Handbook of Computational Sciences E-Book
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- Herausgeber: John Wiley & Sons
- Kategorie: Wissenschaft und neue Technologien
- Sprache: Englisch
The Handbook of Computational Sciences is a comprehensive collection of research chapters that brings together the latest advances and trends in computational sciences and addresses the interdisciplinary nature of computational sciences, which require expertise from multiple disciplines to solve complex problems. This edited volume covers a broad range of topics, including computational physics, chemistry, biology, engineering, finance, and social sciences. Each chapter provides an in-depth discussion of the state-of-the-art techniques and methodologies used in the respective field. The book also highlights the challenges and opportunities for future research in these areas. The volume pertains to applications in the areas of imaging, medical imaging, wireless and WS networks, IoT with applied areas, big data for various applicable solutions, etc. This text delves deeply into the core subject and then broadens to encompass the interlinking, interdisciplinary, and cross-disciplinary sections of other relevant areas. Those areas include applied, simulation, modeling, real-time, research applications, and more. Audience Because of the book's multidisciplinary approach, it will be of value to many researchers and engineers in different fields including computational biologists, computational chemists, and physicists, as well as those in life sciences, neuroscience, mathematics, and software engineering.
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Veröffentlichungsjahr: 2023
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Table of Contents
Cover
Series Page
Title Page
Copyright Page
Preface
1 A Sensor-Based Automated Irrigation System for Indian Agricultural Fields
1.1 Introduction
1.2 Literary Survey
1.3 Proposed System
1.4 Performance Studies
1.5 Image Processing to Determine Physical Characteristics
1.6 Conclusion and Future Enhancements
References
2 An Enhanced Integrated Image Mining Approach to Address Macro Nutritional Deficiency Problems Limiting Maize Yield
2.1 Introduction
2.2 Related Work
2.3 Motivation
2.4 Framework of Enhanced Integrated Image Mining Approaches to Address Macro Nutritional Deficiency Problems Limiting Maize Yield
2.5 Algorithm – Enhanced Integrated Image Mining Approaches to Address Macro Nutritional Deficiency Problems Limiting Maize Yield
2.6 Conclusion
References
3 Collaborative Filtering Skyline (CFS) for Enhanced Recommender Systems
3.1 Introduction and Objective
3.2 Motivation
3.3 Literature Survey
3.4 System Analysis and Existing Systems
3.5 Proposed System
3.6 System Implementation
3.7 Conclusion and Future Enhancements
References
4 Automatic Retinopathic Diabetic Detection: Data Analyses, Approaches and Assessment Measures Using Deep Learning
4.1 Introduction
4.2 Related Work
4.3 Initial Steps and Experimental Environment
4.4 Experimental Environment
4.5 Data and Knowledge Sources
4.6 Data Preparation
4.7 CNN’s Integrated Design
4.8 Preparing Retinal Image Data
4.9 Performance Evaluation
4.10 Metrics
4.11 Investigation of Tests
4.12 Discussion
4.13 Results and Discussion
4.14 Conclusions
References
5 Design and Implementation of Smart Parking Management System Based on License Plate Detection
5.1 Introduction
5.2 Literature Survey
5.3 Proposed System
5.4 High Level Design of Proposed System
5.5 Project Requirement Specification
5.6 Algorithms
5.7 Proposed System Results
5.8 Conclusion
References
6 A Novel Algorithm for Stationary Analysis of the Characteristics of the Queue-Dependent Random Probability for Co-Processor-Shared Memory Using Computational Sciences
6.1 Introduction - Processor-Shared Service Queue with Independent Service Rate Using Probability Queuing Theory
6.2 The Applications of Queuing Models in Processor-Shared Memory
6.3 The Basic Structure of Queuing Models
6.4 Characteristics of Queuing System
6.5 The Arrival Pattern of Customers
6.6 The Service Pattern of Servers Queue Discipline, System Capacity, and the Number of Servers
6.7 Kendall’s Notation
6.8 The Formation of Retrial Queues as a Solution
6.9 The Stationary Analysis of the Characteristics of the M/M/2 Queue with Constant Repeated Attempts
6.10 Computation of the Steady-State Probabilities
6.11 Application of Retrial Queues
6.12 Conclusion
References
7 Smart e-Learning System with IoT-Enabled for Personalized Assessment
7.1 Introduction
7.2 Literature Study
7.3 Architecture Model
7.4 Assessment Technique: CBR Algorithm
7.5 Implementation Modules: Project Outcome
7.6 Conclusion
References
8 Implementation of File Sharing System Using Li-Fi Based on Internet of Things (IoT)
8.1 Introduction
8.2 Existing System/Related Work
8.3 Literature Survey
8.4 Proposed System
8.5 Workflow
8.6 Proposed Solution
8.7 Software Implementation
8.8 Block Diagram – Indoor Navigation System Using Li-Fi
8.9 Implementation
8.10 Unicode Transmission
8.11 Conclusion
References
9 Survey on Artificial Intelligence Techniques in the Diagnosis of Pleural Mesothelioma
9.1 Introduction
9.2 Methods
9.3 Analysis
9.4 Conclusion
References
10 Handwritten Character Recognition and Genetic Algorithms
10.1 Introduction
10.2 Recognition Framework for a Handwritten Character Recognition
10.3 Offline Character Recognition
10.4 Literature Review
10.5 Feature Extraction
10.6 Pattern Recognition
10.7 Noise Reduction
10.8 Segmentation
10.9 Pre-Processing
10.10 Hybrid Recognition
10.11 Applying Genetic Algorithm
10.12 Multilingual Characters
10.13 Results
References
11 An Intelligent Agent-Based Approach for COVID-19 Patient Distribution Management
11.1 Introduction
11.2 Intelligent Agent’s Architecture Proposal for COVID-19 Patient Distribution Management
11.3 Intelligent Agents Task Management
11.4 Java Agent Development Framework
11.5 Conclusions
References
