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2022 | Buch

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Information and Communication Technologies for Agriculture—Theme III: Decision

herausgegeben von: Dionysis D. Bochtis, Claus Grøn Sørensen, Spyros Fountas, Vasileios Moysiadis, Panos M. Pardalos

Verlag: Springer International Publishing

Buchreihe : Springer Optimization and Its Applications

Enthalten in: Springer Professional "Wirtschaft+Technik" , Springer Professional "Technik" , Springer Professional "Wirtschaft"

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Über dieses Buch

This volume is the third (III) of four under the main themes of Digitizing Agriculture and Information and Communication Technologies (ICT). The four volumes cover rapidly developing processes including Sensors (I), Data (II), Decision (III), and Actions (IV). Volumes are related to ‘digital transformation” within agricultural production and provision systems, and in the context of Smart Farming Technology and Knowledge-based Agriculture. Content spans broadly from data mining and visualization to big data analytics and decision making, alongside with the sustainability aspects stemming from the digital transformation of farming. The four volumes comprise the outcome of the 12th EFITA Congress, also incorporating chapters that originated from select presentations of the Congress.

The focus of this book (III) is on the transformation of collected information into valuable decisions and aims to shed light on how best to use digital technologies to reduce cost, inputs, and time, toward becoming more efficient and transparent. Fourteen chapters are grouped into 3 Sections. The first section of is dedicated to decisions in the value chain of agricultural products. The next section, titled Primary Production, elaborates on decision making for the improvement of processes taking place with the farm under the implementation of ICT. The last section is devoted to the development of innovative decision applications that also consider the protection of the environment, recognizing its importance in the preservation and considerate use of resources, as well as the mitigation of adverse impacts that are related to agricultural production.

Planning and modeling the assessment of agricultural practices can provide farmers with valuable information prior to the execution of any task. This book provides a valuable reference for them as well as for those directly involved with decision making in planning and assessment of agricultural production.

Specific advances covered in the volume:

Modelling and Simulation of ICT-based agricultural systemsFarm Management Information Systems (FMIS) Planning for unmanned aerial systems Agri-robotics awareness and planning Smart livestock farming Sustainable strategic planning in agri-production Food business information systems

Inhaltsverzeichnis

Frontmatter

Value Chain

Frontmatter

Agricultural Information Model

Abstract

One of the key challenges towards the realization of smart farming solutions is related to the lack of interoperability between different systems and platforms in the agri-food sector, especially the ones offered by different technology providers. In this respect, seamless exchange and integration of the data produced or collected by those systems is of major importance, which unfortunately is rarely supported. This is in principle due to the wide heterogeneity of data models and semantics used to represent data in the agri-food domain, as well as the lack of related standards to dominate this space and the lack of sufficient interoperability mechanisms that enable the connection of existing agri-food data models. This chapter presents the Agriculture Information Model (AIM) that has been developed by the H2020 DEMETER project, which aims to address the aforementioned issues. AIM has been designed following a layered and modular approach and is realized as a suite of ontologies implemented in line with best practices, reusing existing standards and well-scoped models as much as possible and establishing alignments between them to enable their interoperability and the integration of existing data. AIM is scalable and can easily be extended to address additional needs and incorporate new concepts, maintaining its consistency and compliance. The AIM specification includes a set of guidelines and examples for application developers and other users and is currently being validated in demanding large-scale pilots across various operational environments in the framework of the H2020 DEMETER project aiming to enable the provision of efficient interoperable solutions to farmers and other stakeholders in the agri-food value chain.

Raul Palma, Ioanna Roussaki, Till Döhmen, Rob Atkinson, Soumya Brahma, Christoph Lange, George Routis, Marcin Plociennik, Szymon Mueller

Development of a Framework for Implementing ΙoΤ-Α on the Beef Cattle Value Chain

Abstract

The implementation of the Internet of Things (IoT) paradigm on the beef cattle value chain can improve decision-making, reduce quality losses, improve animal welfare, and better fulfill customer demands. However, few frameworks consider the specific aspects of the agri-food value chains. Also, none of the available frameworks meets the requirements of these value chains. This chapter will thoroughly describe this value chain considering its main stages, stakeholders, processes, and the informational flow. In sequence, the requirements and services needed for implementing IoT are described, containing nine main services and 31 sub-services or activities. Then, a domain model based on the IoT-A framework is developed. The model comprises the main users, physical entities and services for the beef cattle domain, and their interaction and information exchange. The main components of this domain model are described, and important topics related to the implementation are discussed. The methodology used and the analysis conducted can be applied to other agricultural value chains. This work sets the ground requirements for the future development of a framework for Smart Beef Cattle Services. These can also be used for developing a more general Smart Livestock Farming framework. Farm management research may benefit from the resulting requirements, using it to build services and architectures for a panorama of further automation and autonomous operations of the farms and agricultural supply chains.

Gustavo Marques Mostaço, Roberto Fray Silva, Carlos Eduardo Cugnasca

Food Business Information Systems in Western Greece

Abstract

At the beginning of the twenty-first century, more and more businesses are trying to stand out in the competitive global market. They also try to improve their function and gain a competitive advantage by using information technology systems. Food businesses in Western Greece use information systems to improve the management of their functions. They apply such systems in human resources, in accounting and finance, in marketing and sales, in production, and in operational functions. Most enterprises use enterprise resource planning (ERP) systems while fewer use other software packages like human resource management (HRM), customer relationship management (CRM), software requirements specification (SRS), and material requirements planning (MRP). Moreover, a few businesses use tailor made packages mainly to manage production, human resources, and operational functions. Finally, there is a growth prospective in operational, human recourses, and production functions to apply software packages in the short-term future. The contribution of this survey to the existing literature is on the adoption of new information technologies by traditional food enterprises in Western Greece.

Vasileios Mitsos, Grigorios Beligiannis, Achilleas Kontogeorgos

Primary Production

Frontmatter

From Precision Agriculture to Agriculture 4.0: Integrating ICT in Farming

Abstract

The ever-increasing need for food worldwide, the scarcity of natural resources, and climate change call for drastic changes in conventional agricultural processes. Agriculture is called to adapt to the rapid evolution of technology by incorporating innovative technologies to the applied practices. To that end, Information and Communications Technologies (ICT), including sensors, robots, artificial intelligence, wireless sensor networks, and cloud computing constitute a family of technologies that can provide beneficial solutions that can contribute to the modernization of agricultural operations. The “big data,” produced by these technologies, are capable of helping towards this direction and facilitate the management of various fields of agricultural production. As a matter of fact, the next-generation technology standard, namely 5G, gives a plethora of new opportunities for farmers rendering itself a game changer for the ICT realization. This chapter focuses on prerequisites for the fourth agricultural revolution, namely Agriculture 4.0. It briefly describes the main Agricultural 4.0 components, as a means of giving an overview of this field in conjunction with the potential benefits in agriculture.

Lefteris Benos, Nikolaos Makaritis, Vasileios Kolorizos

On the Routing of Unmanned Aerial Vehicles (UAVs) in Precision Farming Sampling Missions

Abstract

This chapter focuses on a very important aspect of the utilization of unmanned aerial vehicles (simply mentioned as drones) in precision agriculture; the route planning of drones in (spot) sampling operations. In particular, a brief description of the main types of drones used in agriculture along with indicative applications is given based on the relative literature. Subsequently, the challenges that arise from on-field drones routing are discussed by highlighting the commonly adopted approach, namely the Travelling Salesman Problem (TSP). This combinatorial optimization problem is solved by employing algorithms, which can result in optimal or near-optimal solutions. Towards this direction, several algorithms are concisely described. Furthermore, representative demonstrations of drones routing are performed under different scenarios. These scenarios include three different agricultural fields comprising of 50, 83, and 100 visiting points. Several hypotheses are evaluated in silico, by considering the number of drones, the initial and final locations of each route, and various operational constraints. The most efficient results in terms of both distance covered and computation time are presented.

Georgios Dolias, Lefteris Benos, Dionysis Bochtis

3D Scenery Construction of Agricultural Environments for Robotics Awareness

Abstract

Depth cameras started to gain popularity in agricultural applications during the last years. This type of cameras has been implemented mainly for three-dimensional (3D) reconstruction of objects in indoor or outdoor sceneries. The use of such cameras in the construction of 3D models for simulation purposes of complex structures that are usually met in nature, such as trees and other plants, is a great challenge. Remarkably, agricultural environments are extremely complex. Thus, the proper setup and implementation of such technologies is particularly important in order to attain useable data. So far, the depth information collected using these cameras varies among different objects’ structure and sensing conditions due to the uncertainty of the outdoor environment. The use of a specific methodology using color and depth images gives the opportunity to extract geometrical characteristics information about point clouds of the targeted objects. This chapter explores the different technologies used by depth cameras and presents several applications concerning indoor and outdoor environments by presenting indicative scenarios for agricultural applications. Towards that direction, a 3D reconstruction of trees was established producing point clouds from Red Green Blue Depth (RGB-D) images acquired in real field conditions. The point cloud samples of trees were collected using an unmanned ground vehicle (UGV) and imported in Gazebo in order to visualize a simulation of the environment. This simulation technique can be used for testing and evaluating the navigation of robotic systems. By further analyzing the resulted 3D point clouds, various geometrical measurements of the simulated samples, such as the volume or the height of tree canopies, can be calculated. