Soil Sensors Research Articles

Soil-Specific Calibration Using Plate Compression Filling Technique and Monitoring Soil Biomass Degradation Based on Dielectric Properties

Accurate estimation of soil water content (SWC) is crucial for effective irrigation management and maximizing crop yields. Although dielectric property-based SWC measurements are widely used, their accuracy is still affected by soil variability, soil–sensor contact, and other factors, making the development of convenient and accurate soil-specific calibration methods a major challenge. This study aims to propose a plate compression filling technique for soil-specific calibrations and to monitor the extent of soil biomass degradation using dielectric properties. Before and after biodegradation, dielectric measurements of quartz sand and silt loam were made at seven different water contents with three different filling techniques. A third-order polynomial fitting equation explaining the dependence of the dielectric constant on the volumetric water content was obtained using the least-squares method. The suggested plate compression filling method has a maximum mean bias error (MBE) of less than 0.5%, according to experimental results. Depending on the water content, silt loam’s dielectric characteristics change significantly before and after biodegradation. The best water content, measured in gravimetric units, to encourage the decomposition of biomass was discovered to be 24%. It has been demonstrated that the plate compression filling method serves as a simple, convenient, and accurate alternative to the uniform compaction method, while the dielectric method is a reliable indicator for evaluating biomass degradation. This exploration provides valuable insights into the complex relationship between SWC, biomass degradation, and soil dielectric properties.

Underground Ink: Printed Electronics Enabling Electrochemical Sensing in Soil.

Improving agricultural production relies on the decisions and actions of farmers and land managers, highlighting the importance of efficient soil monitoring techniques for better resource management and reduced environmental impacts. Despite considerable advancements in soil sensors, their traditional bulky counterparts cause difficulty in widespread adoption and large-scale deployment. Printed electronics emerge as a promising technology, offering flexibility in device design, cost-effectiveness for mass production, and a compact footprint suitable for versatile deployment platforms. This review overviews how printed sensors are used in monitoring soil parameters through electrochemical sensing mechanisms, enabling direct measurement of nutrients, moisture content, pH value, and others. Notably, printed sensors address scalability and cost concerns in fabrication, making them suitable for deployment across large crop fields. Additionally, seamlessly integrating printed sensors with printed antenna units or traditional integrated circuits can facilitate comprehensive functionality for real-time data collection and communication. This real-time information empowers informed decision-making, optimizes resource management, and enhances crop yield. This review aims to provide a comprehensive overview of recent work related to printed electrochemical soil sensors, ultimately providing insight into future research directions that can enable widespread adoption of precision agriculture technologies.

AHFO-based soil water content sensing technology considering soil–sensor thermal contact resistance

The actively heated fiber-optic (AHFO) technology has become emerged as a research focus due to its advantages of distributed, real-time measurement and good durability. These attributes have led to the gradual application of AHFO technology to the water content measurement of in situ soil. However, all existing in situ applications of AHFO technology fail to consider the effect of soil–sensor contact quality on water content measurements, limiting potential for the wider application of AHFO technology. To address this issue, the authors propose a method for determining the soil–sensor thermal contact resistance based on the principle of an infinite cylindrical heat source. This is then used to establish an AHFO water content measurement technology that considers the thermal contact resistance. The reliability and validity of the new measurement technology are explored through a laboratory test and a field case study, and the spatial-temporal evolution of the soil water content in the case is revealed. The results demonstrate that method for determining the soil–sensor thermal contact resistance is highly effective and applicable to all types of soils. This method requires only the moisture content, dry density, and thermal response of the in situ soil to be obtained. In the field case, the measurement error of soil water content between the AHFO method, which takes into account the thermal contact resistance, and the neutron scattering method is only 0.011. The water content of in situ soil exhibits a seasonal variation, with an increase in spring and autumn and a decrease in summer and winter. Furthermore, the response of shallow soils to precipitation and evaporation is significant. These findings contribute to the enhancement of the accuracy of the AHFO technology in the measurement of the water content of in situ soils, thereby facilitating the dissemination and utilization of this technology.

