Soil Moisture Monitoring Research Articles

Inversion of Soil Moisture Content in Silage Corn Root Zones Based on UAV Remote Sensing

Accurately monitoring soil moisture content (SMC) in the field is crucial for achieving precision irrigation management. Currently, the development of UAV platforms provides a cost-effective method for large-scale SMC monitoring. This study investigates silage corn by employing UAV remote sensing technology to obtain multispectral imagery during the seedling, jointing, and tasseling stages. Field experimental data were integrated, and supervised classification was used to remove soil background and image shadows. Canopy reflectance was extracted using masking techniques, while Pearson correlation analysis was conducted to assess the linear relationship strength between spectral indices and SMC. Subsequently, convolutional neural networks (CNNs), back-propagation neural networks (BPNNs), and partial least squares regression (PLSR) models were constructed to evaluate the applicability of these models in monitoring SMC before and after removing the soil background and image shadows. The results indicated that: (1) After removing the soil background and image shadows, the inversion accuracy of SMC for CNN, BPNN, and PLSR models improved at all growth stages. (2) Among the different inversion models, the accuracy from high to low was CNN, PLSR, BPNN. (3) From the perspective of different growth stages, the inversion accuracy from high to low was seedling stage, tasseling stage, jointing stage. The findings provide theoretical and technical support for UAV multispectral remote sensing inversion of SMC in silage corn root zones and offer validation for large-scale soil moisture monitoring using remote sensing.

Cotton Irrigation Scheduling: Which Approach is the Best Fit for Georgia?

Cotton (Gossypium hirsutum) is one of the most difficult crops to manage irrigation effectively due to the crop’s perennial physiology. In recent years, many new technologies have been developed to help improve irrigation management. The main objective of this study was to evaluate various irrigation management tools and to assist farmers in determining which method is best for their operation. Other objectives included monitoring soil moisture and determining the optimal irrigation application point of each method by logging total rainfall and irrigation distribution throughout the growing season. A three-year study was conducted at the University of Georgia (UGA) Stripling Irrigation Research Park near Camilla, GA where cotton was grown on loamy sand soil. A lateral movement, overhead sprinkler system equipped with a variable rate system allowed plots to be irrigated independently based on treatment. Irrigation treatments included 20- and 45-kPa weighted average soil water tension (SWT) measurements made using three Watermark SWT sensors placed in two of the three replicates. The UGA SmartIrrigation Cotton app (SI app), UGA Checkbook method, and a rainfed check were included in the trial. Each irrigation method was evaluated based on crop yield, irrigation water-use efficiency, and profitability. The analysis revealed significant variations in several metrics between treatments and validates the 45-kPa SWT threshold and SI app are top-performing advanced irrigation scheduling tools and showed the importance of advanced irrigation scheduling and the strengths and weaknesses of each method.

Sustainable and Low-Cost Greenhouse Soil Moisture Monitoring Using Battery-Free RFID Sensors

Intelligent irrigation based on measurements of soil moisture levels in every pot in a greenhouse can not only improve plant productivity and quality but also save water. However, existing soil moisture sensors are too expensive to deploy in every pot. We therefore introduce GreenTag, a low-cost RFID-based soil moisture sensing system whose accuracy is comparable to that of an expensive soil moisture sensor. Our key idea is to attach two RFID tags to a plant’s container so that changes in soil moisture content are reflected in their Differential Minimum Response Threshold (DMRT) metric at the reader. We show that a low-pass filtered DMRT metric is robust to changes both in the RF environment (e.g., from human movement) and in pot locations. In addition, we propose a fast DMRT acquisition algorithm and a time-efficient tag query protocol, which can reduce the sensing latency by 90%. In a realistic setting, GreenTag achieves a 90-percentile moisture estimation errors of 5%, which is comparable to the 4% errors using expensive soil moisture sensors. Moreover, this accuracy is maintained despite changes in the RF environment and container locations. We also show the effectiveness of GreenTag in a real greenhouse.

