Maldonado-Guzmán, G., Garza-Reyes, J.A. and Solano-Romo, L.I. (2019), "Prelims", Intelligent Agriculture, Emerald Publishing Limited, Bingley, pp. i-xxiv. https://doi.org/10.1108/978-1-78973-843-820191006
Emerald Publishing Limited
Copyright © 2019 Emerald Publishing Limited
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Developing a System for Monitoring and Controlling Production
Universidad Autónoma de Aguascalientes, Mexico
JOSE ARTURO GARZA-REYES
University of Derby, UK
LIZETH ITZIGUERY SOLANO-ROMO
Universidad Autónoma de Aguascalientes, Mexico
United Kingdom – North America – Japan – India – Malaysia – China
Emerald Publishing Limited
Howard House, Wagon Lane, Bingley BD16 1WA, UK
First edition 2019
Copyright © 2019 Emerald Publishing Limited
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British Library Cataloguing in Publication Data
A catalogue record for this book is available from the British Library
ISBN: 978-1-78973-846-9 (Print)
ISBN: 978-1-78973-843-8 (Online)
ISBN: 978-1-78973-845-2 (Epub)
We dedicate this book project to our families. Their love and constant and unconditional support have been an invaluable source of strength and inspiration to complete this project.
Jose Arturo Garza-Reyes
Lizeth Itziguery Solano-Romo
List of Figures
|Figure 1.||Structure of WSN.||54|
|Figure 2.||Overall System Architecture of WSN.||56|
|Figure 3.||Structure of the Sink Node.||66|
|Figure 4.||Block Diagram of a Sink Node.||68|
|Figure 5.||IEEE 802.15.4 Stack.||79|
|Figure 6.||Business Use Case.||86|
|Figure 7.||Prediction Crop Use Case.||87|
|Figure 8.||Display Historic Data Use Case.||87|
|Figure 9.||Manage Sensor Use Case.||88|
|Figure 10.||Filling Database Use Case.||88|
|Figure 11.||Employee Administration Use Case.||89|
|Figure 12.||Prediction Menu Sequence.||89|
|Figure 13.||Crop Menu Sequence.||90|
|Figure 14.||Manage Sensor Sequence.||91|
|Figure 15.||Filling Database Sequence.||91|
|Figure 16.||Employee Management Sequence.||92|
|Figure 17.||Entity-Relationship (ER) Database.||100|
|Figure 18.||Administrator/User Access.||101|
|Figure 19.||Administrator Menu.||102|
|Figure 20.||Predictive Menu.||102|
|Figure 21.||Formula Menu Button.||103|
|Figure 22.||Maximum Rainfall Bar Graph.||103|
|Figure 23.||Rainfall Probability Dispersion Graph for the Next Seven Days.||104|
|Figure 24.||Rainfall Probability Pie Graph for the Next Seven Days.||104|
|Figure 25.||Cosecha (Crop) Menu Option.||105|
|Figure 26.||Sensor Menu Option.||105|
|Figure 27.||Employee Button Menu.||106|
|Figure 28.||“Agregar Personal Nuevo” Button.||106|
|Figure 29.||“Editar Personal” Button.||107|
|Figure 30.||“Eliminar Personal” Button.||107|
|Figure 31.||“Ayuda” Button (Help).||108|
|Figure 32.||Normal User Menu.||109|
|Figure 33.||Smart Farming Zigbee Cluster Tree Network.||118|
|Figure 34.||Example of Comma-separated Value.||119|
|Figure 35.||Flowchart for HMP-60 Sensor Communication with Router.||122|
|Figure 36.||Pin Assignments, Configuration Values, and Configuration Bits of HMP-60 Sensor.||124|
|Figure 37.||Main Code of HMP-60 Sensor.||125|
|Figure 38.||Raspberry Pi Software.||126|
|Figure 39.||Raspberry Pi, HMP-60 Received.||127|
|Figure 40.||Graphical Representation of HMP-60 Testing.||128|
|Figure 41.||Flowchart of Software for 5TE Sensor Probe.||130|
|Figure 42.||Header Files, Configuration Values, and Configuration Bits of 5TE Sensor.||131|
|Figure 43.||Main Code for 5TE Soil Sensor (A).||132|
|Figure 44.||Main Code for 5TE Soil Sensor (B).||133|
|Figure 45.||Sensor Measurement of Dry Plant.||134|
|Figure 46.||Sensor Measurement of Watered Plant.||135|
|Figure 47.||Raspberry Pi, 5TE Sensor Data Received.||136|