Smart Agrobot: An Integrated Approach to Seed Sowing, Pesticide Application

Abstract: A state-of-the-art multifunctional autonomous agricultural robotic vehicle intended to transform farming methods is presented by the suggested system. This robotic vehicle is mostly operated via Bluetooth, but it also has sophisticated navigation features like GPS and object detection sensors, which allow it to navigate precisely and cautiously over fields of crops. The autonomous irrigation system with soil sensors and intelligent seeding mechanisms for different crops are just two of the robotic vehicle's many features that make it stand out. Farmers have freedom thanks to the Bluetooth control capability, which enables remote operation and monitoring. Safety features protect workers' health and prevent equipment damage, such as emergency stop buttons and collision avoidance procedures. This robotic system also performs exceptionally well in drip irrigation management in places that receive rain, optimizing fertilizing depending on soil conditions, and controlling weeds using computer vision. The suggested method, which reduces human intervention and increases total production, is a comprehensive and intelligent approach to automated and sustainable agriculture with an emphasis on energy conservation, data logging, and analytics.

Strategy for Optimizing Orange Fruit Quality Using Technology: Case Study of Sidikalang Orange Farm

Oranges are one of the most consumed fruits worldwide, and Indonesia is one of the largest orange producers globally. However, the quality of oranges in Indonesia varies widely and is heavily influenced by seasonal and weather conditions. This study aims to optimize the quality of oranges using various modern technologies in an orange orchard in Sidikalang, Dairi Regency, North Sumatra. The research method used is a case study with a qualitative approach through observation and interviews. The results indicate that the use of technologies such as precision irrigation systems, soil sensors, and drones can enhance the size, color, texture, sugar content, and nutritional value of oranges. Additionally, these technologies improve production efficiency by reducing water and fertilizer use and lowering labor costs. However, the adoption of these technologies faces challenges such as high initial costs and the need for farmer training. Therefore, support from the government and financial institutions is essential to overcome these challenges and improve the quality of oranges in Indonesia.

Controller Based Smart Blind Stick for People with Vision Loss

One of the biggest problems faced by the visually impaired is navigating from place to place, be it indoors or outdoors. The main aim of this work is to help the visually impaired person to remotely locate his/her stick using a RF remote. This system incorporates with buzzer and attached with multiple sensors to provide high security to the visually impaired people while walking. Now a days safety is main important cause to the peoples while walking or driving and many more places. The system provides the high security and show way to walk, this system can monitor the blind person position using mobile, an emergency alert message will be sent along with the exact location. The system has obstacle sensor, soil sensor and stair detection sensors, so that it can detect obstacles/ steps automatically and gives alert. By using soil moisture detector used to detect the moisture in the soil and gives alert accordingly. This system can be very useful to peoples to show correct path while walking on the floor or steps and many more places. The system can be interconnected with the microcontroller and alert the respective persons when any emergency occurs. This tracking system is composed of a GPS receiver, Microcontroller and a GSM Modem. The Microcontroller processes this information and this processed information is sent to the respective numbers. Keywords—ultrasonic sensor, raspberry pi, panic button, GPS & GSM

Nanotechnology-based Sensors for Real-time Monitoring and Assessment of Soil Health and Quality: A Review

Soil health and quality are critical factors in maintaining sustainable agriculture, ecosystem stability, and global food security. Conventional methods for assessing soil properties are often time-consuming, labor-intensive, and lack real-time monitoring capabilities. Nanotechnology has emerged as a promising approach to develop advanced sensors for rapid, in-situ, and continuous monitoring of soil health parameters. This comprehensive review discusses the recent advancements in nanotechnology-based sensors for soil health assessment, their working principles, applications, challenges, and future prospects. We highlight the potential of various nanomaterials, such as carbon nanotubes, graphene, metal oxide nanoparticles, and quantum dots, in fabricating highly sensitive, selective, and robust soil sensors. The integration of these nanosensors with wireless communication technologies and data analytics enables real-time monitoring and precision agriculture practices. Furthermore, we discuss the environmental and ecological implications of deploying nanosensors in soil and the need for standardized protocols and regulations. This review provides valuable insights into the current state-of-the-art and future directions of nanotechnology-based sensors for soil health monitoring, promoting sustainable agriculture and environmental management.

Visualized Experimental Study of Soil Temperature Distribution around Submarine Buried Offshore Pipeline Based on Transparent Soil

The temperature distribution around the offshore burial pipeline is an important factor affecting its safety design and economic operation. The traditional test method cannot obtain the continuous temperature distribution of soil owing to the constraints of placing measurement sensors in soil. The transparent soil model test is an alternative method to realize the visualization research of soil temperature. In this paper, a relationship between the temperature of transparent soil and pixel intensity was first established. Then, the transparent soil test and numerical simulation, considering the natural convection, were carried out to study the temperature distribution around the submarine pipeline during start-up and stable operation. The influence of buried depth and pipeline diameter was analyzed. The results suggest that the continuous temperature distribution can be obtained visually by using a transparent soil test, and the observed heating zone of influence extended to a radial distance of 2.6 pipe diameters. The numerical analysis results show that the influence zone of the temperature of pipeline is a distance of four pipeline diameters at a temperature difference of 45 °C. The buried depth and pipeline diameter have little influence on the influence zone. In addition, the contour curves of soil temperature around the pipeline with different diameter are similar in shape. With the decrease in the buried depth of pipeline, the temperature gradient of soil around the pipeline decreases, which is caused by the natural convection.

How and why we built our Smart Farm

The Smart Farm of Bioeconomy Campus project (2021–2023) developed a unique hub of Smart Farming technology. The resulting Smart Farm aims to accelerate the adoption of smart technologies in farms according to the United Nations Sustainable Development Goals (SDGs). Therefore, at the Smart Farm, near-market technologies and services are tested, developed, and demonstrated. The aim is to remove barriers to their adoption and accelerate innovation in the sector, significantly increasing the benefits for farmers and the related agricultural industry. The foundation of the Smart Farm is based on processing various types of data. In the project, data was intensively collected from 16 hectares of test plots where barley was cultivated. Regular measurements were taken from the soil, crops, and from machinery and tractors equipped with ISOBUS technology. Measurements included the use of wireless soil sensors (20 units), drone imaging (RGB, multispectral, and thermal cameras), satellite images, and tractor telematics data. Additionally, the usability of the 5G signal in machine guidance was measured. Based on the collected data, precision farming was planned and implemented. Automated field navigation with headland automation was compared to traditional manual driving methods. Using GIS, maps such as profitability and energy consumption maps were generated from the data. The project developed a Farmer's Data Repository, through which a farmer can license their data to the desired destination via a data delivery service. The project also demonstrated the operation of such a system, compliant with EU data regulations, in collaboration with partner companies. A comprehensive project, the Finnish Future Farm (2023–2026), has begun based on the foundation of the Smart Farm, involving companies, educational and research organizations, farmers, and stakeholders. The project will build a digital twin of the physical Smart Farm, both of which will be utilized in R&D, experimentation, and education.

Enhancing melon yield through a low-cost drip irrigation control system with time and soil sensor

Enhancing melon yield through a low-cost drip irrigation control system with time and soil sensor

The spatiotemporal distribution of river bank erosion events and their drivers in seasonally frozen regions

River bank erosion supplies sediments to river systems, sustaining many river functions. To properly understand and ultimately model river bank erosion, we have to know the temporal and spatial distributions at which it occurs. This is especially challenging in cold-climate regions where a large variety of processes occur that contribute to river bank erosion. We therefore obtained a one-year dataset, using buried soil sensors, on bank erosion and its forcing parameters with a high temporal resolution to answer the question: what are the temporal and spatial distributions of river bank erosion in cold climate regions and what are the forcing conditions causing these distributions? We measured soil movements at multiple river bank sites throughout Finland and compared the movement times with soil moisture, soil and air temperature and discharge information. This analysis showed that there is no clear temporal distribution of bank movement in the Southernmost investigated site, while in the more Northern field sites soil movement was most frequent around the freezing and thawing periods. At one field site an additional period with an increased frequency of soil movement events is likely caused by reindeer during summer months. This research used a new type of dataset of soil temperature, moisture and movement. This unique dataset allowed us to identify individual soil movement events and helps to better understand river bank erosion and by extension fluvial systems in cold-climates.

Effect of training sample size, sampling design and prediction model on soil mapping with proximal sensing data for precision liming

Site-specific estimation of lime requirement requires high-resolution maps of soil organic carbon (SOC), clay and pH. These maps can be generated with digital soil mapping models fitted on covariates observed by proximal soil sensors. However, the quality of the derived maps depends on the applied methodology. We assessed the effects of (i) training sample size (5–100); (ii) sampling design (simple random sampling (SRS), conditioned Latin hypercube sampling (cLHS) and k-means sampling (KM)); and (iii) prediction model (multiple linear regression (MLR) and random forest (RF)) on the prediction performance for the above mentioned three soil properties. The case study is based on conditional geostatistical simulations using 250 soil samples from a 51 ha field in Eastern Germany. Lin’s concordance correlation coefficient (CCC) and root-mean-square error (RMSE) were used to evaluate model performances. Results show that with increasing training sample sizes, relative improvements of RMSE and CCC decreased exponentially. We found the lowest median RMSE values with 100 training observations i.e., 1.73%, 0.21% and 0.3 for clay, SOC and pH, respectively. However, already with a sample size of 10, models of moderate quality (CCC > 0.65) were obtained for all three soil properties. cLHS and KM performed significantly better than SRS. MLR showed lower median RMSE values than RF for SOC and pH for smaller sample sizes, but RF outperformed MLR if at least 25–30 or 75–100 soil samples were used for SOC or pH, respectively. For clay, the median RMSE was lower with RF, regardless of sample size.

