scholarly journals A digital sensor to measure real-time leaf movements and detect abiotic stress in plants

2021 ◽  
Author(s):  
Geldhof Batist ◽  
Pattyn Jolien ◽  
Eyland David ◽  
Carpentier Sebastien ◽  
Van de Poel Bram
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Two Dimensions ◽  
Sensor System ◽  
Measurement Unit ◽  
Plant Phenotyping ◽  
Growth Responses ◽  
Plant Organ ◽  
Potential Applications ◽  
Leaf Movements

Abstract Plant and plant organ movements are the result of a complex integration of endogenous growth and developmental responses, partially controlled by the circadian clock, and external environmental cues. Monitoring of plant motion is typically done by image-based phenotyping techniques with the aid of computer vision algorithms. Here we present a method to measure leaf movements using a digital inertial measurement unit (IMU) sensor. The lightweight sensor is easily attachable to a leaf or plant organ and records angular traits in real-time for two dimensions (pitch and roll) with high resolution (measured sensor oscillations of 0.36° ± 0.53° for pitch and 0.50° ± 0.65° for roll). We were able to record simple movements such as petiole bending, as well as complex lamina motions, in several crops, ranging from tomato to banana. We also assessed growth responses in terms of lettuce rosette expansion and maize seedling stem movements. The IMU sensors are capable of detecting small changes of nutations (i.e., bending movements) in leaves of different ages and in different plant species. In addition, the sensor system can also monitor stress-induced leaf movements. We observed that unfavorable environmental conditions evoke certain leaf movements, such as drastic epinastic responses, as well as subtle fading of the amplitude of nutations. In summary, the presented digital sensor system enables continuous detection of a variety of leaf motions with high precision, and is a low-cost tool in the field of plant phenotyping, with potential applications in early stress detection.

scholarly journals Development of a Raspberry Pi-Based Sensor System for Automated In-Field Monitoring to Support Crop Breeding Programs

Inventions ◽  
2021 ◽  
Vol 6 (2) ◽  
pp. 42
Author(s):  
Worasit Sangjan ◽  
Arron H. Carter ◽  
Michael O. Pumphrey ◽  
Vadim Jitkov ◽  
Sindhuja Sankaran
Keyword(s):  
Real Time ◽  
Imaging System ◽  
Crop Growth ◽  
Sensor System ◽  
Plant Phenotyping ◽  
Raspberry Pi ◽  
The Internet ◽  
Growth Stages ◽  
Crop Breeding ◽  
Breeding Programs

Sensor applications for plant phenotyping can advance and strengthen crop breeding programs. One of the powerful sensing options is the automated sensor system, which can be customized and applied for plant science research. The system can provide high spatial and temporal resolution data to delineate crop interaction with weather changes in a diverse environment. Such a system can be integrated with the internet to enable the internet of things (IoT)-based sensor system development for real-time crop monitoring and management. In this study, the Raspberry Pi-based sensor (imaging) system was fabricated and integrated with a microclimate sensor to evaluate crop growth in a spring wheat breeding trial for automated phenotyping applications. Such an in-field sensor system will increase the reproducibility of measurements and improve the selection efficiency by investigating dynamic crop responses as well as identifying key growth stages (e.g., heading), assisting in the development of high-performing crop varieties. In the low-cost system developed here-in, a Raspberry Pi computer and multiple cameras (RGB and multispectral) were the main components. The system was programmed to automatically capture and manage the crop image data at user-defined time points throughout the season. The acquired images were suitable for extracting quantifiable plant traits, and the images were automatically processed through a Python script (an open-source programming language) to extract vegetation indices, representing crop growth and overall health. Ongoing efforts are conducted towards integrating the sensor system for real-time data monitoring via the internet that will allow plant breeders to monitor multiple trials for timely crop management and decision making.

scholarly journals Differentiation of Patients with Balance Insufficiency (Vestibular Hypofunction) versus Normal Subjects Using a Low-Cost Small Wireless Wearable Gait Sensor

Biosensors ◽  
2019 ◽  
Vol 9 (1) ◽  
pp. 29 ◽  
Author(s):  
Tam Nguyen ◽  
Jonathan Young ◽  
Amanda Rodriguez ◽  
Steven Zupancic ◽  
Donald Lie
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Normal Subjects ◽  
The Elderly ◽  
Measurement Unit ◽  
Balance Disorders ◽  
Remote Sensor ◽  
Data Collection And Analysis ◽  
Dynamic Gait Index ◽  
Dynamic Gait

Balance disorders present a significant healthcare burden due to the potential for hospitalization or complications for the patient, especially among the elderly population when considering intangible losses such as quality of life, morbidities, and mortalities. This work is a continuation of our earlier works where we now examine feature extraction methodology on Dynamic Gait Index (DGI) tests and machine learning classifiers to differentiate patients with balance problems versus normal subjects on an expanded cohort of 60 patients. All data was obtained using our custom designed low-cost wireless gait analysis sensor (WGAS) containing a basic inertial measurement unit (IMU) worn by each subject during the DGI tests. The raw gait data is wirelessly transmitted from the WGAS for real-time gait data collection and analysis. Here we demonstrate predictive classifiers that achieve high accuracy, sensitivity, and specificity in distinguishing abnormal from normal gaits. These results show that gait data collected from our very low-cost wearable wireless gait sensor can effectively differentiate patients with balance disorders from normal subjects in real-time using various classifiers. Our ultimate goal is to be able to use a remote sensor such as the WGAS to accurately stratify an individual’s risk for falls.