State updating of the Xin'anjiang model: joint assimilating streamflow and multi-source soil moisture data via the asynchronous ensemble Kalman filter with enhanced error models

Abstract. Assimilating either soil moisture or streamflow individually has been well demonstrated to enhance the simulation performance of hydrological models. However, the runoff routing process may introduce a lag between soil moisture and outlet discharge, presenting challenges in simultaneously assimilating the two types of observations into a hydrological model. The asynchronous ensemble Kalman filter (AEnKF), an adaptation of the ensemble Kalman filter (EnKF), is capable of utilizing observations from both the assimilation moment and the preceding periods, thus holding potential to address this challenge. Our study first merges soil moisture data collected from field soil moisture monitoring sites with China Meteorological Administration Land Data Assimilation System (CLDAS) soil moisture data. We then employ the AEnKF, equipped with improved error models, to assimilate both the observed outlet discharge and the merged soil moisture data into the Xin'anjiang model. This process updates the state variables of the model, aiming to enhance real-time flood forecasting performance. Tests involving both synthetic and real-world cases demonstrates that assimilation of these two types of observations simultaneously substantially reduces the accumulation of past errors in the initial conditions at the start of the forecast, thereby aiding in elevating the accuracy of flood forecasting. Moreover, the AEnKF with the enhanced error model consistently yields greater forecasting accuracy across various lead times compared to the standard EnKF.

Simultaneous vibration and soil moisture sensing using a single mode fiber for structural health monitoring applications

A dual-purpose single mode optical fiber sensor was developed for simultaneous soil moisture and structural health monitoring. Unmodified G655 SMF with polarization coupled phase components was used to detect environmental changes due to ground movement and moisture content variations. Spectral distribution profiles of the baseband frequencies below 10 Hz were analyzed for structural health monitoring, while resonant peak frequencies higher than 110 Hz had notable dependence on the damping effect of moisture on the spectral peak intensity of the detected acoustic waves. Using these peaks, moisture content was measured within a root mean square error value of +/−0.04. The sensor demonstrates a critical resource optimization tool for distributed sensing applications and monitoring in PON systems.

Quantifying the potential of using Soil Moisture Active Passive (SMAP) soil moisture variability to predict subsurface water dynamics

Abstract. Advances in satellite Earth observation have opened up new opportunities for global monitoring of soil moisture (SM) at fine to medium resolution, but satellite remote sensing can only measure the near-surface soil moisture (SSM). As such, it is critically important to examine the potential of satellite SSM measurements to derive the water resource variations in deeper subsurface. This study compares the SSM variability captured by the Soil Moisture Active and Passive (SMAP) satellite and the Soil Water Index (SWI) derived from SMAP SSM with subsurface SM and groundwater (GW) dynamics simulated by a high-resolution fully integrated surface water–groundwater model over an agriculturally dominated watershed in eastern Canada across two spatial scales, namely SMAP product grid (9 km) and watershed (∼4000 km2). SMAP measurements compare well with the hydrologic simulations in terms of SSM variability at both scales. Simulated subsurface SM and GW storage show lagged and smoother characteristics relative to SMAP SSM variability with an optimal delay of ∼1 d for the 25–50 cm SM, ∼6 d for the 50–100 cm SM, and ∼11 d for the GW storage for both scales. Modeled subsurface SM dynamics agree well with the SWI derived from SMAP SSM using the classic characteristic time lengths (15 d for the 0–25 cm layer and 20 d for the 0–100 cm layer). The simulated GW storage showed a slightly delayed variation relative to the derived SWI. The quantified optimal characteristic time length Topt for SWI estimation (by matching the variations in SMAP-derived SWI and modeled root zone SM) is comparable to Topt obtained in other agricultural regions around the world. This work demonstrates SMAP SM measurements as a potentially useful aid when predicting root zone SM and GW dynamics and validating fully integrated hydrologic models across different spatial scales. This study also provides insights into the dynamics of near-surface–subsurface water interaction and the capabilities and approaches of satellite-based SM monitoring and high-resolution fully integrated hydrologic modeling.

A Deep Learning Approach to Predict Surface Soil Wetness and Its Uncertainty Analysis Over the Tel River Basin, India

ABSTRACTSurface soil moisture (SSM) refers to the capacity of the top layer of soil to hold moisture. It is an essential part of the budget for surface water. Soil moisture monitoring is crucial to reduce the effects of precipitation deficits and determine the best ways to manage natural ecosystems in the face of climate change. The current study collected daily SSW data from MERRA‐2 for the Tel River Basin in Odisha, India, from 2001 to 2020 with a spatial resolution of 0.5° × 0.625°. To forecast SSW time series (SSWTS) one step ahead, this study examines the reliability of three deep learning (DL) models: gated recurrent unit (GRU), long short‐term memory (LSTM), and simple recurrent neural network (simpleRNN). This study aims to address the following research questions: (1) How accurately can DL models predict SSWTS? (2) Which DL model—GRU, LSTM, or simpleRNN—is the most reliable for SSW forecasting? (3) How can the uncertainty in the predicted SSW be quantified and analyzed? Further, in an uncertainty investigation on SSW projected values, a Wilson score technique was employed to evaluate the uncertainty of the DL methods. GRU has outdone the other two models in forecasting monthly SSW with a 12‐lookback timestep with a lower error for all the stations. The model appeared more accurate as it declined in gradient on larger sequencing samples. GRU's ability to remember significant prior knowledge, whereas discarding irrelevant data may assist in finding a novel, dependable solution for SSWTS forecasting.

