Proximity Sensors Research Articles

Wrap‐Around String‐Like ToF and Self‐Capacitance Combined Sensor for Robots

Collaborative robots are becoming popular because they can operate without safety fences and can work in close proximity to humans. Proximity and tactile sensors on the robot's surface are important safety measures for existing robots. In this paper, we propose a string‐like ToF and self‐capacitance combined sensor that can be easily wrapped around various types of existing robots. The sensor can be freely connected to other sensors, and the length of the connected sensors attached can be adjusted depending on the shape of the robot. Prototype sensors were fabricated, and four connected sensors were wrapped around the robotic arm. Sensors on the robot's surface can detect objects within the proximity range and determine contact. In addition, a robotic arm with sensors can be controlled in real‐time using measured data from the sensors. It was confirmed that the proposed sensor can be used for proximity and tactile sensing on existing robots. © 2024 Institute of Electrical Engineers of Japan and Wiley Periodicals LLC.

Low-Cost IoT-Based Sensors Dashboard for Monitoring the State of Health of Mobile Harbor Cranes: Hardware and Soft-ware Description

A cost-effective IoT-based real-time data acquisition and analysis hardware system was developed to enhance the performance of the mobile harbor cranes using a combination of a cost-effective quality control monitoring sensor dashboard (proximity sensors, angle position sensor, weight sensor, vibration sensor, and wind sensor), embedded microcontroller (Arduino), and embedded computer (Raspberry Pi). Hardware was operated using a specially developed novel Quality Control and Data Acquisition Multiprocessing software (QC-DAS). The QC-DAS can automatically collect and save real-time data of the sensors in a large-capacity SD card, monitor the state of health of the hardware, and transmit the real-time data of the sensors and the working state of the crane to an IoT server. The novelty of the QC-DAS design is that each function is encapsulated in a predefined module that is "immersed" in a message transmission medium. Modules interact by sending and receiving various signals through this medium. Modularity makes system design simpler, faster, and flexible. Thanks to modularity, users may incorporate their data processing modules when new sensors are added to match the system's needs. Thanks to modularity the DC-DAS can operate quality control hardware for any mobile cranes. There are several constraints in the quality control data acquisition system used by the Damietta Port Authority in Damietta, Egypt, SESCO TRANS company which cause the loading and unloading process to be slowed down. As a result, the SESCO TRANS company upgraded its quality control data acquisition system using the QC-DAS. The hardware was deployed for six months, during which the collected data was used to verify the crane's performance. The vibration produced by the slewing of the crane was monitored and compared with the bearing fault frequency limits, during the operation the wind speed was monitored and compared with the critical wind speed to stop the crane operation automatically, and the payloads data of the six months was collected and was used to calculate the working efficiency of the load and unload process of the crane. The results demonstrated that while maintenance costs were decreased, the crane load/unload procedure was improved. The SESCO TRANS company crane operators approved the developed approach and appreciated the achieved results.

Development of a Multifingered Hand With Proximity Sensors for Aerial Manipulation

Development of a Multifingered Hand With Proximity Sensors for Aerial Manipulation

Control and monitor bottle filling, capping and labelling machine using a programmable logic controller and human machine interface

Bottle filling, closing, and labellingmachine control and monitoring tool using Programmable Logic Controller (PLC) and Human-Machine Interface (HMI). This tool is designed to optimize the production process in the bottled beverage industry,with dimensions of 200 cm × 70 cm × 100 cm. The conveyor transports bottles through the filling, capping, and labeling units. In each unit, sensors are installed to detect the presence of bottles. Bottle liquid volume is determined through time settings inputted through the HMI,and the filling is performed by a water pump. After filling, bottles are capped and tightened mechanically using an air impact system. Labeling is handled by two DC motors which unroll the label and rotate the bottle, ensuringthe label adheres evenlyto all sides of the bottle. The number of bottles produced is calculated using a capacitive proximity sensor and displayed on the HMI. Testing results show that the system effectivelycontrols and monitors the bottle filling, capping, and labeling process viaPLC and HMI. Based on the test results on the bottle filling unit, the system can fill water into the bottle with a maximum percentage error of 0.5% on a 1000 ml volume bottle. In the capping unit, the success rate achieved is 70%, while the success rate in the bottle labeling unit is 80%.

One-Dimensional Motion Representation for Standing/Sitting and Their Transitions.

