scholarly journals A Wi-Fi Based Smart Irrigation Monitoring for an Agricultural Environment

2021 ◽  
Author(s):  
Nikhil Patankar ◽  
Manoli Charmal ◽  
Nikhil Bhaskar ◽  
Swati Janrao ◽  
Aniket Kamble
Keyword(s):  
Real Time ◽  
Crop Yield ◽  
Water Availability ◽  
Precision Agriculture ◽  
Mobile Application ◽  
Human Error ◽  
Weather Data ◽  
Agricultural Environment ◽  
Weather Patterns

A producer who controls irrigation using a smartphone. A firebase that uses weather data to predict when to water crops. Sensors that read how much water is in the soil. From water availability and unpredictable weather patterns to regulations from outside entities, it can be a challenge to irrigate crops. Nowadays IoT has different solutions to overcome with this kind of problem. The particular research targeted successfully by development effected system using NodeMCU, Sensors, firebase, and assertive application. These tools which could conserve a good amount of water, it will become vital to success as the population groves and water availability dries up. The tool works by helping farmers with limited irrigation capacity determine the best time to water their crops. The main ai m of automating the system is to provide a certain amount of water required by crops by monitoring the moisture of soil and surrounding temperature. This obtains with the help of sensors and NodeMCU for interfacing their values. The values are displayed on a mobile application in real-time using Google’s firebase. Irrigation using IoT is a key component of precision agriculture. By changing manual irrigation with automatic valves and systems reduces the human error. Farmer can monitor his crop yield from anywhere at any time.

scholarly journals Design and Implementation of an Energy-Efficient Weather Station for Wind Data Collection

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3831
Author(s):  
Padma Balaji Leelavinodhan ◽  
Massimo Vecchio ◽  
Fabio Antonelli ◽  
Andrea Maestrini ◽  
Davide Brunelli
Keyword(s):  
Data Collection ◽  
Precision Agriculture ◽  
Climatic Factors ◽  
Wind Data ◽  
Weather Data ◽  
Weather Station ◽  
Weather Patterns ◽  
Weather Stations ◽  
Iot Devices ◽  
Local Weather

Agriculture faces critical challenges caused by changing climatic factors and weather patterns with random distribution. This has increased the need for accurate local weather predictions and weather data collection to support precision agriculture. The demand for uninterrupted weather stations is overwhelming, and the Internet of Things (IoT) has the potential to address this demand. One major challenge of energy constraint in remotely deployed IoT devices can be resolved using weather stations that are energy neutral. This paper focuses on optimizing the energy consumption of a weather station by optimizing the data collected and sent from the sensor deployed in remote locations. An asynchronous optimization algorithm for wind data collection has been successfully developed, using the development lifecyle specifically designed for weather stations and focused on achieving energy neutrality. The developed IoT weather station was deployed in the field, and it has the potential to reduce the power consumption of the weather station by more than 60%.

scholarly journals Iot Based Weather Station Using Raspberry Pi 3

2018 ◽  
Vol 7 (4.30) ◽  
pp. 145 ◽  
Author(s):  
P Y Muck ◽  
M J Homam
Keyword(s):  
Real Time ◽  
Mobile Application ◽  
Weather Prediction ◽  
Weather Conditions ◽  
Weather Data ◽  
Raspberry Pi ◽  
Weather Station ◽  
Media Platform ◽  
Weather Parameters ◽  
Graphical Presentation

Weather is the day-to-day state of atmosphere that is hard to predict which affects the activities of mankind and has great significance in many different domains. However, the current weather station in the market is expensive and bulky which cause inconvenience. The aim of this project is to design a weather station with real time notifications for climatology monitoring, interface it to a cloud platform and analyse weather parameters. In this project, a weather station is assembled using SparkFun Weather Shield and Weather Meter and Arduino Uno R3 to collect weather parameters. Data collected from the sensors are then stored into Google Cloud SQL using Raspberry Pi 3 Model B which acts as a gateway between them and analysis of weather data are done. A website and mobile application are developed using Google Data Studio and Android Studio respectively to display the real-time weather conditions in graphical presentation which are accessible by administrator and users. Users will receive notification regarding the weather conditions at that particular place on social media platform regularly and irregularly. Weather prediction is done in short term which allows users to get themselves prepared for their future plan in the next thirty minutes.