Sistema inteligente para monitoramento automatizado da umidade do solo

ABSTRACT Water application to cultivated soil is often done without careful consideration in irrigated agriculture, leading to inefficient or suboptimal water usage. In many instances, the intricate relationship between soil, water, and plants, as well as the potential and limitations of irrigation systems, is overlooked. Sustainable irrigated agriculture necessitates the development of a soil moisture monitoring system that curtails water loss and enhances overall efficiency. This study aimed to develop and assess the efficiency of an intelligent system for monitoring soil moisture. The system comprises two stations: the first collects data on apparent soil moisture parameters using sensors, while the second transmits this data to a central processing station. The system precisely determines current soil moisture values, enabling estimation of the required irrigation water volume to meet the crop’s water demand based on field capacity. Results from the calibration curve of the sensors indicate that the system can measure current soil moisture precisely, aiding in irrigation management. For irrigated areas under unsaturated soil conditions, it is recommended to use tensiometers due to their higher reliability between field capacity and permanent wilting point, ensuring more accurate irrigation practices.

Soybean Water Monitoring and Water Demand Prediction in Arid Region Based on UAV Multispectral Data

Soil moisture content is a key factor influencing plant growth and agricultural productivity, directly impacting water uptake, nutrient absorption, and stress resistance. This study proposes a rapid, low-cost, non-destructive method for dynamically monitoring soil moisture at depths of 0–200 cm throughout the crop growth period under dryland conditions, with validation in soybean cultivation. During critical soybean growth stages, UAV multispectral data of the canopy were collected, and ground measurements were conducted for three GPS-referenced 50 cm × 50 cm plots to obtain canopy leaf water content, coverage, and soil volumetric moisture at 20 cm intervals. Ten vegetation indices were constructed from multispectral data to explore statistical relationships between vegetation indices, surface soil moisture, canopy leaf water content, and deeper soil moisture. Predictive models were developed and evaluated. Results showed that the NDVI-based nonlinear regression model achieved the best performance for leaf water content (R2 = 0.725), and a significant correlation was found between canopy leaf water content and 0–20 cm soil moisture (R2 = 0.705), enabling predictions of deeper soil moisture. Surface soil models accurately estimated 0–200 cm soil moisture distribution (R2 = 0.9995). Daily water dynamics simulations provided robust support for precision irrigation management. This study demonstrates that UAV multispectral remote sensing combined with ground sampling is a valuable tool for soybean water management, supporting precision agriculture and sustainable water resource utilization.

Development of an Automated Drip Irrigation System using Arduino Microcontroller for Sweet Corn

This study proposes an automated irrigation system using an Arduino microcontroller that is cost-effective and reliable for use in planting. The proposed drip irrigation system was developed to automatically irrigate the sweet corn seedlings from 0–4 weeks of age by detecting water insufficiency based on the moisture percentage of the soil used. The automated irrigation system was a fully functional prototype where the crop was irrigated automatically when the soil moisture sensor sensed a soil moisture value below 40%. The system consists of Arduino microcontroller, sensors to monitor soil moisture, ambient temperature and humidity; an LCD display to show the sensors readings; a relay module that is used to control the on and off switch of the water pump. The performance of the automated system development was then evaluated to ensure proper irrigation. The relay module was found switched on the water pump automatically to start the watering process when the soil moisture value below 40%. The data on soil moisture percentage (%), ambient temperature (°C), and ambient humidity (%) was displayed on an LCD screen that allowed real-time monitoring. The range of temperature and humidity were recorded around 310C to 400C and 62% to 80% respectively affecting the reduction of soil moisture percentage from 80% to 44%. In conclusion, an Arduino Uno-based automated plant watering system has been successfully installed and performed.