12 Computational Science Role in Medical and Healthcare-Related Approach
12.1 Introduction
12.2 Background
12.3 Healthcare Nowadays
12.4 Healthcare Activities and Processes
12.5 Organization and Financial Aspects of Healthcare
12.6 Health Information Technology is Currently Being Used
12.7 Future Healthcare
12.8 Research Challenges
12.9 Modeling
12.10 Automation
12.11 Teamwork and Data Exchange
12.12 Scaled Data Management
12.13 Comprehensive Automated Recording of Exchanges Between Doctors and Patients
12.14 Johns Hopkins University: A Case Study
12.15 Case Study: Managing Patient Flow
12.16 Case Study: Using Electronic Health Records and Human Factors Engineering
12.17 Technology, Leadership, Culture, and Increased Learning are Necessary for the Spread of Systems Approaches
12.18 Conclusion
References
13 Impact of e-Business Services on Product Management
13.1 Introduction
13.2 Literature Review
13.3 Data Collection and Method Details
13.4 Results
13.5 Conclusion
References
14 Analysis of Lakeshore Images Obtained from Unmanned Aerial Vehicles
14.1 Introduction
14.2 Possibilities of Processing Images Acquired from Unmanned Aerial Vehicles
14.3 Defining Purpose, Location and Users of the Study
14.4 Suitable and Available Data and Vehicle
14.5 Usable Image Processing Methods
14.6 Invasive-Plant Detection
14.7 Evaluation and Interpretation of the Results
14.8 Analysis of Lakeshore – Koblov Lake
14.9 Area of Interest
14.10 Data Acquisition
14.11 Data Pre-Processing
14.12 Data Processing and Evaluation
14.13 Evaluation and Interpretation of the Results
14.14 Conclusion
References
15 Robotic Arm: Impact on Industrial and Domestic Applications
15.1 Introduction
15.2 Circuit Diagram
15.3 Literature Survey
15.4 Operation of Robot
15.5 The Benefits of Industrial Robotic Arms
15.6 Component Details
15.7 Working of Robotic Arm
15.8 Intel Takes Robotic Arms to The Next Level
15.9 Robotic Arm Applications
15.10 Conclusion
References
16 Effects of Using VR on Computer Science Students’ Learning Behavior in Indonesia: An Experimental Study for TEFL
16.1 Introduction
16.2 VR as FUTELA
16.3 Using the Internet to Study and Learning Behavior
16.4 Methods
16.5 Research Participants
16.6 Techniques for Analyzing Data
16.7 The Indicator of Efficiency
16.8 Results and Discussion
16.9 Discussion
16.10 Conclusion
References
17 Satisfaction of Students Toward Media and Technology Innovation Amidst COVID-19
17.1 Introduction
17.2 Literature Review
17.3 Methods
17.4 Results and Findings
17.5 Discussion
17.6 Conclusion
References
Index
End User License Agreement
List of Tables
Chapter 1
Table 1.1 Connection specification.
Table 1.2 Threshold values for temperature and moisture.
Table 1.3 Tabulation of results for irrigation automation.
Table 1.4 Physical characteristics of alluvial soil.
Table 1.5 Physical characteristics of red soil.
Chapter 3
Table 3.1 Symbols in algorithm.
Chapter 4
Table 4.1 Class distribution in the initial dataset.
Table 4.2 Three models’ CNN architectures.
Table 4.3 Class distribution following class balancing.
Table 4.4 Ensemble metrics for the identifying systems.
Table 4.5 Accuracy percentages for processed image classification images by ea...
Table 4.6 Accuracy rates of individual algorithms for image statistics.
Chapter 6
Table 6.1 Number of customers in the orbit.
Table 6.2 Variations of probabilities.
Chapter 8
Table 8.1 Literature survey.
Table 8.2 Literature survey.
Table 8.3 Base64 encoding table.
Table 8.4 Huffman coding table.
Table 8.5 Unique code table.
Chapter 9
Table 9.1 Performance of ANN techniques for mesothelioma.
Table 9.2 Performance of Mtiling and SVM.
Chapter 11
Table 11.1 Communication management.
Chapter 13
Table 13.1 Case study of manager and team members.
Table 13.2 Survey of questionnaire session in Sphoorti Machine Tools Pvt. Ltd.
Table 13.3 Survey of questionnaire session in Tata Motors.
Chapter 14
Table 14.1 Error matrix for evaluation of classification methods.
Chapter 16
Table 16.1 Pre-test.
Table 16.2 Post-test.
Table 16.3 Tests of normality.
Table 16.4 Paired samples test.
List of Illustrations
Chapter 1
Figure 1.1 Proposed system architecture.
Figure 1.2 Process flow of irrigation automation.
Figure 1.3 Circuit diagram of DC MOTOR.
Figure 1.4 Project explorer window showing the network shared variables.
Figure 1.5 NI MAX window for Wi-Fi configuration on NI-myRIO.
Figure 1.6 Process of physical parameter estimation of soil.
Figure 1.7 Block diagram for irrigation automation by monitoring soil temperat...
Figure 1.8 Front panel for irrigation automation.
Figure 1.9 Shared variables (T>30°C)-Motor ON.
Figure 1.10 Shared variables (M<50%)-Motor ON.
Figure 1.11 Shared variables (T<30°C and M>50%)-Motor OFF.
Figure 1.12 Shared variables (T>30°C and M<50%)-Motor ON.
Figure 1.13 (a) Alluvial soil samples. (b) Red soil samples.
Figure 1.14 LabVIEW VI for image processing.
Figure 1.15 Front panel of color thresholding and subarray extraction.
Figure 1.16 Front panel results for physical parameters of (i) alluvial soil a...
Chapter 2
Figure 2.1 Proposed architecture.
Figure 2.2 Presence of deficiency determined using multivariate image analysis...
Figure 2.3 Multiple macronutrients deficiency identification in maize crop usi...
Figure 2.4 Significance spectra range for nitrogen deficiency.
Figure 2.5 Significance spectra range for K deficiency.
Chapter 3
Figure 3.1 Skyline points.
Figure 3.2 Skyline points.
Figure 3.3 ESC algorithm.
Figure 3.4 ASC algorithm.
Figure 3.5 DFD Level 0.
Figure 3.6 DFD Level 1.
Figure 3.7 DFD for ASC algorithm.
Figure 3.8 ER diagram.
Chapter 4
Figure 4.1 A distinction of normal retina and retinopathy with diabetics.
Figure 4.2 DNN-PCA model for diabetic retinopathy.
Figure 4.3 Class distribution in the original dataset.
Figure 4.4 Unique image.
Figure 4.5 CNN architectures.
Figure 4.6 Class distribution after class balancing.
Figure 4.7 Retinal feature-based techniques for DR detection and segmentation.