scholarly journals Real-time assessment of plant photosynthetic pigment contents with an artificial intelligence approach in a mobile application

2020 ◽  
Vol 51 (4) ◽  
pp. 220-228
Author(s):  
Kestrilia Rega Prilianti ◽  
Syaiful Anam ◽  
Tatas Hardo Panintingjati Brotosudarmo ◽  
Agus Suryanto
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Precision Agriculture ◽  
Mobile Application ◽  
Environmental Changes ◽  
Photosynthetic Pigment ◽  
Smart Devices ◽  
Pigment Contents ◽  
Intelligence Approach ◽  
Artificial Intelligence Approach

The assessment of the photosynthetic pigment contents in plants is a common procedure in agricultural studies and can describe plant conditions, such as their nutritional status, response to environmental changes, senescence, disease status and so forth. In this report, we show how the photosynthetic pigment contents in plant leaves can be predicted non-destructively and in real-time with an artificial intelligence approach. Using a convolutional neural network (CNN) model that was embedded in an Androidbased mobile application, a digital image of a leaf was processed to predict the three main photosynthetic pigment contents: chlorophyll, carotenoid and anthocyanin. The data representation, low sample size handling and developmental strategies of the best CNN model are discussed in this report. Our CNN model, photosynthetic pigment prediction network (P3Net), could accurately predict the chlorophyll, carotenoid and anthocyanin contents simultaneously. The prediction error for anthocyanin was ±2.93 mg/g (in the range of 0-345.45 mg/g), that for carotenoid was ±2.14 mg/g (in the range of 0-211.30 mg/g) and that for chlorophyll was ±5.75 mg/g (in the range of 0-892.25 mg/g). This is a promising result as a baseline for the future development of IoT smart devices in precision agriculture.

scholarly journals Development of an Online Tool for Tracking Soil Nitrogen to Improve the Environmental Performance of Maize Production

2021 ◽  
Vol 13 (10) ◽  
pp. 5649
Author(s):  
Giovani Preza-Fontes ◽  
Junming Wang ◽  
Muhammad Umar ◽  
Meilan Qi ◽  
Kamaljit Banger ◽  
...  
Keyword(s):  
Real Time ◽  
N Uptake ◽  
Growing Season ◽  
Weather Data ◽  
N Availability ◽  
Soil N ◽  
N Losses ◽  
Online Tool ◽  
Support Tool ◽  
Soil N Availability

Freshwater nitrogen (N) pollution is a significant sustainability concern in agriculture. In the U.S. Midwest, large precipitation events during winter and spring are a major driver of N losses. Uncertainty about the fate of applied N early in the growing season can prompt farmers to make additional N applications, increasing the risk of environmental N losses. New tools are needed to provide real-time estimates of soil inorganic N status for corn (Zea mays L.) production, especially considering projected increases in precipitation and N losses due to climate change. In this study, we describe the initial stages of developing an online tool for tracking soil N, which included, (i) implementing a network of field trials to monitor changes in soil N concentration during the winter and early growing season, (ii) calibrating and validating a process-based model for soil and crop N cycling, and (iii) developing a user-friendly and publicly available online decision support tool that could potentially assist N fertilizer management. The online tool can estimate real-time soil N availability by simulating corn growth, crop N uptake, soil organic matter mineralization, and N losses from assimilated soil data (from USDA gSSURGO soil database), hourly weather data (from National Weather Service Real-Time Mesoscale Analysis), and user-entered crop management information that is readily available for farmers. The assimilated data have a resolution of 2.5 km. Given limitations in prediction accuracy, however, we acknowledge that further work is needed to improve model performance, which is also critical for enabling adoption by potential users, such as agricultural producers, fertilizer industry, and researchers. We discuss the strengths and limitations of attempting to provide rapid and cost-effective estimates of soil N availability to support in-season N management decisions, specifically related to the need for supplemental N application. If barriers to adoption are overcome to facilitate broader use by farmers, such tools could balance the need for ensuring sufficient soil N supply while decreasing the risk of N losses, and helping increase N use efficiency, reduce pollution, and increase profits.