Potential of the Advanced Precision Irrigation Techniques for Enhanced Protected Cultivation Systems in the Developing Nations

Precision irrigation techniques have revolutionized protected cultivation systems worldwide by optimizing water use efficiency, reducing resource consumption, and enhancing crop yield and quality. Key technologies covered include drip irrigation, micro-sprinklers, subsurface irrigation, sensors for monitoring soil moisture and crop water status, automated irrigation scheduling software, and the integration of these tools with fertigation systems. Case studies from leading horticultural regions illustrate best practices and the benefits of precision irrigation, such as water savings of 40-70%, fertilizer reductions of 30-50%, and yield improvements of 20-40% compared to conventional irrigation. However, challenges remain in terms of high initial costs, maintenance requirements, and the need for grower training and technical support. In Asia and India, government initiatives and public-private partnerships are driving the expansion of protected cultivation with precision irrigation to boost productivity, conserve resources, ensure food security, and increase smallholder incomes. Future directions emphasize sensor-based automation, data-driven decision support systems, crop-specific precision irrigation strategies, and the integration of precision irrigation with other technologies like hydroponics, vertical farming, and renewable energy to further enhance the sustainability and profitability of protected cultivation.

Wi-Fi signal for soil moisture sensing

Measuring soil moisture is essential in various scientific and engineering disciplines. Over recent decades, numerous technologies have been employed for in situ monitoring of soil moisture. Currently, dielectric-based sensors are the most popular measurement technology and provide acceptable accuracy for various measurement purposes. However, these sensors are relatively expensive, and alternative technologies, which are cheaper, are not accurate enough for scientific purposes. Recently, the idea of using a Wi-Fi signal to measure soil moisture has been presented. Theoretically, the use of Wi-Fi technology in soil sensing follows the same concepts as the previous dielectric sensors. The main advantage of Wi-Fi technology is the possibility of providing a relatively accurate and cost-effective solution for soil moisture measurement. In this work, we try to investigate the possibility of using Wi-Fi signal characteristics for soil sensing. Therefore, a series of small-scale laboratory and field experiments were conducted to test the concept. The results of these experiments were promising, showing strong linear relationships between Wi-Fi signal properties (received signal strength indicator, RSSI) and soil water content, with R2 values ranging between 0.92 and 0.99, indicating a strong correlation. They also illustrate the possibility of using this technology to develop an inexpensive and accurate device for measuring soil moisture. However, observations from the experiments also point to problematic factors involving the hardware and software used in the measurements. It is important to control these factors in the next steps to develop a reliable measurement device.

Smart Agri Bricks: IOT-Enabled Future Farming (IOT-Enabled Future Farming With Biodegradable Solutions)

The Smart Agri Bricks project presents an innovative solution to environmental and agricultural challenges by transforming waste materials into sustainable, smart-integrated bricks. The project focuses on reducing plastic pollution, managing biodegradable waste, and improving water efficiency in agriculture. Through the collection and separation of mixed waste, including plastics and biodegradable materials, the waste is repurposed into durable bricks. These bricks are embedded with IoT sensors to monitor soil moisture and water levels, enabling precision irrigation and promoting water conservation. The experimental phase includes waste processing, brick production, and field trials to assess the effectiveness of the solution in real-world agricultural settings. Initial results indicate significant improvements in plastic waste management, brick durability, and water efficiency. This approach highlights the potential for integrating sustainable practices into both waste management and agricultural systems, offering a scalable solution to pressing global issues.

An Overview of Within-Season Agricultural Monitoring from Remotely Sensed Data

Remote sensing data have been widely used to monitor various agricultural activities, such as crop distribution mapping, crop phenology extraction, farmland soil moisture monitoring, crop diseases prevention, and crop ideotype breeding [...]

Improved representation of soil moisture processes through incorporation of cosmic-ray neutron count measurements in a large-scale hydrologic model