A Machine Learning-Based High-Resolution Soil Moisture Mapping and Spatial–Temporal Analysis: The mlhrsm Package

Soil moisture is a key environmental variable. There is a lack of software to facilitate non-specialists in estimating and analyzing soil moisture at the field scale. This study presents a new open-sourced R package mlhrsm, which can be used to generate Machine Learning-based high-resolution (30 to 500 m, daily to monthly) soil moisture maps and uncertainty estimates at selected sites across the contiguous USA at 0–5 cm and 0–1 m. The model is based on the quantile random forest algorithm, integrating in situ soil sensors, satellite-derived land surface parameters (vegetation, terrain, and soil), and satellite-based models of surface and rootzone soil moisture. It also provides functions for spatial and temporal analysis of the produced soil moisture maps. A case study is provided to demonstrate the functionality to generate 30 m daily to weekly soil moisture maps across a 70-ha crop field, followed by a spatial–temporal analysis.

Characterization of an In-Situ Soil Organic Carbon (SOC) via a Smart-Electrochemical Sensing Approach.

Soil is a vital component of the ecosystem that drives the holistic homeostasis of the environment. Directly, soil quality and health by means of sufficient levels of soil nutrients are required for sustainable agricultural practices for ideal crop yield. Among these groups of nutrients, soil carbon is a factor which has a dominating effect on greenhouse carbon phenomena and thereby the climate change rate and its influence on the planet. It influences the fertility of soil and other conditions like enriched nutrient cycling and water retention that forms the basis for modern 'regenerative agriculture'. Implementation of soil sensors would be fundamentally beneficial to characterize the soil parameters in a local as well as global environmental impact standpoint, and electrochemistry as a transduction mode is very apt due to its feasibility and ease of applicability. Organic Matter present in soil (SOM) changes the electroanalytical behavior of moieties present that are carbon-derived. Hence, an electrochemical-based 'bottom-up' approach is evaluated in this study to track soil organic carbon (SOC). As part of this setup, soil as a solid-phase electrolyte as in a standard electrochemical cell and electrode probes functionalized with correlated ionic species on top of the metalized electrodes are utilized. The surficial interface is biased using a square pulsed charge, thereby studying the effect of the polar current as a function of the SOC profile. The sensor formulation composite used is such that materials have higher capacity to interact with organic carbon pools in soil. The proposed sensor platform is then compared against the standard combustion method for SOC analysis and its merit is evaluated as a potential in situ, on-demand electrochemical soil analysis platform.

Advances in Precision Agriculture Technologies for Sustainable Crop Production

Precision agriculture (PA) encompasses a set of technologies that allow farmers to collect data on variations within fields and manage crops at a more granular level. PA technologies include sensors, satellite imagery, information management tools and variable rate application systems. Together, these enable data-driven, site-specific decision making to optimize productivity while minimizing environmental impacts. This review examines recent advances in PA technologies and their role in supporting sustainable crop production. Key developments reviewed include remote sensing platforms and techniques, proximal soil sensors, variable rate systems, robotics and automation, and decision support tools powered by artificial intelligence and machine learning. Challenges and future directions are also discussed. Widespread adoption of PA technologies has the potential to increase yields and profitability for farmers while reducing use of inputs such as water, nutrients, and pesticides. However, barriers to adoption exist, including costs, technical complexity, and integration challenges. Continued innovation and knowledge transfer will be critical to unlocking the full promise of PA for sustainable agriculture.