Figure 4.8 Changes in blood vessel structure (a), unique fundus image (b), and...
Figure 4.9 Examples of the dataset’s poor quality and preprocessing images.
Figure 4.10 A comparison of the identification system’s preprocessing before a...
Figure 4.11 The training curves for the identification system’s various parts.
Figure 4.12 Accuracy percentages of each algorithm.
Figure 4.13 Accuracy rates of each algorithm for image statistical data.
Chapter 5
Figure 5.1 Typical car park system [1].
Figure 5.2 Proposed architecture.
Figure 5.3 DFD level 0.
Figure 5.4 DFD level 1.
Figure 5.5 DFD level 3.
Figure 5.6 Class diagram.
Figure 5.7 Use case diagram.
Figure 5.8 Sequence diagram.
Figure 5.9 Activity diagram.
Figure 5.10 License plate sizing sequence.
Figure 5.11 User Login and registration.
Figure 5.12 User dashboard.
Figure 5.13 Adding vehicle details.
Figure 5.14 Check available slot.
Figure 5.15 Parking booking.
Figure 5.16 Parking booking: out time.
Figure 5.17 Parking payment.
Figure 5.18 Vehicle license plate pre-processing.
Figure 5.19 Vehicle license plate recognized.
Chapter 6
Figure 6.1 A typical queuing process.
Figure 6.2 Retrial queues.
Figure 6.3 Number of busy servers.
Chapter 7
Figure 7.1 IoT company influence [4].
Figure 7.2 System architecture.
Figure 7.3 Activity diagram.
Figure 7.4 Component diagram.
Figure 7.5 Homepage.
Figure 7.6 Homepage – e-learning platform introduction.
Figure 7.7 Download notes.
Figure 7.8 Author upload notes.
Figure 7.9 “Raspberry Pi 3 Model B kit” [12].
Figure 7.10 Test questions page 01.
Figure 7.11 Test questions page 02.
Figure 7.12 Test assessment result page.
Chapter 8
Figure 8.1 Transmitter side workflow.
Figure 8.2 Image representation of 5 × 5 pixels.
Figure 8.3 Frequency of pixels.
Figure 8.4 Huffman optimal tree.
Figure 8.5 Receiver side workflow.
Figure 8.6 Li-Fi hardware implementation.
Figure 8.7 Execution of text transfer.
Figure 8.8 Image transfer module.
Figure 8.9 Block diagram of indoor map reading system.
Figure 8.10 Demonstration of indoor map reading system.
Figure 8.11 Demonstration of placement of LED in room.
Chapter 9
Figure 9.1 Healthy and MM-affected lung.
Figure 9.2 MTiling architecture.
Chapter 10
Figure 10.1 Different styles of handwritten character writing.
Figure 10.2 Lexicon character sets for bank cheque recognizer.
Figure 10.3 Preprocessing.
Figure 10.4 Flow of process in genetic algorithm.
Figure 10.5 Hindi characters.
Figure 10.6 Tamil characters.
Figure 10.7 Malayalam consonants (vyanjanams).
Figure 10.8 Tamil characters.
Figure 10.9 Kannada characters.
Figure 10.10 Polish characters.
Chapter 11
Figure 11.1 Intelligent agents’ architecture for COVID-19 patient distribution...
Figure 11.2 JADE implementation 1.
Figure 11.3 JADE implementation 2.
Figure 11.4 Intelligent agent – send message.
Figure 11.5 Intelligent agent – receive message.
Figure 11.6 JADE agent container running.
Figure 11.7 JADE agent container finish running.
Chapter 12
Figure 12.1 Various factors responsible for healthcare management.
Figure 12.2 Process flow of EMR.
Figure 12.3 Medical computing using artificial intelligence.
Figure 12.4 Flow diagram for AI-assisted healthcare.
Figure 12.5 Healthcare data management system.
Figure 12.6 Healthcare activities and processes.
Figure 12.7 Organization and financial aspects of healthcare.
Figure 12.8 Current Health information technology.
Figure 12.9 Future healthcare.
Figure 12.10 Various types of healthcare-related research.
Figure 12.11 Data of number of deaths vs cases.
Figure 12.12 Simulation of patient flow.
Chapter 13
Figure 13.1 Design of well-designed product-service-system.
Figure 13.2 Description of SusProNet networking with investors.
Figure 13.3 Structure of Industry 4.0.
Figure 13.4 Description of the working process in a smart manufacturing indust...
Figure 13.5 Graphical representation of benefits of IoT.
Figure 13.6 Examples of smart products.
Chapter 14
Figure 14.1 Example of potential
Solidago
invasion directions on a fragment of...
Figure 14.2 Location of Koblov lake [22].
Figure 14.3 (a) Koblov shoreline surveyed using the GNSS RTK technique and (b)...
Figure 14.4 Dovetail target placed on the shore of Koblov Lake [Source: Author...
Figure 14.5 Orthomosaic – 27 Jul 2021 – Koblov Lake [Source: Authors, 2022].
Figure 14.6 Reduction of the area of interest [Source: Authors, 2022].
Figure 14.7 Creating training samples from the orthomosaic from 27 Jul 2021 [S...
Figure 14.8 Classified water cover combined with an orthomosaic and the RTK co...
Figure 14.9 An invasive plant classified using the maximum likelihood classifi...
Figure 14.10 The original polygon and the extracted and smoothed shoreline of ...
Figure 14.11 An abundance of invasive plants on the shore of Koblov Lake, 27 J...
Chapter 15
Figure 15.1 Prototype of a mechanical arm.
Figure 15.2 Circuit diagram.
Figure 15.3 Robotic arm and its components.
Figure 15.4 Circuit using components.
Chapter 16
Figure 16.1 VR treatment in the class and students’ English performance.
Figure 16.2 Likeability on VR.
Figure 16.3 Interactiveness.
Figure 16.4 Retention.
Figure 16.5 Effectiveness and attractiveness.
Figure 16.6 VR platform from Co Space Edu.
Chapter 17
Figure 17.1 Mix method flow chart.
Figure 17.2 Constructivist approach to teaching/learning.
Figure 17.3 Effective online pedagogy.
Figure 17.4 Online learning and teaching strategy.
Figure 17.5 Pedagogical dimension.
Figure 17.6 Media and online technology for assessment.