Abstract. Profound knowledge of soil moisture and its variability plays a crucial role in hydrological modelling to support agricultural management, flood and drought monitoring and forecasting, and groundwater recharge estimation. Cosmic-ray neutron sensing (CRNS) has been recognised as a promising tool for soil moisture monitoring due to its hectare-scale footprint and decimetre-scale measurement depth. But since CRNS provides an integral measurement over several soil horizons, a direct comparison of observed and simulated soil moisture products is not possible. This study establishes a framework to assess the accuracy of soil moisture simulated by the mesoscale Hydrologic Model (mHM) by generating simulated neutron counts and comparing these with observed neutron measurements for the first time. We included three different approaches to estimate CRNS neutron counts in the mHM as a function of the simulated soil moisture profiles: two methods based on the Desilets equation and one based on the forward operator COSMIC (COsmic-ray Soil Moisture Interaction Code). For the Desilets method, we tested two different approaches to average the vertical soil moisture profiles: a uniform vs. a non-uniform weighting scheme depending on the CRNS measurement depth. The methods were tested at two agricultural sites, namely one pasture site and one forest site, in Germany. To explore the prior and posterior distributions of the mHM parameters when constrained by CRNS observations, we used a Monte Carlo method based on Latin hypercube sampling with a large sample size (S = 100 000). We found that all three methods performed well, with a Kling–Gupta efficiency > 0.75 and a percent bias < ± 10 % across the majority of investigated sites and for the best 1 % of parameter sets. The performance of the neutron forward models varied slightly across different land cover types. The non-uniform approach generally showed good performance, particularly at the agricultural sites. On the other hand, the COSMIC method performed slightly better at the forest site. The uniform approach showed slightly better results at the grassland site. We also demonstrated for the first time that the incorporation of CRNS measurements into the mHM could improve both the soil moisture and the evapotranspiration products of the mHM. This suggests that CRNS is capable of improving the model parameter space in general and adds a broader perspective on the potential of CRNS to support large-scale hydrological and land surface models.

Improving the Calibration of Low-Cost Sensors Using Data Assimilation.

In the context of smart agriculture, accurate soil moisture monitoring is crucial to optimise irrigation, improve water usage efficiency and increase crop yields. Although low-cost capacitive sensors are used to make monitoring affordable, these sensors face accuracy challenges that often result in inefficient irrigation practices. This paper presents a method for calibrating capacitive soil moisture sensors through data assimilation. The method was validated using data collected from a farm in Dos Hermanas, Seville, Spain, which utilises a drip irrigation system. The proposed solution integrates the Hydrus 1D model with particle filter (PF) and the Iterative Ensemble Smoother (IES) to continuously update and refine the model and sensor calibration parameters. The methodology includes the implementation of physical constraints, ensuring that the updated parameters remain within physically plausible ranges. Soil moisture was measured using low-cost SoilWatch 10 capacitive sensors and ThetaProbe ML3 high-precision sensors as a reference. Furthermore, a comparison was carried out between the PF and IES methods. The results demonstrate that the data assimilation approach markedly enhances the precision of sensor readings, aligning them closely with reference measurements and model simulations. The PF method demonstrated superior performance, achieving an 84.8% improvement in accuracy compared to the raw sensor readings. This substantial improvement was measured against high-precision reference sensors, confirming the effectiveness of the PF method in calibrating low-cost capacitive sensors. In contrast, the IES method showed a 68% improvement in accuracy, which, while still considerable, was outperformed by the PF. By effectively mitigating observation noise and sensor biases, this approach proves robust and practical for large-scale implementations in precision agriculture.

Modeling water balance components of conifer species using the Noah-MP model in an eastern Mediterranean ecosystem

Abstract. Few studies have investigated the performance of land surface models for semiarid Mediterranean forests. This study aims to parameterize and test the performance of the Noah-MP land surface model for an eastern Mediterranean ecosystem. To this end, we calibrated the model for root zone soil moisture and transpiration of two conifer species, Pinus brutia, and Cupressus sempervirens, in a plantation forest on the Mediterranean island of Cyprus. The study area has a long-term average annual rainfall of 315 mm. Observations from 4 sap flow and 48 soil moisture sensors, for the period from December 2020 to June 2022, were used for model parameterization. A local sensitivity analysis found that the surface infiltration (REFKDT), hydraulic conductivity (SATDK), and stomatal resistance (RSMIN) parameters had the highest impacts on the water balance components (soil evaporation, tree transpiration, surface runoff, and drainage). The model performed better during the wetter 9-month validation period (379 mm rain) than during the drier 10-month calibration period (175 mm rain). Average soil moisture in the top 60 cm of the soil profile was reasonably well captured for both species (daily Nash–Sutcliffe efficiency > 0.80 for validation). Among the three soil layers, the second layer (20–40 cm) showed better simulation performance during both periods and for both species. The model exhibited limitations with respect to simulating transpiration, particularly during the drier calibration period. The inclusion of a root distribution function in the model, along with the monitoring of soil moisture below the 60 cm soil depth in the field, could improve the accuracy of model simulations in such water-limited ecosystems.