In everyday life, people often stand up and sit down. Unlike young, able-bodied individuals, older adults and those with disabilities usually stand up or sit down slowly, often pausing during the transition. It is crucial to design interfaces that accommodate these movements. Additionally, in public settings, protecting personal information is essential. Addressing these considerations, this paper presents a distance-based representation scheme for the motions of standing up and sitting down. This proposed scheme identifies both standing and sitting positions, as well as the transition process between these two states. Our scheme is based solely on the variations in distance between a sensor and the surfaces of the human body during these movements. Specifically, the proposed solution relies on distance as input, allowing for the use of a proximity sensor without the need for cameras or additional wearable sensor attachments. A single microcontroller is adequate for this purpose. Our contribution highlights that using a proximity sensor broadens the applicability of the approach while ensuring that personal information remains secure. Additionally, the scheme alleviates users' mental burden, particularly regarding privacy concerns. Extensive experiments were performed on 58 subjects, including 19 people over the age of 70, to verify the effectiveness of the proposed solution, and the results are described in detail.

Otomatisasi Perhitungan OEE pada Mesin Filling ILAPAK 50g Menggunakan PLC dan HMI

This study addressed the limitations in calculating Overall Equipment Effectiveness (OEE) manually, which frequently led to inaccuracies and inefficiencies in industrial processes. The purpose of this research was to design an automated OEE calculation system for the ILAPAK 50g filling machine to improve data accuracy and reduce dependency on manual input. A Design Science Research (DSR) methodology was employed, involving stages of system analysis, design, implementation, and evaluation. The system utilized key components, including an Omron CP2E PLC, proximity sensors, relays, and a Human-Machine Interface (HMI) for real-time data acquisition. The findings indicated that the automated system significantly improved data accuracy, achieving reliable, continuous OEE calculations across different machine shifts. The system successfully replaced manual logbook methods, reducing human estimation errors, and displayed OEE data locally on the HMI, making it accessible for operational decisions. Furthermore, the implemented automation reduced overall downtime, enhanced machine availability, and minimized errors associated with manual record-keeping. This research has practical implications for industries aiming to optimize production efficiency. The automated OEE system demonstrated potential for scalability to other machines in manufacturing settings, highlighting its adaptability and utility in modern industrial environments. Future research could explore integration with cloud-based databases to enhance data storage and accessibility, providing a foundation for advanced predictive maintenance and performance monitoring across multiple machines.

Multi-Sensor Soil Probe and Machine Learning Modeling for Predicting Soil Properties.

We present a data-driven, in situ proximal multi-sensor digital soil mapping approach to develop digital twins for multiple agricultural fields. A novel Digital Soil CoreTM (DSC) Probe was engineered that contains seven sensors, each of a distinct modality, including sleeve friction, tip force, dielectric permittivity, electrical resistivity, soil imagery, acoustics, and visible and near-infrared spectroscopy. The DSC System integrates the DSC Probe, DSC software (v2023.10), and deployment equipment components to sense soil characteristics at a high vertical spatial resolution (mm scale) along in situ soil profiles up to a depth of 120 cm in about 60 s. The DSC Probe in situ proximal data are harmonized into a data cube providing vertical high-density knowledge associated with physical-chemical-biological soil conditions. In contrast, conventional ex situ soil samples derived from soil cores, soil pits, or surface samples analyzed using laboratory and other methods are bound by a substantially coarser spatial resolution and multiple compounding errors. Our objective was to investigate the effects of the mismatched scale between high-resolution in situ proximal sensor data and coarser-resolution ex situ soil laboratory measurements to develop soil prediction models. Our study was conducted in central California soil in almond orchards. We collected DSC sensor data and spatially co-located soil cores that were sliced into narrow layers for laboratory-based soil measurements. Partial Least Squares Regression (PLSR) cross-validation was used to compare the results of testing four data integration methods. Method A reduced the high-resolution sensor data to discrete values paired with layer-based soil laboratory measurements. Method B used stochastic distributions of sensor data paired with layer-based soil laboratory measurements. Method C allocated the same soil analytical data to each one of the high-resolution multi-sensor data within a soil layer. Method D linked the high-density multi-sensor soil data directly to crop responses (crop performance and behavior metrics), bypassing costly laboratory soil analysis. Overall, the soil models derived from Method C outperformed Methods A and B. Soil predictions derived using Method D were the most cost-effective for directly assessing soil-crop relationships, making this method well suited for industrial-scale precision agriculture applications.