COMPATIBILITY ANALYSIS OF THE SOIL CALCIUM CONTENT BY MULTIFREQUENCY EC SENSORS

Nowadays in precision agriculture, the on-the-go measurement of soil nutrients is an important research topic in the sustainable nutrient management practices. Determining the nutrient content of soils and the judicious and site-specific replacement of missing mineral compounds of soil has a major impact on production costs in terms of current fertilizer prices. Soil sensors currently on the market can only determine total soil salinity. Therefore, selective soil salinity testing is only possible using laboratory methods. However, these methods are rather expensive, slow, and cumbersome. Growers often need faster and cheaper soil sampling process and immediate results. We believe that by developing measurement models of soil sensors, the data delivery process could be significantly shortened, so that measurement results could be processed and used even in real time. In this paper, we prove that electrical conductivity measurements can be a suitable tool for the determination of the selective salinity of soil. In our experiments, the calcium cation content of soil was measured by conductometry in laboratory conditions. In our model, we investigated the effect of a reasonable and well-considered variation of the measuring current frequency on the measurement output variable, i.e. the electrical conductivity (often abbreviated as EC) value. Our experiments have shown that with multi-frequency, solutions containing predetermined concentrations of Ca2+ ions, the EC obtained as an output parameter follows different functions.

Internet of things sensors and support vector machine integrated intelligent irrigation system for agriculture industry

Because there is more demand for freshwater around the world and the world’s population is growing at the same time, there is a severe lack of freshwater resources in the central part of the planet. The world’s current population of 7.2 billion people is expected to grow to over 9 billion by the year 2050. The vast majority of freshwater is used for things like cooking, cleaning, and farming. Most industrialised countries are in desperate need of smart irrigation systems, which are now a must-have because of how quickly technology is improving. In article presents IoT based Sensor integrated intelligent irrigation system for agriculture industry. IoT based humidity and soil sensors are used to collect soil related data. This data is stored in a centralized cloud. Features are selected by CFS algorithm. This will help in discarding irrelevant data. Clustering of data is performed by K means algorithm. This will help in keeping similar data together. Then classification model is build using the SVM, Random Forest and Naïve Bayes algorithm. Model is trained, validated and tested using the acquired data. Historical soil and humidity related data is also used in training the model. K-means SVM hybrid classifier is achieving better results for classification, prediction of water demand and saving fresh water by intelligent irrigation. K-means SVM hybrid classifier has achieved accuracy rate of 98.5 percent. Specificity, recall and precision of K-means SVM hybrid classifier is also higher than random forest and naïve bayes classifier.

The role of big data in advancing precision agriculture and ensuring food security

The advent of big data analytics is revolutionizing the agro-industrial complex, particularly in the realm of precision agriculture. This article explores how big data technologies are advancing precision farming practices and contributing to global food security. By integrating data from diverse sources such as satellite imagery, weather stations, soil sensors, and farm management systems, big data analytics enables farmers to make informed decisions that optimize resource use, enhance crop yields, and reduce environmental impact. This study examines key applications of big data in precision agriculture, including predictive modeling for crop health, real-time monitoring of soil conditions, and data-driven pest management strategies. Furthermore, it discusses the socio-economic benefits of big data adoption, such as increased efficiency, cost savings, and improved resilience against climate variability. Case studies from various regions highlight successful implementations and the resulting improvements in agricultural productivity and sustainability. The findings underscore the critical role of big data in transforming agricultural practices and ensuring food security in the face of growing global challenges. By leveraging the power of big data, the agro-industrial complex can achieve a more sustainable and secure future, addressing the needs of a rapidly expanding population.

Concept for long-term geo-monitoring of the post-mining environment using the example of the Prosper-Haniel mine

The operation of a mining facility provides a wealth of data, starting from mining licenses, documentation of extracted deposits, tunnel reinforcement methods, to documentation regarding the termination of mining operations, which impacts the natural environment at the local, regional, and supra-regional levels. The results of projects conducted by the Research Center of Post-Mining at the Technical University of Georg Agricola in Bochum present the possibilities of integrating environmental geo-monitoring methods to understand the processes occurring both during and after mining operations. Among the research methods used, spatiotemporal multispectral analyses of satellite imagery and images from drone flights stand out, and these will be presented in this paper. Additionally, in-situ measurements using soil sensors, weather stations, the application of mobile GIS, and three-dimensional modelling of geological structures should be noted. A key aspect of mining process research is the implementation and integration of all available geospatial data, allowing the consideration of post-mining processes as a cycle of interconnected, independent values that, through data analysis and validation, enable a comprehensive understanding.

Precision crop prediction using IoT-enabled soil sensors and machine learning

Precision crop prediction using IoT-enabled soil sensors and machine learning