Development and Implementation of a Novel Digital Shot Sensor System

2013 ◽  
Vol 364 ◽  
pp. 228-232
Author(s):  
Wei Tian Wang ◽  
Quan Jun Song ◽  
Yu Man Nie ◽  
Bu Yun Wang ◽  
Hong Yu Ren ◽  
...  
Keyword(s):  
Real Time ◽  
Information Acquisition ◽  
Inertial Measurement Unit ◽  
Degrees Of Freedom ◽  
Sensor System ◽  
Measurement Unit ◽  
Inertial Measurement ◽  
Kinetic Information ◽  
Shot Put

Kinetic information acquisition of shot throwing is significant for the train of shot put athletes. This paper presents a novel sensor system based on a 9 degrees of freedom inertial measurement unit, which provides attitude information of shot throwing in real time. The sensor system is designed with modularized structure and installed in the digital shot which has almost the same size and weight as the standard shot for females. A multi-target and multi-parameter information acquisition platform is constructed to acquire kinematics information. With the help of the sensor system, the coaches can combine attitude information with kinematics data to analyze the shot throwing movements.

scholarly journals Real-time Vehicle Roll Angle Estimation based on IoT low-cost devices and Neural Networks

2018 ◽  
Author(s):  
Javier Garcia-Guzman ◽  
Lisardo Prieto González ◽  
Jonatan Pajares Redondo ◽  
Mat Max Montalvo Martinez ◽  
María Jesús López Boada
Keyword(s):  
Neural Networks ◽  
Real Time ◽  
Low Cost ◽  
Roll Angle ◽  
Measurement Unit ◽  
Raspberry Pi ◽  
State Variables ◽  
Angle Estimation ◽  
Experimental Environment ◽  
Time Operation

Given the high number of vehicle-crash victims, it has been established as a priority to reduce this figure in the transportation sector. For this reason, many of the recent researches are focused on including control systems in existing vehicles, to improve their stability, comfort and handling. These systems need to know in every moment the behavior of the vehicle (state variables), among others, when the different maneuvers are performed, to actuate by means of the systems in the vehicle (brakes, steering, suspension) and, in this way, to achieve a good behavior. The main problem arises from the lack of ability to directly capture several required dynamic vehicle variables, such as roll angle, from low-cost sensors. Previous studies demonstrate that low-cost sensors can provide data in real-time with the required precision and reliability. Even more, other research works indicate that neural networks are efficient mechanisms to estimate roll angle. Nevertheless, it is necessary to assess that the fusion of data coming from low-cost devices and estimations provided by neural networks can fulfill the reliability and appropriateness requirements for using these technologies to improve overall safety in production vehicles. Because of the increasing of computing power, the reduction of consumption and electric devices size, along with the high variety of communication technologies and networking protocols using Internet have yield to Internet of Things (IoT) development. In order to address this issue, this study has two main goals: 1) Determine the appropriateness and performance of neural networks embedded in low-cost sensors kits to estimate roll angle required to evaluate rollover risk situations. 2) Compare the low-cost control unit devices (Intel Edison and Raspberry Pi 3 Model B), to provide the roll angle estimation with this artificial neural network-based approach. To fulfil these objectives an experimental environment has been set up composed of a van with two set of low-cost kits, one including a Raspberry Pi 3 Model B, low cost Inertial Measurement Unit (BNO055 - 37€) and GPS (Mtk3339 - 53€) and the other having an Intel Edison System on Chip linked to a SparkFun 9 Degrees of Freedom module. This experimental environment will be tested in different maneuvers for comparison purposes. Neural networks embedded in low-cost sensor kits provide roll angle estimations very approximated to real values. Even more, Intel Edison and Raspberry Pi 3 Model B have enough computing capabilities to successfully run roll angle estimation based on neural networks to determine rollover risks situation fulfilling real-time operation restrictions stated for this problem.