|Figure 48.||Graphical Representation of 5TE Sensor Data Received.||136|
|Figure 49.||Flowchart of SPI-212 Global Radiation Sensor Arduino Software.||138|
|Figure 50.||Configuration Bits of SPI-212 Sensor Software on Arduino.||139|
|Figure 51.||Main Code of SPI-212 Sensor Software on Arduino.||139|
|Figure 52.||Raspberry PI, SPI-212 Sensor Data Received.||140|
|Figure 53.||Graphic Representation of SPI-212 Sensor Data Received.||141|
|Figure 54.||Small Scale of Network.||142|
|Figure 55.||Example of Simple Bridge Connection.||142|
|Figure 56.||Software on the Coordinator.||144|
|Figure 57.||Software on the Bridges.||145|
|Figure 58.||Data Send by 5TE Soil Sensor.||147|
|Figure 59.||Data Send from SPI-212 Global Radiation Sensor.||148|
|Figure 60.||Data Send from HMP-60 Outdoor Temperature and Humidity Sensor.||149|
|Figure 61.||Data Collected on Coordinator.||150|
|Figure 62.||Graph of Outdoor Temperature vs Time.||151|
|Figure 63.||Graph Representing Outdoor Humidity vs Time.||152|
|Figure 64.||Graph Representing Global Radiation.||153|
|Figure 65.||Graph Representing Soil Dielectric Constant vs Time.||154|
|Figure 66.||Graph Representing Soil Moisture vs Time.||154|
|Figure 67.||Graph Representing Soil Temperature vs Time.||155|
|Figure 68.||Graph Representing Temperature and Humidity vs Time.||156|
|Figure 69.||Software for Setting Time and Data on Real-time Clock.||157|
|Figure 70.||Software for DTH-22 Temperature and Humidity Sensor.||158|
|Figure 71.||Technology Readiness Levels.||159|
|Figure 72.||Smart Farming Wireless Sensor Network.||165|
|Figure 73.||Smart Farming Wireless Sensor Network Communication.||166|
|Figure 74.||Arduino Mega 2560 Serial Monitor-A.||167|
|Figure 75.||Graphical Representation in Raspberry Pi (Python Shell).||168|
|Figure 76.||Sensor Reading from the Sensor Node Plotted Graphically.||168|
List of Tables
|Table 1.||Companies that Offer Agrotechnology Services.||14|
|Table 2.||The Most Important Sowings in Mexico.||43|
|Table 3.||Broccoli Producers in Mexico.||44|
|Table 4.||Technical Specification of Atmospheric Sensors.||63|
|Table 5.||Technical Specification of Soil Sensors.||64|
|Table 6.||Battery Power Consumption Calculation.||71|
|Table 7.||Arduino Runtime from V44 Batteries.||72|
|Table 8.||Technical Specification of Solar Panel and V44 USB Battery.||72|
|Table 9.||Hardware Components.||73|
|Table 10.||Raspberry Pi3 Model B Technical Specification.||75|
|Table 11.||Arduino Mega 2560 Technical Specification.||78|
|Table 12.||Actor Description/Functions.||85|
|Table 13.||Prediction User Case Description.||93|
|Table 14.||Crop User Case Description.||94|
|Table 15.||Manage Sensor User Case Description.||95|
|Table 16.||Filling DB User Case Description.||96|
|Table 17.||Employee Administration User Case Description.||97|
|Table 18.||Smart Farming Measurement Frequency.||119|
|Table 19.||Connections between HMP-60 Sensor and Arduino Mega 2560.||121|
|Table 20.||Description of Devices Used.||146|
About the Authors
Gonzalo Maldonado-Guzmán is a Professor at the Universidad Autónoma de Aguascalientes, Director of the Small and Medium Enterprises Observatory, and Director of the Research and Postgraduate Studies Department. His areas of research include marketing, corporative social responsibility, innovation and knowledge management, and IT and intellectual property in small and medium size enterprises (SMEs). He has coordinated projects in the Aguascalientes state, Mexico, in innovation and organizational culture in micro and SMEs. He has international projects with Universities of Murcia, Cantabria and Cartagena, in Spain.
Jose Arturo Garza-Reyes is a Professor of Operations Management and Head of the Centre for Supply Chain Improvement at the University of Derby, United Kingdom. He is actively involved in industrial projects where he combines his knowledge, expertise, and industrial experience in operations management to help organizations achieve excellence in their internal functions and supply chains. He has also led and managed international research projects funded by the European Union, British Academy, British Council, and Mexico’s National Council of Science and Technology (CONACYT). As a leading academic, he has published over 100 articles in leading scientific journals, participated in international conferences, and has four books in the areas of operations management and innovation, manufacturing performance measurement, and quality management systems. Professor Garza-Reyes is Associate Editor of the International Journal of Operations and Production Management and Journal of Manufacturing Technology Management as well as the Editor of the International Journal of Supply Chain and Operations Resilience and Editor-in-Chief of the International Journal of Industrial Engineering and Operations Management. The areas of expertise and interest for Professor Garza-Reyes include general aspects of operations and manufacturing management, business excellence, quality improvement, and performance measurement.
Lizeth Itziguery Solano-Romo is a Professor at the Universidad Autónoma de Aguascalientes. Her areas of research include information technology management, IT use and adoption, and digital marketing in SMEs. She has participated in the Aguascalientes state, Mexico, in the implementation of the new criminal justice system. She has international project participation to reduce the IT gap between public and private universities (ALFA-EU) with Universities of Finland, Romania, Brazil, Ecuador, and Colombia.
Agriculture is today one of the fields of knowledge least analyzed and discussed by various researchers, academics, and professionals not only in the field of agriculture but also in different areas of knowledge, although it is an elementary construct for the existence of humanity itself (Ding et al., 2018). Also, currently, the total world population amounts to a little more than seven billion people, and according to the estimates that have been made by the main international organizations, it is expected that by the year 2050, it will generate a substantial population growth of a little more than 2.5 billion people, which will be located primarily in the main urban cities, which will mean that a little more than 90% of the total world population will be concentrated practically in two continents: Asia and Africa (Lloyd, 2017).
However, world food production is totally limited, especially in Africa, and the serious problem of food shortages worldwide has not yet been resolved (Sánchez, 2002). In addition, the Asian continent has serious problems of shortage of drinking water (Pomeranz, 2009), even though 72% of the total surface of the earth is covered by water, and it is estimated that there are a little more than 1.45 billion cubic kilometers of water. Despite the existence of an extensive territorial extension covered by water, a little less than 1% of the total water on the planet is fresh water that is used not only for human consumption but also for agricultural irrigation, which represents a little more than 13 billion hectares; however, only 22% of that land is potentially arable (Lal, 1990).
In this context, there are currently diverse countries that apply traditional agriculture methods that have a high consumption of potable water, are intensive in labor, use fungicides and pesticides that are highly polluting, and are low in productive efficiency (Ding et al., 2018). Therefore, considering the significant increase in the world’s population, the severe shortage of drinking water, the existing limitation of resources, and the low level of efficiency of agricultural productivity, among other factors, it is indispensable and urgent that researchers, academics, and professionals from all areas of scientific knowledge guide their studies in the analysis and discussion, not only of the efficiency of a regulated agriculture but also in the development of agrotechnology that propitiates an Intelligent Agriculture, because this will allow an adequate utilization of the available resources.
In this sense, even when the systems of Smart Agriculture are too complex, multivariate, and unpredictable (Kamilaris, 2018), it is also possible to incorporate classic technological controls, such as integral processes or differentiated integral processes (Christofides, 2013; Afram and Janabi-Sharifi, 2014), which are not only easy to implement but also to control the movement processes they generate, thereby allowing an adjustment in the control of energy and the time of consumption (Wang, 2001). In addition, the use of intelligent methods such as the control of fuzzy logic, linear regression, and artificial neural networks involves not only deterministic mathematical models but also generalized mathematical models and mixed models, which allow the development of predictive models of agricultural production more accurately (Afram and Janabi-Sharifi, 2014).
Likewise, the use of these mathematical methods require a high level of reasoning and understanding and are generally based on the use of historical data on agricultural or agroindustrial production, or on the generation of expert or high-level knowledge (Ding et al., 2018). Therefore, the performance of the mathematical models of control and prediction of agricultural production is superior to that of the classic models of production control, and they are generally simpler to implement when using intelligent algorithms through computers. Thus, the mathematical models of production control and prediction have a high reliability and accuracy of the levels of agricultural and agroindustrial production, in addition to significantly reducing the use of drinking water, electricity, and emission of CO2 (Ding et al., 2018).
Similarly, control and prediction models of agricultural or agroindustrial production generally refer to the use of advanced algorithms through computers that are used to explain and develop predictive models of future growth that plants will have, or the growth that is estimated to have food production (Qin and Badgwell, 2003). Therefore, this type of control and prediction models work with a series of inputs that are controlled by the computers during a certain period of time, and they take the data usually from a selected sample of a dataset that reveals agricultural or agroindustrial production; however, only some of these models are implemented in the production prediction process (Bumroongsri and Kheawhom, 2014) because they generate the smallest possible error in the prediction of food production.
In addition, the use of advanced algorithms in the models of control and prediction of agricultural production is often done through three steps: prediction models, optimization in its implementation, and adjustment in the feedback (Zhang, 2017), with these three steps being equally important for the development of agricultural control and prediction models. Production control and prediction models were developed at the beginning of the 1960s, and these types of models were used almost exclusively in the process of predicting industrial production (Garriga and Soroush, 2010); however, its use has expanded to all areas of scientific knowledge, and its use has been considered important and paramount in all production prediction processes, including, of course, agricultural and agroindustrial production.
Additionally, most of the production control and prediction models require a series of constraints, predictive information, and linear and nonlinear dynamics for their application (Ding et al., 2018). Linear models of control and production prediction are usually used to solve quadratic problems of online programming, and nonlinear production control and prediction models are generally used to control systems with nonlinear dynamics, for which undoubtedly greater mathematical calculations than linear models (Vukov, 2015) are required. In addition, matrices of control dynamics and controlled algorithm models, which are commonly based on linear quadratic mathematical models that are relatively easy to use, have recently been incorporated into the theory of production control and prediction models.
Within the models of controlled algorithms are the models of internal control, which are widely used by researchers, academics, and professionals in the field of computer science and mathematics, and which can be defined as a simple entry and/or exit of information through a discrete time series system (García and Morari, 1982). Therefore, it is possible to affirm that the internal control models are nothing more than a combination of a dynamic control matrix and a model of control algorithms, but theoretically it is better; and the internal control model is more complete than the two previous models, and usually the internal control model tends to solve the problems of control and production prediction more robustly and with a much smaller error, which makes the model more efficient and effective.
Therefore, given that industrial processes are increasingly complex, involve an increasing number of interfaces, and are strongly non-linear, it is essential that new production control and prediction models are adapted and implemented in the companies of all sizes and sectors, as is the case of internal control models, which are more robust and have the minimum possible error in their application (Ding et al., 2018). However, the time to perform the calculations for the internal control models should be relatively long and totally efficient, to aspire to obtain robust results and with a minimum error, for which researchers and academics have considered necessary that this type of models be stabilized (Ding et al., 2018), that is, that they adapt to the production processes of the companies where they will be applied (Zhang, 2017).
We thank the British Council for having financially funded the international research project entitled Developing Food Security and Water Conservation for Economic Growth in Mexico – A Smart Monitoring and Control System (SMCS) Agro-Technology for Sustainable and Efficient Farming Operations (No. 275317449), from which this work is derived. The project was funded through the Newton Fund and the Institutional Links scheme of the British Council, and it was carried out through an international collaboration between the University of Derby (UK) and the Universidad Autόnoma de Aguascalientes (Mexico).
We would like to thank our institutions, the University of Derby (UK) and the Universidad Autόnoma de Aguascalientes (Mexico), for their unconditional support to complete the research project and production of this book. Also, we would like to thank our publisher “Emerald Publishing Limited” and its editorial team for assisting us with this publication. Finally, we would like to express our deepest gratitude to our following colleagues who also made a significant contribution to the research project and this work:
Dr Jose Manuel Andrade, Senior Lecturer in Electrical And Electronic Engineering, University of Derby, UK.
Gisha Gangadharan, Research Assistant Engineer in Electrical and Electronic Engineering University of Derby, UK.
Christopher Horry, Student Research Assistant in Electrical and Electronic Engineering, University of Derby, UK.
Ruben Michael Rodríguez-González, Student Research Assistant in MBA, Universidad Autónoma de Aguascalientes, Mexico.