scholarly journals A Real-Time, Automatic, and Dynamic Scheduling and Control System for PET Patients Based on Wearable Sensors

Sensors ◽  
2021 ◽  
Vol 21 (4) ◽  
pp. 1104
Author(s):  
Shin-Yan Chiou ◽  
Kun-Ju Lin ◽  
Ya-Xin Dong
Keyword(s):  
Control System ◽  
Real Time ◽  
Human Error ◽  
Dynamic Scheduling ◽  
Wearable Sensors ◽  
Time Data ◽  
Scan Time ◽  
Number Of Patients ◽  
And Control ◽  
Automatic Scheduling

Positron emission tomography (PET) is one of the commonly used scanning techniques. Medical staff manually calculate the estimated scan time for each PET device. However, the number of PET scanning devices is small, the number of patients is large, and there are many changes including rescanning requirements, which makes it very error-prone, puts pressure on staff, and causes trouble for patients and their families. Although previous studies proposed algorithms for specific inspections, there is currently no research on improving the PET process. This paper proposes a real-time automatic scheduling and control system for PET patients with wearable sensors. The system can automatically schedule, estimate and instantly update the time of various tasks, and automatically allocate beds and announce schedule information in real time. We implemented this system, collected time data of 200 actual patients, and put these data into the implementation program for simulation and comparison. The average time difference between manual and automatic scheduling was 7.32 min, and it could reduce the average examination time of 82% of patients by 6.14 ± 4.61 min. This convinces us the system is correct and can improve time efficiency, while avoiding human error and staff pressure, and avoiding trouble for patients and their families.

scholarly journals Recognition of Bloom/Yield in Crop Images Using Deep Learning Models for Smart Agriculture: A Review

Agronomy ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 646
Author(s):  
Bini Darwin ◽  
Pamela Dharmaraj ◽  
Shajin Prince ◽  
Daniela Elena Popescu ◽  
Duraisamy Jude Hemanth
Keyword(s):  
Deep Learning ◽  
Crop Yield ◽  
Precision Agriculture ◽  
Soil Parameters ◽  
Learning Models ◽  
Weed Detection ◽  
Agricultural Industry ◽  
Agricultural Robots ◽  
Detection Techniques ◽  
Learning Techniques

Precision agriculture is a crucial way to achieve greater yields by utilizing the natural deposits in a diverse environment. The yield of a crop may vary from year to year depending on the variations in climate, soil parameters and fertilizers used. Automation in the agricultural industry moderates the usage of resources and can increase the quality of food in the post-pandemic world. Agricultural robots have been developed for crop seeding, monitoring, weed control, pest management and harvesting. Physical counting of fruitlets, flowers or fruits at various phases of growth is labour intensive as well as an expensive procedure for crop yield estimation. Remote sensing technologies offer accuracy and reliability in crop yield prediction and estimation. The automation in image analysis with computer vision and deep learning models provides precise field and yield maps. In this review, it has been observed that the application of deep learning techniques has provided a better accuracy for smart farming. The crops taken for the study are fruits such as grapes, apples, citrus, tomatoes and vegetables such as sugarcane, corn, soybean, cucumber, maize, wheat. The research works which are carried out in this research paper are available as products for applications such as robot harvesting, weed detection and pest infestation. The methods which made use of conventional deep learning techniques have provided an average accuracy of 92.51%. This paper elucidates the diverse automation approaches for crop yield detection techniques with virtual analysis and classifier approaches. Technical hitches in the deep learning techniques have progressed with limitations and future investigations are also surveyed. This work highlights the machine vision and deep learning models which need to be explored for improving automated precision farming expressly during this pandemic.