Guide
Cover Page
Series Page
Title Page
Copyright Page
Preface
Table of Contents
Begin Reading
Index
WILEY END USER LICENSE AGREEMENT
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Scrivener Publishing100 Cummings Center, Suite 541JBeverly, MA 01915-6106
Publishers at ScrivenerMartin Scrivener ([email protected])Phillip Carmical ([email protected])
Handbook of Computational Sciences
A Multi and Interdisciplinary Approach
Edited by
Ahmed A. Elgnar
Faculty of Computers & Artificial Intelligence, Beni-Suef University, Egypt
Vigneshwar M
Cybase Technologies, Coimbatore, Tamil Nadu, India
Krishna Kant Singh
Department of Computer Science and Engineering, Amity University, Uttar Pradesh, India
and
Zdzislaw Polkowski
The Karkonosze University of Applied Sciences, Poland
This edition first published 2023 by John Wiley & Sons, Inc., 111 River Street, Hoboken, NJ 07030, USA and Scrivener Publishing LLC, 100 Cummings Center, Suite 541J, Beverly, MA 01915, USA© 2023 Scrivener Publishing LLCFor more information about Scrivener publications please visit www.scrivenerpublishing.com.
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Library of Congress Cataloging-in-Publication Data
ISBN 978-1-197-6046-7
Cover image: Pixabay.ComCover design by Russell Richardson
Preface
This handbook explores computational methods that are found on the engineering side of computer science. These methods appear in worldwide applications and are the focus of research works that seek to apply them to meet current and future societal needs.
This volume pertains to applications in the areas of imaging, medical imaging, wireless and WS networks, IoT with applied areas, big data for various applicable solutions, etc. This text delves deeply into the core subject and then broadens to encompass the interlinking, inter–disciplinary, and cross-disciplinary sections of other relevant areas. Those areas include applied, simulation, modeling, real-time, research applications, and more.
In proportion with greater technological advancements come increased complexities and new intriguing issues that require micro-level analysis with strong consideration for future outcomes. Such analysis involves the use of computing hardware, networking, algorithms, data structures, programming, databases, and other domain-specific knowledge for the implementation of physical processes that run on computers.
Computational sciences can conglomerate with other cross- and inter-disciplines to evolve into something useful to humankind. A wide-ranging perspective is necessary for the evolution of a new paradigm. To accomplish this, the approach must involve highly advanced learning that includes research by scholars, scientists, engineers, medical practitioners, biologists, chemists, physicists, etc.; and it touches upon areas of physical-, biological-, and life science; many forms of computational science (physics, chemistry, neuroscience, mathematics, and biology), software engineering, arts and humanities, and more.
The editors wish to thank Scrivener Publishing and their team for the opportunity to publish this volume.
The Editors
May 2023
1A Sensor-Based Automated Irrigation System for Indian Agricultural Fields
I.S. Akila1 and Ahmed A. Elngar2*
1Department of ECE, Coimbatore Institute of Technology, Coimbatore, Tamil Nadu, India
2Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef City, Egypt
Abstract
The continuous monitoring of field conditions using an IoT system is essential to maintain the optimum levels of the field parameters like soil moisture and temperature and to increase productivity. Continuous monitoring and automation of irrigation to optimize field conditions is a challenge faced by the agriculture sector. The proposed work presents an IoT system that collects temperature and humidity data through the sensors, processes them, and automates the irrigation process based on the results. Considering the pragmatic feasibility of many conventional and modern approaches, this work proposes a simplified and economic solution to automate the process of irrigation amidst the challenges such as lack of skilled labor and overhead involved in the complexity of the existing solutions. The work could be extended to various geographical regions of the Indian landscape to make a comprehensive and all-inclusive solution for the automation of the irrigation process.
Keywords: Agriculture, irrigation, image processing, sensor, box-counting, fractal dimension
1.1 Introduction
Agriculture plays a major role in the Indian economy. Due to the unpredictable and inconsistent nature of factors that influence farming in India, there arises a need to extend the technology into agriculture thereby increasing productivity. Across the wide scope of the Indian landscape, farmers employ several styles of agriculture that could be synthesized and enhanced with the invention of emerging technologies.
Optimum soil temperature and moisture levels should be maintained to augment crop growth. Macronutrient levels should also be maintained to adequacy for better yield. A smart monitoring platform is required to monitor field conditions continuously. An effective automated system would reduce human efforts to a great extent. This system facilitates smart monitoring of several parameters such as optimum water and other soil properties that cumulatively influence productivity in the primary sector.
The process of automation in the agricultural monitoring process is done by interfacing the required sensors to NI myRIO, which is supported by the LabVIEW platform. Image processing technique using a suitable box counting algorithm estimates the fractal dimension of the soil particles and further, it has been implemented to detect physical parameters of the soil.
Modern technology finds its applications in various fields for better outcomes. Agriculture, a significant field to be monitored and enhanced, has exploited technology to obtain higher productivity, growth, and yield rates. Hence reducing human effort and automating the process of irrigation by enabling remote access to field parameters would contribute to the betterment of yields in agriculture.
The rest of the chapter is organized as follows. Section 1.2 presents significant research works performed to automate the process of agricultural monitoring amidst their boundaries. Section 1.3 illustrates the proposed work of this chapter. Section 1.4 discusses the performance study and results of the work. Section 1.5 concludes the work and presents future possibilities for enhancement.
1.2 Literary Survey
Through the traces of modern technological advancements, shortage of skilled labor, and increasing demand, there is much research work focused on the automation of agricultural monitoring systems.
A.D. Kadage and J.D. Gawade [1] have designed and implemented a system to monitor field conditions continuously by interfacing sensors to a microcontroller and notifying the farmer through SMS (Short Message Service) when field conditions deviate from normal parameters. The system utilizes a GSM module and prompts commands from the farmer to take necessary action. However, their work has not considered network availability and related constraints. Thus, continuous monitoring is facilitated without emphasizing automating the process of irrigation.
Swarup S. Mathurkar and Rahul B. Lanjewar [2] proposed a system to develop a smart sensor-based monitoring system for the agricultural environment using a Field Programmable Gate Array (FPGA) which comprised of a wireless protocol, different types of sensors, microcontroller, serial protocol, and the FPGA with display element. The work was done using sensors that help in checking moisture, temperature, and humidity conditions. According to these conditions, farmers can schedule their work.
Bansari Deb Majumder, Arijita Das, Dibyendu Sur, Sushmita Das, Avishek Brahma, and Chandan Dutta [3] have attempted to develop an automated system that can measure different agricultural process parameters (like temperature, soil moisture, sunlight intensity, humidity, chemical contents, etc.) and control using PID controller. These parameters can be remotely monitored and controlled. With the help of MATLAB interfaced with NI LabVIEW, a virtual simulation of the entire process on the front panel is made feasible. Alarm systems are incorporated to generate the necessary alarm signal in case of the worst scenario to alert the farmer about the consequences. This will provide the farmer with a remote-control approach to looking after his land and crops. It will also increase the productivity of land through efficient control and will reduce human efforts through complete automation of the harvesting process.
Anastasia Sofou, Georgios Evangelopoulos, and Petros Maragos [4] proposed a system to examine the sophisticated integration of selected modern methods for image feature extraction, texture analysis, and segmentation into homogeneous regions. The experimental results in images are digitized under different specifications and scales demonstrating the proposed system’s efficacy. Further, this work explores the possibilities of a smart agricultural monitoring system based on geographical constraints.
H.T. Ingale and N.N. Kasat [5] proposed a system for automatic irrigation by using sensors that would make farmers aware of the changing conditions of humidity levels to schedule the proper timing for irrigation. Due to the direct transfer of water to the roots, water conservation takes place and also helps to maintain the moisture to soil ratio at the root zone constant to some extent. Thus, the system is efficient and compatible with the changing environment. Also, the system saves water and improves the growth of plants. The influence of other climate parameters could be considered to improve the quality of the outcome in this work.
Rahul B. Lanjewar, Swarup S. Mathurkar, Nilesh R. Patel and Rohit S. Somkuwar [6] presented a sensor system to monitor and measure parameters such as temperature, soil moisture, and humidity. Further, the work done by M.K. Gayatri, J. Jayasakthi, and G.S. Anandha Mala [7] motivates the employment of the data obtained through emerging technologies such as the Internet of Things (IoT) and cloud computing to automate manual agricultural activities.
V. Vijay Hari Ram, H. Vishal, S. Dhanalakshmi and P. Meenakshi Vidya [8] presented a framework for regulating water supply in an agricultural field with a simple IoT model. Tanmay Baranwal, Nitika and Pushpendra Kumar Pateriya [9] proposed an IoT-based system for from the security perspective and further makes an example of using modern technologies to adopt and support traditional environments. In their work, Nikesh Gondchawar and R.S. Kawitkar [10] presented a GPS-based automation framework for a smart agriculture system.
Yijun Hu, Jingfang Shen, and Yonghao Qi [11] have predicted the growth of rice plants using fractal dimension parameters for the prediction of biomass of the rice and have proven to improvise the prediction model. P. Senthil and Akila I.S. [12] presented the Fire Bird V ATmega2560 robotic kit for the prediction of soil moisture and to determine the shortest path using Dijkstra’s algorithm for the robotic kit movement and it was proven that the system was efficient in monitoring the moisture content of the soil.
I.S. Akila, A. Sivakumar, and S. Swaminathan [13] depict the work of extracting the texture of the plant using image processing techniques and the height of the plant was estimated using a virtual height measurement scheme. The works of [14, 15], and [16] discuss the use of IoT in agriculture, its issues, challenges, and solutions. They also depict different IoT architectures which are application specific.
From the literature survey, the features such as network constraint, distance viability, reliability, complex algorithms in image processing, increased human efforts and manual control are identified as predominant parameters to improve contemporary agricultural monitoring systems. Our work is proposed to overcome these real-time challenges through automated irrigation, remote access to field conditions, and simple image processing techniques for determining soil physical parameters.
1.3 Proposed System
1.3.1 System Architecture
The block diagram for our proposed system is shown in Figure 1.1. The work is aimed at automating the irrigation process by interfacing the necessary sensors, namely, the temperature sensor and moisture sensors to the NI myRIO board with configured Wi-Fi (Wireless Fidelity) facility. When the power is supplied to myRIO, the temperature sensor monitors the soil temperature. If it exceeds 30°C, it will automatically turn on the motor. The temperature is displayed on the Data dashboard. Similarly, irrigation takes place when the moisture level violates the threshold range. When the temperature and moisture levels go below their respective threshold values, the motor remains off. The outcome of the sensors is used to automatically initiate the irrigation process.
Figure 1.1 Proposed system architecture.
1.3.2 Flow of Automated Irrigation
Figure 1.2 shows the flow of activities involved in the automation of the irrigation system. Initially, myRIO is configured and then interfaced with the appropriate sensors. The data are read from the sensor, and according to the threshold fixed, the automation control of ON/OFF the motor is done.
1.3.3 Interfacing Sensors
YL69 soil moisture sensor and LM35 soil temperature sensor are interfaced with the NI myRIO as per Table 1.1. The sensors are connected to the MXP connector pins of myRIO, where the MXP connector consists of both analogy and digital input and output pins with 5V.
Figure 1.2 Process flow of irrigation automation.
Table 1.1 Connection specification.
S. no.
Sensors
Sensor connector pins
Myrio connector pins
1
Soil moisture sensor
Vcc
MSP DIO(5V)
Ground
MSP DGND
AO (analog output)
MSP AI0+
2
Soil temperature sensor
Vcc
MXP A1
Analog output
MSP AI1+
GND
MSP AI 1-
The optimum values for the soil temperature and moisture are fixed as the threshold as depicted in Table 1.2 which is compared to the sensed values of parameters.
Upon the comparison of the sensed values and fixed threshold values, the motor is initiated, and irrigation is done automatically. A DC motor is interfaced to MXP connector PIN 27 as shown in Figure 1.3.
The NI Data dashboard mobile application enables the implementation of network-shared variables. In the irrigation automation the motor status, temperatures in Celsius, and percentage of moisture status have been configured as shared variables as shown in Figure 1.4. They can be accessed by connecting to the Wi-Fi network in the configured myRIO as shown in Figure 1.5. The NI Measurement and Automation Explorer provide a facility for configuring on-board Wi-Fi on NI myRIO. After the configuration of Wi-Fi, myRIO can be disconnected from the host computer and deployed to work as a stand-alone system.
Table 1.2 Threshold values for temperature and moisture.
S. no.
Parameters
Threshold values
1
Soil moisture content
50% of Moisture Value
2
Soil temperature
Up to 30° C
Figure 1.3 Circuit diagram of DC MOTOR.
