, temperature, humidity, and pest activity. The data will be sent to a cloud-based server or database for analysis and visualization. The infrared sensor or camera module will detect the presence of pests in the crops. The system will use machine learning algorithms to distinguish between pests and other objects, such as leaves or debris. When pests are detected, the system will alert the farmer through a buzzer or LED connected to the Arduino board. The farmer can then take appropriate action, such as applying pesticide or removing infested plants. The pest detection system has the potential to reduce the use of harmful pesticides and insecticides in agriculture, as farmers will be able to identify pests before they cause significant damage. The system will also provide farmers with real-time information on pest activity, enabling them to take proactive measures to control pests and reduce crop damage. Additionally, the system can track and store the pest detection data over time, allowing farmers to monitor trends and patterns in pest activity. In conclusion, this project proposes the development of a pest detection system using IoT and Arduino that will enable farmers to monitor pest activity in their crops in real-time. The system has the potential to reduce the use of harmful chemicals in agriculture and improve crop yield while ensuring sustainable and environmentally friendly practices.

Design of Hybrid Sprinkler: The IOT-Powered Robot for Watering Plants

Innovative methods for watering plants have been developed as a result of the growing demand for efficient and automated systems in agriculture. The design of a hybrid sprinkler, an IoT-powered robot created specifically for effective and precise plant irrigation, is presented in this study. The suggested system enables autonomous operation and intelligent control by fusing the benefits of conventional sprinklers with those of IoT technology. To continuously monitor the environmental conditions, the hybrid sprinkler integrates a variety of sensors, including soil moisture sensors, temperature sensors, and humidity sensors. A central control unit receives this real-time data and uses sophisticated algorithms to establish the best watering schedule for each plant depending on its individual needs. The hybrid sprinkler's design prioritizes sustainability and energy economy. The robot has a rechargeable battery system, and to increase battery life, it uses energy-saving features like sleep modes and sophisticated power management. In a variety of agricultural and horticultural applications, this system assures optimal watering practises, conserves resources, and fosters healthy plant growth by fusing IoT technology, cutting-edge sensors, precision spraying mechanisms, and remote control capabilities.

Development of a sandy soil water content monitoring system for greenhouses using Internet of Things

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.

Vertical Farming using Smart Farming

Nutrient content (Nitrogen, Phosphorous, Potassium) monitoring in soils is essential for the proper use of fertilizers to minimize the environmental impact of wrong pattern fertilization practice. Collecting samples from different locations and laboratory testing takes longer and costs a lot. New-generation digital sensors are smart enough to replace chemical lab testing in real-time with minimum effort and with almost precise results. The Proposed system tends to implement various suggestions that would also help improve the quality of the work being done by the soil testing laboratories. By this, we can able to test our soil parameter in that condition quickly in our field for further processes and we will get the soil parameter report without any delay. And we can get information about the fertilizer and crop matches for that result and the number of fertilizers to be used. And to demonstrate soil humidity sensors and pH sensors to measure the NPK values which determine the fertility of the soil, which helps to improve cultivating system. The results obtained from our developed sensing system are almost accurate and very close to the laboratory test readings. Arduino plays a key role in processing data received from the sensor as input, the counters used here produce the respected output values for the parameters. There the values received by the Arduino are compared with the database and provide related information which is matched to the current calculated values. The output values provided by the Arduino and sensors will be displayed on the LCD. Here the output not only provides formation on fertility present in the soil but also suggests crops to be grown on that soil. Keywords—Vertical farming, smart farming, nutrient monitoring, digital sensors, soil fertility, crop suggestions.

Development of prototype and measurement, control and automation system in real time for small production greenhouses

The main objective of this work is to develop a real-time control system and accessible instrumentation for small production greenhouses located in the State of Guanajuato; whose goal is to increase production, make optimal use of the irrigation system and efficient water management. For this, a prototype of a greenhouse irrigation system was built with the inclusion of temperature sensors, environmental relative humidity, flowmeters, solenoid solenoid valves and soil humidity sensors, these values are compared with a numerical model to corroborate the magnitudes obtained and assess the delivery of flow and irrigation time in each section of the laboratory greenhouse. The development of a graphical interface in LabVIEW for the control and operation of sensors and the complete irrigation system stands out, being this quite intuitive and easy to use for a farmer. The results obtained from the work show differences between the measured and modeled of the hydrodynamic variables in a range of 8 to 10%, where it is concluded in an improvement in the development of the code in Arduino for its optimization in response time and migration to mobile platforms.