One-Second Touchscreen Disinfection with Internal UVC-LEDs

The transmission of pathogens via touchscreens is a concern both in medical environments regarding nosocomial infections and in public spaces. This study presents the development of a self-disinfecting touchscreen system and its implementation into an existing medical device. The disinfection was achieved by irradiating UVC light laterally into the quartz front glass of the touchscreen. The system also included a two-stage safety shutdown feature to protect users from UVC exposure. To detect the presence of the user’s hand, a proximity sensor behind the front glass of the touch screen was installed in combination with touch input registration. A rapid disinfection of staphylococci was achieved without compromising usability and user safety. The prototype demonstrated a bacterial reduction of 99.96% or 3.4 (±0.36) log levels in less than 1 s. The results suggest that the technology has the potential to make a significant contribution to reducing the transmission of infections in healthcare settings and beyond.

Identification of Technical Requirements for the Initial Design of an Autonomous Fruit-Harvesting Robot

Automation in agriculture is revolutionizing the management of field tasks. This paper focuses on identifying the essential needs and preliminary technical requirements for designing an autonomous robot specifically for fruit harvesting. The study examines the integration of advanced machine vision technologies and neural networks for the accurate detection of ripe fruits and obstacles, optimizing harvesting efficiency. Key features such as proximity and touch sensors are also evaluated to ensure the safe handling of fruits, minimizing damage during harvesting. The technical specifications, including traction, speed, and the robot’s ability to operate on uneven terrain, are analyzed to ensure adaptability across various crops and ground conditions. Perception systems, incorporating cameras and sensors, are crucial for crop inspection and route planning. The main findings of this review establish a set of technical requirements that serve as a foundation for developing efficient and economically viable fruit-harvesting robots. These systems offer significant potential to transform agriculture by addressing labor shortages and improving harvesting precision. However, challenges remain, particularly in adapting to diverse crops and environments, emphasizing the need for further research and development.

Adaptive Movement Analysis And Enhanced Self-Monitoring Hypercube For Parkinson’s Disease

Parkinson’s Disease (PD) is a chronic neurodegenerative disorder characterized by motor impairments, including tremors, rigidity, bradykinesia, and postural instability, which progressively diminish patients’ ability to perform daily activities independently. Current rehabilitation practices, primarily focused on physical therapy, medication, and traditional support devices, provide essential symptom management but often lack customization to individual patient needs, leading to inconsistent adherence and limited long-term benefits. This paper introduces the Adaptive Movement Analysis and Enhanced Self-Monitoring Hypercube, a novel rehabilitation framework aimed at improving PD management through a comprehensive, adaptive approach.The system leverages cutting-edge sensors, including the MPU6050 Inertial Measurement Unit (IMU) and infrared proximity sensors, integrated with a Teensy microcontroller to deliver real-time movement analysis and tailored exercise feedback. Through Bluetooth-enabled connectivity, patient data is transmitted to a digital platform where it can be analyzed and adjusted based on performance metrics. Additionally, gamification techniques, task-specific training modules, and customizable feedback mechanisms are embedded to increase patient engagement and adherence to exercise routines. This adaptive solution provides a structured, motivating environment that addresses the fluctuating capabilities of PD patients, promoting functional recovery and enhanced self-monitoring.

Simulation of Launch Pad Atomization for Military Weapon System

This study aims to optimize three critical parameters (inclination angle, hook lifting, and sparking) for a missile launching system by combining precise mathematical methods, IoT concepts, and advanced sensor technologies. The system uses an Inertial Navigation System (INS) in conjunction with gyro and proximity sensors to ensure accurate target interception by carefully determining the optimal launch angle. The Internet of Things concept is integrated effortlessly, with a clever coordinating WiFi networking, weight estimation, and sensor data processing for real-time cloud data flow. Security protocols protect the integrity of the data, and a reliable power supply system guarantees reliable operation. The project incorporates both Hot Start and Cold Start functionalities, enabling dependable launches and prompt adaptations to dynamic changes. Flexible igniting mechanisms such as Chemical, Electrical, and Electro-Chemical systems are available, and reliable and secure missile launches are prioritized with safety features like Spark Damping Inner Tube Coating and Moisture-Resistant igniting. All things considered, this project prioritizes security, dependability, and adaptability while addressing critical elements required for a sophisticated and efficient missile launching system.