Figure 1.4 Project explorer window showing the network shared variables.
Figure 1.5 NI MAX window for Wi-Fi configuration on NI-myRIO.
1.3.4 Physical Characteristics Determination Using Image Processing
The physical characteristics of soil can be estimated through simple image processing techniques by the estimation of fractal dimension. As depicted in Figure 1.6 image processing involves capturing the images of the soil and converting them into binary images, and then calculating the average value using the box-counting method. The computed fractal dimension is used to estimate the value of the physical characteristics of the soil.
1.3.4.1 Fractal Dimension Estimation
Fractal dimension is a mathematical descriptor of image features which characterizes the physical properties of the soil. The box-counting method is one of the methods to estimate fractal dimensions.
Figure 1.6 Process of physical parameter estimation of soil.
1.3.4.2 Box-Counting Method
The box-counting principle involves counting the number of ones covered by a specified box size in the binary image. It consists of the following steps:
Convert the color image to a binary image using color thresholding.
Extract the central 3x3 subarray
Count the number of 1’s in the extracted array
Estimate the fractal dimension using the Eqn. [3.1]
where, Box size (S=3), Number of one’s in that box.
1.3.4.3 Physical Parameters
The following physical characteristics of the soil are estimated to determine the quality of the soil content.
Water content: Water content or moisture content is the quantity of water contained in the soil and is estimated using Eqn. [3.2].
Liquid limit: The Atterberg limits are a basic measure of the critical water contents of fine-grained soil using Eqn. [
3
.
3
] the liquid limit of the soil is estimated.
Plastic limit: The plastic limit is defined as the moisture content at which soil begins to behave as a plastic material and is estimated using Eqn. [
3
.
4
].
Shrinkage limit: The shrinkage limit is the water content where the further loss of moisture will not result in any more volume reduction and is determined using the Eqn. [3.5].
Specific gravity: Specific gravity is defined as the ratio of the weight of an equal volume of distilled water at that temperature both weights taken in air and computed using the Eqn. [3.6].
Uniformity and curvature coefficient: The uniformity coefficient Cu is defined as the ratio of D60 by D10. So, when Cu is greater than 4–6, it is understood as a well-graded soil and when the Cu is less than 4, they are poorly graded or uniformly graded. Uniformly graded in the sense, that the soils have got the identical size of the particles and are determined using the Eqns. [3.7 and 3.8].
Field density: Field density or density of field is expressed in lines of force per unit area of a cross-section perpendicular to lines of force and is estimated using the Eqn. [3.9].
The formulae used for estimating physical characteristics are shown from Eqns. [3.2] to [3.9] where X stands for the fractal dimension of the image.
Thus, the system computes the features to determine the time to initiate the process of irrigation. This system reduces manual effort, increases productivity, and reduces water wastage.
1.4 Performance Studies
The results obtained from the performance evaluation of our proposed system are discussed below.
1.4.1 Experimental Environment
Figure 1.7 shows LabVIEW VI for interfacing the temperature sensor (lm35) and soil moisture sensor to NI myRIO. The analog output voltages from the sensors are converted to suitable temperature and moisture (in terms of percentage).
Figure 1.7 Block diagram for irrigation automation by monitoring soil temperature and moisture.
Figure 1.8 Front panel for irrigation automation.
The optimum threshold values of temperature and moisture are fixed. Any variation in soil temperature above threshold temperature (30° C) and drop in soil moisture content below 50% is detected and it turns on a motor that irrigates the field. The soil temperature, moisture levels and motor status have been configured as network-shared variables.
Figure 1.8 shows the front panel containing the output voltage wave-forms of the temperature and moisture sensors. The numeric display of temperature and moisture in terms of Celsius and percentage moisture is also shown across time intervals.
1.4.2 Accessing Shared Variables from NI Data Dashboard
By connecting to the Wi-Fi network configured on myRIO, the values of shared variables for four different scenarios are given as follows and the results are presented in Figures 1.9−1.12.
Figure 1.9 Shared variables (T>30°C)-Motor ON.
Figure 1.10 Shared variables (M<50%)-Motor ON.
Figure 1.11 Shared variables (T<30°C and M>50%)-Motor OFF.
Figure 1.12 Shared variables (T>30°C and M<50%)-Motor ON.
1.4.2.1 Scenario 1
Temperature above threshold (T>30°C)
Moisture(M) within optimum range (51–100)%
1.4.2.2 Scenario 2
Temperature below threshold (T<30
°
C)
Moisture(M) outside optimum range (0-50)% (motor on)
1.4.2.3 Scenario 3
Temperature and moisture within optimum range (T<30°C and M>50%)
1.4.2.4 Scenario 4
Temperature and moisture outside the optimum range (T>30°C and M<50%)
The various values of temperature, moisture of soil, and motor status are shown in Table 1.3. It is obvious that motor is on when threshold conditions are violated.
Table 1.3 Tabulation of results for irrigation automation.
S. no.
Temperature (°C)
Moisture (%)
Motor status
1
32.715
60.059
ON
2
28.9
39.355
ON
3
28.8
85.93
OFF
4
30.762
33.105
OFF
1.5 Image Processing to Determine Physical Characteristics
The images of different soil samples, collected from various fields are processed using LabVIEW. The alluvial and red soil samples that were considered for analysis to determine physical characteristics are shown in Figure 1.13 (a) and (b).
Figure 1.13 (a) Alluvial soil samples. (b) Red soil samples.
Figure 1.14 shows the LabVIEW VI for Image processing. As discussed, image processing involves reading the images of the soil and conversion to binary, and then applying the box-counting method to calculate the average value. The computed fractal dimension is used to estimate the value of the physical characteristics of the soil.
Figure 1.15 shows the results obtained on applying color thresholding to the image. The entire image has been converted into an array of pixels. The resized subarray shown is used for fractal dimension estimation by the box-counting method.
Figure 1.14 LabVIEW VI for image processing.
Figure 1.15 Front panel of color thresholding and subarray extraction.
In Figure 1.16, the results obtained for various physical parameters of the alluvial and red soil samples taken are displayed. The results show that the water content of the soil samples is relatively low.
Tables 1.4 and 1.5 list the physical parameters that have been estimated by image processing of alluvial and red soil samples. The readings shown were obtained by averaging the readings over three samples taken.