Measurement of a World-Wide Transfer Function for Marine Cloud Brightening

2021 ◽  
Vol 6 (3) ◽  
Author(s):  
Salter SH ◽  
Keyword(s):  
Transfer Function ◽  
Real Time ◽  
World Wide ◽  
Climate Model ◽  
Sea Water ◽  
Cloud Condensation Nuclei ◽  
Short Life ◽  
Weather Patterns ◽  
Weather Observations ◽  
The World

Marine cloud brightening with a sub-micron spray of filtered sea water can exploit the Twomey effect to enhance planetary cooling. Several previous climate model results show that it can also affect precipitation in both directions in different places. Modulating the climate model settings for the concentration of cloud condensation nuclei with separate coded sequences in a number of spray regions round the world and correlating each sequence with the resulting weather patterns in observing stations round the world can give an everywhereto- everywhere transfer function of spray from each region to each observing station. The short life of spray allows the best choices of spray regions and seasons. Spray patterns can be modified tactically to suit real-time weather observations.

Case Study: Enhancing Human Reliability With Artificial Intelligence and Augmented Reality Tools for Nuclear Maintenance

2018 ◽  
Author(s):  
Geoffrey Momin ◽  
Raj Panchal ◽  
Daniel Liu ◽  
Sharman Perera
Keyword(s):  
Augmented Reality ◽  
Real Time ◽  
Human Error ◽  
Nuclear Industry ◽  
Industrial Accidents ◽  
Worker Productivity ◽  
Energy Agency ◽  
Computer Mediated ◽  
Critical Feedback

Human error accounts for about 60% of the annual power loss due to maintenance incidents in the fossil power industry. The International Atomic Energy Agency reports that 80\% of industrial accidents in the nuclear industry can be attributed to human error and 20\% to equipment failure. The Personal Augmented Reality Reference System (PARRS) is a suite of computer-mediated reality applications that looks to minimize human error by digitizing manual procedures and providing real-time monitoring of hazards present in an environment. Our mission is to be able to provide critical feedback to inform personnel in real-time and protect them from avoidable hazards. PARRS aims to minimize human error and increase worker productivity by bringing innovation to safety and procedural compliance by leveraging technologies such as augmented reality, LiDAR, computer machine learning and particulate mapping using remote systems.

scholarly journals Applying Nitrogen Site-Specifically Using Soil Electrical Conductivity Maps and Precision Agriculture Technology

2001 ◽  
Vol 1 ◽  
pp. 767-776 ◽  
Author(s):  
E.D. Lund ◽  
M.C. Wolcott ◽  
G.P. Hanson
Keyword(s):  
Electrical Conductivity ◽  
Case Studies ◽  
Crop Yield ◽  
Soil Texture ◽  
Precision Agriculture ◽  
Management Practices ◽  
N Availability ◽  
Soil Electrical Conductivity ◽  
Variable Yield ◽  
Wide Range

Soil texture varies significantly within many agricultural fields. The physical properties of soil, such as soil texture, have a direct effect on water holding capacity, cation exchange capacity, crop yield, production capability, and nitrogen (N) loss variations within a field. In short, mobile nutrients are used, lost, and stored differently as soil textures vary. A uniform application of N to varying soils results in a wide range of N availability to the crop. N applied in excess of crop usage results in a waste of the grower’s input expense, a potential negative effect on the environment, and in some crops a reduction of crop quality, yield, and harvestability. Inadequate N levels represent a lost opportunity for crop yield and profit. The global positioning system (GPS)-referenced mapping of bulk soil electrical conductivity (EC) has been shown to serve as an effective proxy for soil texture and other soil properties. Soils with a high clay content conduct more electricity than coarser textured soils, which results in higher EC values. This paper will describe the EC mapping process and provide case studies of site-specific N applications based on EC maps. Results of these case studies suggest that N can be managed site-specifically using a variety of management practices, including soil sampling, variable yield goals, and cropping history.

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