IoT- enabled crop waste mulching machine for sustainable farming: perspective of circular economy

Abstract A large amount of crop waste is generated after crop harvesting. A substantial quantity of waste on the farm is burnt to clear the field, contributing to environmental harm and global warming. The study aims to develop a technological solution for crop waste management (CWM) by designing and developing an IoT-enabled crop waste mulching machine. This initiative addresses the environmental and health issues caused by burning crop waste on farms. The research involves designing and developing an IoT-enabled mulching machine that can be attached to the back side of a tractor. The machine consists of power-transmitting elements, supports, and mechanisms to distribute mulching material evenly onto the plant bed. It incorporates advanced IoT load cell sensors, soil moisture sensors, and proximity sensors to optimize the mulching process based on soil moisture content and to prevent machinery blockages. The IoT-enabled mulching machine effectively manages crop waste by utilizing it in mulch, thus preventing the harmful practice of burning waste. This machine ensures a more efficient, safer, and environmentally friendly approach to mulching, offering significant improvements over traditional manual mulching methods.
The novelty of this research lies in integrating IoT technologies within mulching machine. The mulching machine uses sensors to automate and optimize the mulching process, representing a significant technological advancement over conventional methods. The study offers practical benefits by reducing labour requirements, minimizing the risk of injuries, and ensuring a more uniform mulch application. This technology facilitates more sustainable farming practices, aligning with global sustainability goals. The study can potentially revolutionize the agricultural equipment manufacturing industry (AEMI) by shifting the focus toward environmental sustainability. It promotes replacing plastic film mulching machines with more eco-friendly crop waste mulching solutions. This technology has the potential to reduce air pollution, carbon emissions, and mitigate climate. It also enhances soil health through better mulching practices.

Design System of Solar-Powered Safety Lamp Integrated Proximity Sensor as a Solution for Accident Prevention at Blind Spot Areas

The curved contour of the road creates blind spots which increase the risk of traffic accidents. Throughout 2019, there were 107,500 accident cases recorded in Indonesia. Therefore, efforts are needed to provide road infrastructure that supports driving safety, such as traffic signs to reduce the risk of accidents in blind spot areas. This research aims to design a smart safety light warning system that is integrated with solar-based street lighting. The design uses a design-based research approach referring to several established standards including Indonesia National Standard (SNI) 7931: 2008, SNI IEC 60529:2014, Regulation of the Minister of Transportation of the Republic of Indonesia Number PM 13 of 2014, Manual on Uniform Traffic Control Devices (MUTCD) issued by the Federal Highway Administration (FHWA) of the United States Department of Transportation (USDOT), and Article 115 of Minister of Transportation Republic of Indonesia Regulation PM 27 of 2018. This study used Design-Based Research (DBR) as a research methodology. The result of this study is a prototype design and development of smart safety light warning system which can be optimized. Validation of the design and circuit is conducted using prototyping modeling 1:20 from the original dimensions to measure the function and performance of the circuit. The prototype is capable of turning on warning lights and siren notifications when detecting objects with a maximum distance of 350 cm as measured by the HC – SR04 sensor. Charging the stored solar panels via the accumulator allows the technology to continue working for 45 hours without charging. For power efficiency purposes, the technology is equipped with an LDR (Light Dependent Resistor) sensor with a minimum limit of 341-lux which regulates the lighting. The development of these safety lights will increase driver awareness when passing through blind spot areas and have positive implications for reducing the number of accidents in Indonesia.

Comprehensive Analysis of Needle Bearing Failures in Fuel Transfer Pumps: Insights from Testing and Finite Element Analysis

<div>This study investigates the failure mechanisms of needle bearings within fuel transfer pump assemblies through a comprehensive approach combining endurance testing, detailed inspection, the Dykem blue method, proximity sensors, and finite element analysis (FEA). The findings reveal critical insights into the causes of failure, highlighting significant axial displacement, with a maximum of 0.37 mm measured by proximity sensors. The Dykem technique identified distinct wear patterns across various components, pinpointing areas of high stress and potential failure. Detailed bearing inspections uncovered trunnion damage and abrasive wear, corroborated by FEA, which quantified displacements of 0.144 mm in the x-direction, 0.030 mm in the y-direction, and 0.015 mm in the z-direction. The primary operational factors contributing to bearing failure were contamination and inadequate axial control. These insights are pivotal, as they align with and expand upon established literature on bearing failures, providing a deeper understanding of the interplay between mechanical wear and operational conditions. Despite the robustness of the methodology, challenges included ensuring the accuracy of axial displacement measurements and replicating real-world operational stresses in a controlled environment. The study proposes several recommendations to enhance axial support and optimize system design to mitigate the identified issues. The societal impact of this research is significant, offering potential improvements in machinery reliability, which can lead to enhanced industrial efficiency and safety standards. This work advances the current knowledge in the field and provides practical solutions for extending the lifespan and performance of critical mechanical components in fuel transfer systems.</div>