Smart Agriculture Monitoring System

Abstract: In this morden industry remote monitoring and controlling equipment at farm from a long distance is challenging now a days. At present we are able to control the equipment with the help of smartphones using IOT. This paper presents a novel of smart agriculture system using ATmega328P with global connection using Internet of Things (IOT). Internet of Thing (IOT) plays a important role in smart agriculture system. Smart agriculture helps to reduce the farmers work. It works automatically or farmer can operate it from anywhere. Smart agriculture monitoring system used wireless sensor network that collect all live information from different sensors and send that data through wireless protocol. Sensors that are used in system provides information about agriculture field. This project is developed to monitor crop-field using sensors (Soil Moisture Sensor, Rain Sensor, Temperature and Humidity Sensor).

Design and Implementation of A Simple Pot Watering Control System

People who like to care for plants need to take care of plants in addition to the work pressure, which consumes people's time and energy. To solve this problem, an intelligent pouring system based on STM32 and WiFi module ESP8266 was designed. The system took STM32F103 development board as the main control core, combined with ESP8266, soil moisture sensor, temperature and humidity sensor DHT11, photosensitive resistance and other sensors to carry out data transmission to realize temperature and humidity monitoring of flowers. The system can carry out local control through the terminal, realize manual mode control based on MQTT platform and Java language, and set alarm threshold of each parameter. The results show that the system can be applied to different types of plants, which provides convenience for people to conserve plants.

Prediction and monitoring model for farmland environmental system using soil sensor and neural network algorithm

Abstract In this study, data fusion algorithm is used to classify the soil species and calibrate the soil humidity sensor, and by using edge computing and a wireless sensor network, farmland environment monitoring system with a two-stage calibration function of frequency domain reflectometer (FDR) is established. Edge computing is used in system nodes, including the saturation value of the soil humidity sensor, the calculated soil hardness, the calculation process of the neural network, and the model of soil classification. A bagged tree is adopted to avoid over-fitting to reduce the prediction variance of the decision tree. A decision tree model is established on each training set, and the C4.5 algorithm is adopted to construct each decision tree. After primary calibration, the root mean squared error (RMSE) between the measured and standard values is reduced to less than 0.0849%. The mean squared error (MSE) and mean absolute error (MAE) are reduced to less than 0.7208 and 0.6929%. The bagged tree model and backpropagation neural network are used to classify the soil and train the dynamic soil dataset. The output of the trained neural network is closer to the actual soil humidity than that of the FDR soil humidity sensor. The MAE, the MSE, and the RMSE decrease by 1.37%, 3.79, and 1.86%. With accurate measurements of soil humidity, this research shows an important guiding significance for improving the utilization efficiency of agricultural water, saving agricultural water, and formulating the crop irrigation process.

IOT-BASED SMART GARDENING SYSTEM USING ARDUINO UNO

Abstract—Environmental awareness is one of the most important factors for environmental sustainability. This paper describes a Smart gardening system using an IoT platform. The main purpose of this automation is to make home gardening more convenient by eliminating manual work. The Soil Moisture Sensor has many applications in agricultural research, everyday gardening, and humidity and temperature sensors. By tracking the amount of water in your soil, you can ensure your valuable crops stay hydrated and know how they’re doing before planting. Our project uses a soil moisture meter and a humidity and temperature sensor using the ESP8266 to alert the user when plants need to be watered. Next, we built an automatic irrigation system using the IoT board. Our system also includes an OLED display and uses an IoT cloud, so you can monitor and control the system over the internet. PIR sensors are built into the system to help prevent pests from getting closer to your plants or destroy plants. Additionally, after much research, our project includes a purple LED light to stimulate plant growth. Index Terms—IoT, ESP8266 micro controller, soil moisture sensor, humidity sensor and temperature sensor, PIR sensor, purple LED lights

The Design of Soil Temperature and Humidity Monitoring Systems with IoT-Based LoRa Technology

Soil temperature and humidity are important factors in affecting the condition of agricultural sector, which has an impact on the quality and quantity of the production. Lack of information on the condition of agricultural soil is one of the causes in productivity deficiency in the process of agricultural cultivation. The application of technology in the field of agriculture is expected to be able to reduce various adverse effects of agricultural soil conditions. One of which is by periodic monitoring, such as the temperature and humidity of agricultural soil. This research aims to design LoRa technology to be used as a data transmission medium for monitoring soil temperature and humidity by applying a system that is based on the Blynk application, which will make the users easier to monitor the system remotely. The temperature sensor was able to acquire data with 98.37% accuracy and the soil humidity sensor was able to acquire data with 91.63% accuracy. The changes in LoRa transmission parameters for monitoring data have an effect on the quality of its performance. The experimental results with Bandwidth variation (BW) from 31.25 kHz, 62.50 kHz, 125 kHz, 250 kHz, and 500 kHz at a distance of 15m, the best SNR and RSSI values were obtained for BW 31.25 kHz with values of 5.42 dB and -104.90 dBm. Whereas, the best ToA is obtained with a BW of 500 kHz with a value of 27.50 ms. While, the experimental result with the variation of Coding Rate (CR) from CR 4/5, 4/6, 4/7, and 4/8 at a distance of 15m, the best SNR and RSSI values were obtained CR 4/8 with values of 4.10 dB and -106.40 dBm and he best ToA was obtained CR 4/5 with a value of 112.70 ms. In testing by using variation Spreading Factor (SF) from SF7, SF9, and SF12, the higher the SF value used, the wider the range of area data communication will be. Configuration SF7 and SF9 were only able to reach a distance of 25m, while SF12 was able to reach a distance of 35m.