Are conductivity sensors useless for irrigators? Exploring measurement consistency around soil moisture thresholds relevant to different applications

AbstractThe cheap conductivity‐based sensors favoured by farmers for soil moisture monitoring remain largely ignored by researchers because of their low accuracy, which results in high uncertainty regarding their utility for irrigators. In this work, conductivity measurements were compared with dielectric permittivity measurements in moisture ranges relevant to different applications. The results showed that the permittivity measurements captured moisture variability well (R2 > 0.8) throughout the full range tested (0%–35%), which is consistent with the literature. Conductivity measurements consistently distinguished dry from wet conditions (p < 0.0001) and reflected moisture variability in lower ranges (R2 > 0.5) but not in higher ranges (>20%). This is problematic because important monitoring thresholds such as field capacity and saturation are in the upper moisture ranges. Conductivity measurements were found to lack any meaningful utility in most applications except those relevant to distinguishing dry from wet conditions and indicating lower‐range moisture patterns, such as monitoring in arid environments. This gives some merit to conductivity sensors considering their very low cost if corrosion is minimised. The described evaluation approach is suggested as an example for developers, labs and extension services to better communicate potential sensor utilities and restrictions to practitioners to improve their accessibility to decision support technologies.

Soil moisture retrieval over croplands using novel dual-polarization SAR vegetation index

Synthetic Aperture Radar (SAR) data, known for its high spatial resolution and all-weather observation capabilities, holds immense promise in soil moisture monitoring. The Water Cloud Model (WCM) is widely applied in soil moisture inversion using SAR data. However, the optical vegetation indices employed in traditional WCM cannot synchronize with SAR data, and the polarimetric scattering information contained in current SAR vegetation indices is incomplete, consequently compromising the accuracy of SAR-based soil moisture retrieval. Therefore, this study proposes a method for soil moisture retrieval over cropland using a novel dual-polarization SAR vegetation index. The method initially combines SAR data covariance elements with backscatter information to establish a polarization scattering contrast parameter (mcp). Then, based on mcp and incorporating the degree of polarization, a polarization scattering correlation contrast parameter (Rcp) is defined. Rcp integrates the distinctive features of scattering differences and polarization states. Building on Rcp, a novel dual-polarization SAR vegetation index (DRVIs) is introduced. Ultimately, DRVIs are utilized in the WCM to achieve surface soil moisture retrieval in cropland. This research conducts experiments in four crop cover areas of the Carman test site in Canada, namely soybean, wheat, canola, and corn. Under VV and VH polarizations, the overall correlation coefficients between measured in-situ data and SSM estimates reach 0.89 and 0.84, respectively. Compared to SSM estimates based on NDVI and LAI products, SSM estimates based on DRVIs exhibit a notable improvement in accuracy, with enhancements of approximately 7.2% and 12.7%, respectively. This novel DRVIs is poised to expand the utilization of SAR data in monitoring vegetation growth and soil moisture retrieval.

An IoT-based Intelligent Irrigation and Weather Forecasting System

Introduction:: The most crucial ingredient in agriculture is water. The amount of water that plants require must be provided to them. However, growers alternate between giving their plants more water than they truly need and giving them less and partly because they become overwatered due to meteorological circumstances like unexpected rainfall. Methods:: We employ an IoT-based intelligent irrigation system to get around this problem. It includes a centrifugal pump, a motor driver board, and a soil moisture sensor with YL69 probes. When the soil moisture level drops, the pump automatically delivers water to the plants with minimal human involvement. The electrical conductivity theory is how the sensor for soil moisture functions. A DHT11 sensor and a barometer, which provide information on the local temperature, humidity, and atmospheric pressure, are both parts of the weather monitoring system with the help of this, farmers can forecast the local weather and plan their irrigation accordingly. Results:: In this patent study, the thing speak API enables us to continually monitor information from a computer or mobile device, and the ESP8266 module links the complete system to the internet. Through this approach, water waste is reduced, and irrigation efficiency is increased while crop health and quality are preserved. Conclusion:: Overall, this research demonstrated how the Internet of Things-based intelligent irrigation systems may enhance agricultural water management. By combining soil moisture monitoring, weather monitoring, and autonomous management, we may develop irrigation techniques that are more precise, effective and patent leading to higher crop yields and sustainable agricultural practices.