Otherness and Alienation in Jana Egle’s Fiction

The Latvian writer and poet Jana Egle devotes special attention in her texts to people who are perceived by society as “others”. Such individuals are frequently regarded as anomalous by those in their immediate vicinity, which often results in a sense of detachment and disconnection. In her short fiction, Egle examines a range of forms of otherness, including individuals from diverse national backgrounds, members of the LGBTQ+ community, and those facing social disadvantage. Adults are frequently influenced by long-standing stereotypes and traumatic experiences, whereas children tend to be more receptive to forming close relationships due to the absence of such biases. This article seeks to examine the manifestations of otherness in Egle’s works through the lens of the self-other binary and the insights gleaned from contemporary researchers on identity issues. Egle adroitly depicts the tenuous and nebulous border between the self and the other by exploring the inner realms and experiences of the marginalised. Through catharsis, she fosters a sense of proximity between these “others” and the reader. Despite portraying tragic scenarios, she maintains a quality of levity that is challenging to articulate but perceptible.

Instrumentation in Tractor for Evaluating Performance of a Tractor Drawn Rotavator

Performance measurement of agricultural machinery is essential for enhancing their efficiency for which they are operated. The performance related data helps the operator to select optimum operating parameters which reduce fuel consumption and increase field productivity. This study was aimed to develop various sensing units and install them on the tractor for evaluating performance of a tractor drawn rotavator. A microcontroller based embedded system was developed for continuous recording of wheel slip, actual forward speed, throttle opening, engine speed, velocity ratio, draft and power take-off (PTO) torque related data while carrying out tillage operation with rotavator. The wheel slip (from 1.5% to 35%) and actual forward speed of the tractor (up to 6 km h-1) was measured using inductive proximity sensors. For measuring throttle opening (0 to 100%), engine speed (750 to 2500 rpm), draft (65 to 350 kg) and PTO torque (up to 420 N-m) values, a potentiometer, magnetic pickup sensor, S-type loadcell and PTO torque transducer, respectively, were used. For computing velocity ratio, peripheral speed of rotavator was measured using PTO speed data received from PTO torque transducer, and actual forward speed of tractor was measured using inductive proximity sensor. The developed sensing units were calibrated under both dynamic and static conditions, and were also verified in laboratory and field conditions. Results showed that output signals received from the sensing units varied linearly with applied load. The mean absolute percentage error (MAPE) between measured and actual values was found to be below 5%. From the measured parameters, specific energy requirement and total power required for carrying out tillage operation could be computed. The power requirement was found to increase with increase in both gear and throttle settings. The specific energy requirement was minimum when rotavator was operated in L2 gear of the tractor. The minimum specific energy of 21 Wh m-3 was found at L2 gear and 75% throttle opening.

In-Motion, Non-Contact Detection of Ties and Ballasts on Railroad Tracks

This study aims to develop a robust and efficient system to identify ties and ballasts in motion using a variety of non-contact sensors mounted on a robotic rail cart. The sensors include distance LiDAR sensors and inductive proximity sensors for ferrous materials to collect data while traversing railroad tracks. Many existing tie/ballast health monitoring devices cannot be mounted on Hyrail vehicles for in-motion inspection due to their inability to filter out unwanted targets (i.e., ties or ballasts). The system studied here addresses that limitation by exploring several approaches based on distance LiDAR sensors. The first approach is based on calculating the running standard deviation of the measured distance from LiDAR sensors to tie or ballast surfaces. The second approach uses machine learning (ML) methods that combine two primary algorithms (Logistic Regression and Decision Tree) and three preprocessing methods (six models in total). The results indicate that the optimal configuration for non-contact, in-motion differentiation of ties and ballasts is integrating two distance LiDAR sensors with a Decision Tree model. This configuration provides rapid, accurate, and robust tie/ballast differentiation. The study also facilitates further sensor and inspection research and development in railroad track maintenance.

Noncontact Perception of Thin-Film Transistors by the Synergy of Both Capacitive and Electrostatic Induction Mechanisms.

In recent years, the rapid expansion of research and application of the Internet of Things and wearable electronics has prompted the development of a variety of sensors to perceive physical or chemical information from both the human body and the environment, among which the proximity sensor is a kind of noncontact sensor used to detect the approach of a target and thus exhibits promising applications in human-machine interactions. Thin-film transistors are one type of key components in modern electronics and have been further developed as electrostatic-induction-type proximity sensors to perceive the approach of electrically charged objects. However, they are immune to the approach of a zero-potential object. Capacitive-induction-type proximity sensors are capable of detecting the approach of conductive targets while being less sensitive to insulated ones. Integration of both electrostatic and capacitive induction mechanisms into one proximity sensor is highly expected to broaden its perception to a variety of targets. Here, an interdigital electrode was introduced as an extended gate into an amorphous metal oxide thin-film transistor to construct proximity sensors that combine both electrostatic and capacitive induction mechanisms and therefore can sensitively perceive the approach of a variety of objects that were electrically charged, grounded, or floated. Besides proximity sensing, remote velocity measurement and positioning of an invasive object were also realized, which further extended its functions as a kind of interdigital-electrode gate transistor.

Design and Construction of Automatic Railway Crossing Gate Control Using Proximity and Infrared Sensors Based on Omron CP1E E30-SDRA PLC

Railway crossing gates are one of the railway infrastructure facilities. Currently, there are still many problems, especially in traffic accidents. The cause of traffic accidents at railway crossings generally occurs due to the lack of facilities and infrastructure (rail crossing gates) and negligence of guards in carrying out their duties. Therefore, it is necessary to design an automatic railway crossing gate. The prototype of the automatic railway crossing controller uses proximity and infrared sensors to detect the arrival and departure of trains. When the infrared or proximity sensor detects the arrival of a train, the Programmable Logic Controller (PLC) activates Pulse Width Modulation (PWM), buzzer, and Light Emitting Diode (LED), so that the crossing motor goes down. After the crossing touches the limit switch, the motor stops and the buzzer and Light Emitting Diode (LED) remain on. The infrared or proximity sensor detects the departure of the train so that it reverses the motor so that the crossing goes up, then turns off the buzzer and Light Emitting Diode (LED). In this way, it is hoped that this design can be used to increase the efficiency of the system's operational costs and optimize the railway crossing system and is expected to reduce the number of traffic accidents at railway crossings.

Determination of soil organic carbon by conventional and spectral methods, including assessment of the use of biostimulants, N-fertilisers, and economic benefits

Soil organic carbon (SOC) is a very important indicator of soil quality, where even small changes can have large effects on the global carbon cycle, climate change, soil fertility and plant growth conditions. The aims of this study were to investigate changes in soil SOC using two types of biostimulants, to confirm the quality of different SOC determination methods, and to estimate fertiliser requirements and winter crop income and costs. Two methods were used to determine the concentration of SOC: the first was conventional sampling of two soil layers and laboratory analysis. The second method measured SOC using a mounted proximal soil sensor with an integrated visible and near-infrared (VIS-NIR) spectroscopic system. Experimental studies were carried out in three scenarios, of which SC1 used a biostimulant consisting of two components: one for soil (humus) and one for plants (leaf-special), SC2 used a biostimulant consisting of many bacterial species and SC3 was a control without biostimulant. The results of the 3-year experimental studies showed that the use of VIS-NIR spectroscopy in the field allows the prediction of soil SOC concentrations with sufficient accuracy. The coefficient of determination R2 ranged from 0.819 to 0.959 compared to the conventional laboratory SOC test. In the SC1 and SC2 scenarios with biostimulants, R2 was higher than in the SC3 control. The use of biostimulants significantly increased the yield of winter wheat and winter oilseed rape and reduced the need for nitrogen fertiliser per tonne of grain yield for both winter wheat (23.0–37.3 %) and winter oilseed rape (6.3–32.1 %). The economic analysis showed that the application of biostimulants increased the relative profit in all years from EUR 54 ha−1 (for winter oilseed rape) to EUR 143 ha−1 (for winter wheat) compared to the control scenario.