Smart Plants: Low-Cost Solution for Monitoring Indoor Environments

Humans tend to spend most of their life indoors, making the quality of indoor environments essential for human health and wellbeing. While several solutions for monitoring the indoor environment have been proposed, ranging from infrastructure-based monitoring solutions to cameras, these tend to require separate installation, making the sensors difficult to maintain and upgrade. In this article, we introduce the idea of using smart plants as an easy-to-deploy and affordable solution for monitoring the indoor environment. Plants are typically deployed close to humans and they increasingly are placed in containers that integrate sensors, such as soil moisture, temperature, humidity, and CO2 sensors. We demonstrate how these sensors can be used as an alternative technology for monitoring—and enriching—indoor spaces without needing to install proprietary sensors or other technology. Specifically, we show how smart plants can be used to estimate overall CO2 accumulation, occupancy information, and whether people use protective face masks or not. We also establish a research roadmap for the use of smart plants to monitor indoor environments.

Inversion model of surface bare soil temperature and water content based on UAV thermal infrared remote sensing

Soil moisture content is important to agricultural monitoring, ecological restoration and landslide warning. Traditional methods cannot monitor soil moisture content in large area, high precision and continuous data. Unmanned aerial vehicle (UAV) remote sensing monitoring has enabled wide-scale water content monitoring, improving the spatial resolution accuracy to centimeter level and the temporal resolution accuracy to minute level, which makes UAV remote sensing favorable for soil moisture content monitoring. In this paper, to achieve rapid and accurate inversion data of bare surface soil moisture content via UAV thermal infrared remote sensing, UAV is used to obtain a visible light map of the experiment area and a thermal infrared radiation map at 9:00 am, 1:00 pm, and 6:00 pm in a given day. Gray value of thermal infrared radiation is based on Planck’s blackbody radiation law and quadratic polynomial fitting to invert temperature data; the ground soil temperature and humidity sensor provides sample areas of the measured temperature and soil moisture content. The linear relationship between gray value of thermal infrared radiation, brightness temperature, measured temperature, and soil moisture content was obtained by analyzing the data. The experimental results show that there is a good linear relationship between the brightness temperature and water content, and the brightness t temperature R2 at 6 PM is better than the measured one. Finally, a model for calculating the surface bare soil moisture content based on brightness temperature is established. RMSE and RPD were used to verify the accuracy of the inversion model. The model accuracy verification results show that the inversion model at 6 pm has a good accuracy, which verifies the feasibility and accuracy of unmanned aerial vehicle thermal infrared remote sensing monitoring of surface bare soil moisture content.

IoT Based Smart Agricultural System for Efficient Crop Growth

Population growth has made agriculture the most important growth sector in the world. The challenge in the agricultural industry right now is to improve the efficiency and quality of agriculture without regular physical monitoring to meet the rapidly increasing food demand. Along with population growth, climatic condition is also a major challenge in agriculture. The purpose of this study is to propose an internet-based smart agricultural model of things that uses clustering to deal with adverse conditions. This model uses different types of sensors for different purposes, such as soil moisture, barometric pressure, rainfall detection, and humidity sensors. The data is collected in the cloud and calculated automatically. Intelligent agriculture is automatically taken over by plant management, useful data collection and analysis. The purpose of this white paper is to implement the Internet of Things (IoT) to monitor humidity, soil conditions, temperature and field water supply, water levels, and climatic conditions. The IoT-based smart farming system envisioned in this report is integrated with a variety of sensors and a Wi-Fi module that produces live data feeds that can be accessed online. Keywords: Smart farming, IOT, Clustering, Sensors, Cloud.