Low Cost IoT Sensor System for Real-time Remote Monitoring

2020 ◽  
Author(s):  
Matteo D'Aloia ◽  
Annalisa Longo ◽  
Gianluca Guadagno ◽  
Mariano Pulpito ◽  
Paolo Fornarelli ◽  
...  
Keyword(s):  
Real Time ◽  
Remote Monitoring ◽  
Low Cost ◽  
Sensor System

scholarly journals A New Cable-Less Seismograph with Functions of Real-Time Data Transmitting and High-Precision Differential Self-Positioning

Sensors ◽  
2020 ◽  
Vol 20 (14) ◽  
pp. 4015
Author(s):  
Kang Liu ◽  
Qingyu You ◽  
Juan Wang ◽  
Xiqiang Xu ◽  
Pengcheng Shi ◽  
...  
Keyword(s):  
Real Time ◽  
High Precision ◽  
Low Cost ◽  
Area Network ◽  
Time Data ◽  
Zigbee Technology ◽  
Field Quality ◽  
Potential Applications ◽  
Differential Positioning ◽  
Working Efficiency

This study developed a new cable-less seismograph system, which can transmit seismic data in real-time and automatically perform high-precision differential self-positioning. Combining the ZigBee technology with the high-precision differential positioning module, this new seismograph system utilized the wireless personal area network (WPAN) and real-time kinematic (RTK) technologies to improve its on-site performances and to make the field quality control (QC) and self-positioning possible. With the advantages of low-cost, good scalability, and good compatibility, the proposed new cable-less seismograph system can improve the field working efficiency and data processing capability. It has potential applications in noise seismology and mobile seismic monitoring.

scholarly journals A Redundant Configuration of Four Low-Cost GNSS-RTK Receivers for Reliable Estimation of Vehicular Position and Posture

Sensors ◽  
2021 ◽  
Vol 21 (17) ◽  
pp. 5853
Author(s):  
Jesús Morales ◽  
Jorge L. Martínez ◽  
Alfonso J. García-Cerezo
Keyword(s):  
Operating System ◽  
Real Time ◽  
Reference System ◽  
High Performance ◽  
Low Cost ◽  
Sensor System ◽  
Dynamic Tests ◽  
Irregular Terrain ◽  
Reliable Estimation ◽  
Accurate Position

This paper proposes a low-cost sensor system composed of four GNSS-RTK receivers to obtain accurate position and posture estimations for a vehicle in real-time. The four antennas of the receivers are placed so that every three-antennas combination is optimal to get the most precise 3D coordinates with respect to a global reference system. The redundancy provided by the fourth receiver allows to improve estimations even more and to maintain accuracy when one of the receivers fails. A mini computer with the Robotic Operating System is responsible for merging all the available measurements reliably. Successful experiments have been carried out with a ground rover on irregular terrain. Angular estimates similar to those of a high-performance IMU have been achieved in dynamic tests.

scholarly journals Real-Time Visual Tracking of Moving Targets Using a Low-Cost Unmanned Aerial Vehicle with a 3-Axis Stabilized Gimbal System

2020 ◽  
Vol 10 (15) ◽  
pp. 5064
Author(s):  
Xuancen Liu ◽  
Yueneng Yang ◽  
Chenxiang Ma ◽  
Jie Li ◽  
Shifeng Zhang
Keyword(s):  
Real Time ◽  
Visual Tracking ◽  
Flight Control ◽  
Low Cost ◽  
Detection Algorithm ◽  
Outdoor Environment ◽  
Measurement Unit ◽  
Tracking Tasks ◽  
Great Performance ◽  
Aerial Vehicle

Unmanned Aerial Vehicles (UAVs) have recently shown great performance collecting visual data through autonomous exploration and mapping, which are widely used in reconnaissance, surveillance, and target acquisition (RSTA) applications. In this paper, we present an onboard vision-based system for low-cost UAVs to autonomously track a moving target. Real-time visual tracking is achieved by using an object detection algorithm based on the Kernelized Correlation Filter (KCF) tracker. A 3-axis gimbaled camera with separate Inertial Measurement Unit (IMU) is used to aim at the selected target during flights. The flight control algorithm for tracking tasks is implemented on a customized quadrotor equipped with an onboard computer and a microcontroller. The proposed system is experimentally validated by successfully chasing a ground and aerial target in an outdoor environment, which has proven its reliability and efficiency.

Smart ECG Monitoring Through IoT

2020 ◽  
pp. 224-246
Author(s):  
Ibtihel Nouira ◽  
Mohamed Hadj Said
Keyword(s):  
Real Time ◽  
Low Cost ◽  
Measurement Unit ◽  
Cloud Environment ◽  
Process Technology ◽  
Remote Monitoring System ◽  
Unit Module ◽  
Electrocardiogram Signal ◽  
Flexible Fabrication ◽  
Minimal Constraints

The emergence of internet of things allows the integration of health systems by enabling real-time monitoring with a low cost. Therefore, one of the essential targets in this work is the realization of a new smart real-time electrocardiogram remote monitoring system based on cloud networks. This health wireless system allows the acquisition of electrocardiogram signal with the temperature and acceleration measurement of the patient's body using the inertial measurement unit module sensor. A strong access schemes is employed to transfer the data from sensors to cloud environment by keeping the protection of e-health information. The second objective in this chapter is designing a flexible and stretchable health circuit basing on design considerations, aiming the combination of flexible, elastic, and rigid materials around minimal constraints and maximum mechanical dependability in the structures. The flexible fabrication part was inspired from the biocompatible process technology.

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