Figure 1.16 Front panel results for physical parameters of (i) alluvial soil and (ii) red soil.
Table 1.4 Physical characteristics of alluvial soil.
Soil tested: alluvial soil
S. no.
Physical characteristic
Value obtained (averaged over 3 samples)
1
Fractal Dimension (FD)
1.57
2
Water content (%w)
4.39
3
Liquid limit (%l)
66.79
4
Plastic limit (%p)
25.18
5
Shrinkage limit (%S)
16.08
6
Specific gravity (G)
3.06
7
Coefficient of curvature (CC)
1.02
8
Coefficient of uniformity (Cu)
7.34
9
Field density (fd)
1.014
Table 1.5 Physical characteristics of red soil.
Soil tested: red soil
S. no.
Physical characteristic
Value obtained (averaged over 3 samples)
1
Fractal Dimension (FD)
1.76
2
Water content (%w)
7.75
3
Liquid limit (%l)
76.52
4
Plastic limit (%p)
24.168
5
Shrinkage limit (%S)
18.686
6
Specific gravity (G)
2.62
7
Coefficient of curvature (CC)
0.675
8
Coefficient of uniformity (Cu)
7.507
9
Field density (fd)
1.443
1.6 Conclusion and Future Enhancements
1.6.1 Conclusion
From the results obtained, it is observed that the proposed system ensures enhanced productivity and safety, easier agriculture procedures, and simplified determination of physical characteristics as compared to conventional procedures. This research work focuses on developing a smart agriculture irrigation monitoring system for the Indian scenario.
1.6.2 Future Scope
The performance of the proposed system can further be tuned to accommodate fuzzy-based estimation of parameters to study the rapid changes in the climate. Also, the study can be extended to various regions of the Indian landscape considering the distinguishable challenges of these regions.
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Note
*
Corresponding author
:
2An Enhanced Integrated Image Mining Approach to Address Macro Nutritional Deficiency Problems Limiting Maize Yield
Sridevy S.1*, Anna Saro Vijendran2 and Ahmed A. Elngar3
1Computer Science, Department of Physical Science & Information Technology, Agricultural Engineering College & Research Institute, Tamil Nadu Agricultural University, Coimbatore, Tamil Nadu, India
2School of Computing, Sri Ramakrishna College of Arts and Science Nava India Road, Peelamedu, Coimbatore, Tamil Nadu, India
3Faculty of Computers and Artificial Intelligence, Beni-Suef University, Beni-Suef City, Egypt
Abstract
Agriculture has become much more of a means to feed ever-growing populations. Plants have become an indispensable energy source and are found to be an important solution to solve the problem of global warming. This chapter presents an integrated approach using image processing and mining to detect, quantify and classify plant diseases from maize leaf images in the visible spectrum. The proposed work can be able to determine single macro nutritional, multiple macro nutritional deficiencies determination, develop an expert decision support system, and analyzes spectral reflectance and RGB intensities of control which may then be useful to restrict the ineffective data to be processed in the design of suitable approaches. The experimental result is conducted on each stage and the result shows the more promising result on this proposed work while comparing the existing approaches.
Keywords: Agriculture, maize leaf, quantify, nutrient deficiency, spectral reflectance
2.1 Introduction
Traditional agricultural management practices assume parameters in crop fields to be homogeneous, thus the output of pesticides and managing actions is not in relation to the demands [15]. The occurrence of deficiencies in plants is turning out to be an alarming condition in today’s agricultural world. As plants become victims of unsettled climatic, environmental, and soil conditions, a nutritional imbalance is observed in these sensitive plants [16]. Precision agriculture integrating different modern technologies like sensors, information, and management systems aim to match agricultural input and practices to the spatial and temporal variability within a field. Thus, better use of resources and avoidance of great differences in yield quality and quantity due to small-scale site-specific differences can be attained. The aim of this study is to diagnose nutrient deficiency in maize with the help of variation in leaf color of the maize crop using Multivariate Image Analysis and Multivariate Image Regression Analysis. Further developing an expert system to give recommendations to the farmers based on the diagnosed nutritional deficiency.
2.2 Related Work
The contribution of several imaging techniques such as thermal imaging, fluorescence imaging, hyperspectral imaging, and photometric (RGB) feature-based imaging are notable. This section discusses existing work done on identifying nutritional deficiency in crops using image processing and image mining techniques.
Mutanga et al. [1] reported that phosphorus and potassium elements, which are responsible for both the photosynthetic process and the tissue composition of plants, affected reflection and absorption in the visible region of the spectrum. Bogrekci and Lee [2] reported that spatial variation in actual and predicted maps of phosphorus variability could be represented using diffuse reflectance spectroscopy in the UV, VIS, and NIR regions. Using the visible region of the spectrum i.e., red (500–600 nm) to green (600–700 nm) reflectance ratio Gamon and Surfus [3] suggested that prediction of anthocyanins content was possible. Horgan et al. [4] have shown that it is possible to identify a cultivar of carrot roots based on their color and shape using statistical analysis of digital images.
Du and Sun [5] using image segmentation evaluated several food quality assessment methods separating defects and infirmities in the food products. The efficiency of various segmentation algorithms applied for apple defects detection, pizza sauce separation, and detecting touching pistachio nuts was also studied. Puchalski et al. [6] developed an image processing system for detecting defects on the apple surfaces such as bruises, frost damage, and scabs from the combination of the images reported an overall accuracy of 96%. Besides, other work by Mizushima and Lu [7] proposed Otsu’s method for apple defects detection and support vector machine for apple grading and sorting.
Pastrana and Rath [8] contributed a novel approach to segmenting plantlets suffering from the problem of occlusion, testing with plants having 2, 3, and 4 leaves. This method, by ellipse approximation, solved leaf complexities and found leaf clusters using active shape models. Another study by Cope et al. [9] reviewed various computational, morphometric, and image processing methods analyzing images of plants measuring leaf outlines, flower shape, vein structures, and leaf textures proposed a robust automated species identification system that could instigate people in botanical training and working expertise. The efficiency of the system can be increased by having a small number of classes and a restricted set of features improvements
Kelman and Linker [10] proposed 3D convexity analysis for shape and color analysis of the mature apple detection in tree images especially the Golden Delicious apple variety orchard under natural light conditions which accounted for 94% correctness when the edges were identified using the Canny filter. Kamalak Kannan and Hemalatha [11] provided an expert system about agriculture that helps the farmer to cultivate the crops for high yield and gives awareness about organic farming.
Pawan et al. [12] instigated the preprocessing of the input image using histogram equalization applied to increase the contrast in low contrast image, K-means clustering algorithm is used for feature selection, and finally, classification is performed using Neural-network. Thus, the image processing technique is used for detecting diseases on cotton leaves early and accurately which will be useful to farmers. Multispectral full-waveform light detection and ranging (LiDAR) instrument prototype was developed by Zheng Niu et al. [13] with four wavelengths and a supercontinuum laser as a light source was calibrated to investigate the biochemical parameters and fine structure of vegetation.
The objectives of the study done by Brent et al. [21] are to find specific regions of the spectral reflectance response curves of a soybean canopy that show genotypic differences and to determine factors that influence the spectral reflectance response curves of soybean cultivars by the specified breeding process. Vasudev et al. [22] in their work introduced the software Nitrate App which has revolutionized the method to find nitrogen content in Maize leaves. The methodology incorporated was to turn the manual process into a software application using image processing. Initially, the image of the Maize leaf is captured and preprocessed to remove the noise of the source image to extract the color and texture characteristics of maize leaves by utilizing RGB and the HSV model.
Ali et al. [23] applied a new technique based upon a commercially available hand-held scanner which overcomes the problems. An algorithm was developed to determine the chlorophyll content, using a Logarithmic sigmoid transfer function that non-linearly maps the normalized value of G, with respect to R and B. In [24] Selma et al. developed a remote-sensing system consisting of a helium balloon with two small-format digital cameras for the generation of classifiers based on different combinations of spectral bands and vegetation indices from original, segmented, and reflectance images in order to determine the levels of leaf nitrogen and chlorophyll in the bean. Sridevi and Anna Saro Vijendran in their survey [25] provided new insight into the detection of the nutritional deficiency of plants and explored the need for agricultural input rationalization. Besides, by adjusting agricultural practices like fertilizer and pesticide application environmental damage can be reduced to the site that demands profit maximization.
2.3 Motivation
The motivation of this proposed work is to develop an enhanced integrated approach using image mining for determining a nutrient deficiency in the maize leaf. It is performed by adapting proper color space for input images, removing the presence of noise, implementing smart algorithms to reduce the computational cost, and increasing accuracy and reliability by implementing unsupervised learning techniques and analysis to reveal insights into the relationship between spectra and RGB intensities.
2.4 Framework of Enhanced Integrated Image Mining Approaches to Address Macro Nutritional Deficiency Problems Limiting Maize Yield
The proposed architecture of this research work is shown in Figure 2.1. The dataset used in this work is maize leaf images according to the recommendation given by AgriPortal of Tamilnadu Agricultural University, Coimbatore [14]. The images are preprocessed prior to the identification of the presence of deficiency. To determine the intensity and color variance near the edges of the objects the given input images are converted to HSV Color Transformation. Histogram equalization is applied to produce a gray map which increases the intensities that can be better visualized on the histogram. To perform better image analysis the important features are extracted using Independent Component Analysis (ICA). With the extracted Features four different phases are deployed in this research work. In the First and Second Phases Single Macro Nutrition Deficiency is determined using Multivariate [17] and Multivariate Partial Least Square Regression Analysis [18].
Figure 2.1 Proposed architecture.
The third phase concentrates on the identification of multiple macronutrients deficiency which is observed by modeling fuzzy K-means [19]. In the fourth phase, the expert system is developed for end users namely the farmers which provide immediate and instant information on the possible nutrition deficiency affecting the life of maize with the consideration of several known symptoms supplied [20]. The final phase proposed to identify effective spectra ranges and significant component images of RGB intensities which may then be useful to restrict the ineffective data to be processed in the design of suitable approaches.
2.5 Algorithm – Enhanced Integrated Image Mining Approaches to Address Macro Nutritional Deficiency Problems Limiting Maize Yield
The sequence of steps may be as follows:
Experimental Results
The experimental process of the proposed Enhanced Integrated Image Mining Approaches to Address Macro Nutritional deficiency problems has been deployed using MATLAB. Control and deficient leaf images are collected from experimental fields of Tamil Nadu Agricultural University, Coimbatore [13]. Totally 60 images are collected from open source. Out of these 30 images are considered for training purposes and 30 images for testing purposes (Figure 2.2).
Figure 2.2 Presence of deficiency determined using multivariate image analysis.
In this chapter multiple macronutrients deficiency of nitrogen, phosphorous, potassium, and InV are detected using fuzzy k means shown in Figure 2.3.
The proposed work exhibits Spectral reflectance and component image of RGB analysis reveal insights into relationship between spectra and RGB intensities as in Figures 2.4 and 2.5.
Differences in spectral reflectance of the leaves were highly correlated (r) to red, green, and blue intensity values of nitrogen and K-deficient leaves. In nitrogen-deficient leaves, the highest r value (0.63–0.68) was observed in the VIS portion (blue: 420–520 nm) for red, green, and blue intensity values. However, the control leaves had the highest r value (0.63–0.83) from 400 to 420 nm (blue) for red, green, and blue intensity values (Figure 2.4).
Figure 2.3 Multiple macronutrients deficiency identification in maize crop using Fuzzy K means.
Figure 2.4 Significance spectra range for nitrogen deficiency.
Figure 2.5 Significance spectra range for K deficiency.
For K deficient leaves, a significant negative relationship (R-value: -0.6 to -0.8) was observed in 500–720 nm (green and red) for Green intensity values; however, the Red and Blue intensity values were found to be non-significant for K deficient leaves (Figure 2.5). The control leaves had the highest r value (0.63–0.83) from 400–420 nm (blue) for Red, Green, and Blue intensity values (Figure 2.5).
2.6 Conclusion
The proposed work developed integrated novel techniques to address the nutritional deficiency problems limiting maize yield. This is one of the most essential problems to be solved in agriculture to feed hunger. This work put forth the pitfalls of the early research carried out over the last three decades and solved them by proposing innovative techniques for determining single and multiple micronutrient deficiencies and giving suggestions by developing an expert system. Also, the work is extended to model the relationship between spectral reflectance and RGB intensity values to suggest future research to be carried out in effective spectra ranges and significant component images of RGB intensities which may then be useful to restrict the ineffective data to be processed in the design of suitable approaches.
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