Optimizing irrigation for boosting gynura procumbens growth in Malaysia urban area

<p><span>Growing herbs traditionally could not meet the increasing demand for high medicinal value herbs in the pharmaceutical industry and domestic market. One of the solutions is by growing herbs using vertical structures in urban areas. Even so, the amount of water needed to optimise the growth of Asian local perennial herbs in vertical structures is yet to be explored. Hence, this paper investigated the performance of a fuzzy based irrigation method in optimising irrigation for boosting the local perennial herb growth. The understudy local perennial herb is Gynura Procumbens. The decision-making for irrigation relies on the data given by soil moisture, temperature, and humidity sensors. The performance of the proposed system is compared with a timer-based system, in terms of plant growth rates, given by average leaves diameter, height, and plant crown of local perennial herbs. The results have shown that the proposed intelligent irrigation system has reduced water consumption by 16.93% and the average plant growth rate has increased by at least 1.5% and to a maximum rate of 76.64%.</span></p>

Highly sensitive few-layer MoS2 nanosheets as a stable soil moisture and humidity sensor

In this work, an MoS2 nanosheet based ultrasensitive humidity and soil moisture sensor has been developed. MoS2 nanosheets are obtained by sonication based surfactant-induced exfoliation. The performance of the sensor is examined at room temperature (25 ∘C) with respect to various relative humidity (RH) levels and soil water content (θg). A 434 fold variation (35–15200 pF) in capacitance value is seen when the relative humidity level changes from 11% to 96%. The response and recovery times of the microsensor are approximately 30 s and 40 s, respectively. The capacitance of the MoS2 based sensor increases monotonically when the soil moisture content increases from 3.6% to 53% and 2.3–45% for black and red soil, respectively. The observed variation in capacitance corresponding to θgB = 53% (black soil moisture content) and θgR = 45% (red soil moisture content) is approximately 250 and 200 times that observed for the respective dry soil samples. The error in the soil moisture response measurement for the temperature variation from 25 ∘C to 70 ∘C is ± 2.52%. The device features stable performance for six months with reasonably high accuracy (maximum error ± 1.50%). The soil sensing results show fast response (35 s), negligible hysteresis error (2.80% at θgB = 31.9%), long-term stability (6 months and more), and good reproducibility (with more than 10 cycles).

The effect of smart fertigation systems on chilli grown in a greenhouse for urban farming

AbstractReasonable fertigation methods are essential for plant growth, as excessive or inadequate water and fertilizer supply can lead to plant diseases, resource consumption, or even crop production reduction. These problems are common to local farmers in Malaysia, especially for large‐scale chilli cultivation using traditional irrigation approaches in rural areas. These problems need to be addressed, as chilli are one of Malaysia's high‐cash crops with massive local demands. Realizing an efficient irrigation control approach using an innovative fertigation method is a potential solution to tackle such problems. This paper proposes a smart fertigation system for chilli cultivation in greenhouses in urban areas to increase chilli production and improve irrigation management. The proposed system utilized the fuzzy logic method for intelligent irrigation decisions, where the decision is based on the input data provided by soil moisture, temperature, and humidity sensors. In addition, the proposed system can monitor and control irrigation in the greenhouse remotely through a website that was developed using the Hostinger platform. The results showed that the fuzzy‐based fertigation approach produced approximately 50% irrigation application efficiency and a higher growth gradient and chilli yield than the traditional approach in both cycles.

Design and implementation of solar-powered with IoT-Enabled portable irrigation system

This paper proposes a solar-powered portable water pump (SPWP) for IoT-enabled smart irrigation system (IoT-SIS). A NodeMCU microcontroller with a Wi-Fi interface and soil moisture, temperature, and humidity sensors are exploited to monitor and control the water pump and build an IoT-based irrigation system. The proposed irrigation system updates the gathered information from sensors (moisture, temperature, and humidity) using an integrated algorithm to the Blynk IoT cloud in real-time. Farmers can access this information and control the water pump accordingly via a user-friendly interface using their smartphones. The portable and eco-friendly water pump is powered via a solar panel and can be controlled using Blynk mobile application, which is also used to monitor the surroundings. The fabricated pump is inspired by wheeled travel luggage and is equipped with a water filter and multi-nozzles sprayer. The developed solar-based water pump has managed to save electricity and mitigate operational costs. Furthermore, the integration of the IoT concept has facilitated real-time monitoring and control of the pump; thus, enhancing the water usage efficiency and enabling convenient farming operations. The system functionality has been practically tested in a real environment, and its performance has been evaluated.

Smart Irrigation System using IoT

In this work we use drip irrigation where the water was allowed to drip slowly to the roots of plant either from above the soil surface or buried into the surface so that the water can be placed directly into the root zone and minimize evaporation. It uses temperature sensor, soil humidity sensor to collect and monitor field information and also uses float switches to monitor ground water level through web page. When the field gets dry and ground water level falls down it will be notified through SMS. This provides a solution for the problems in developing a smart farming system. It uses node MCU, relay and water pump.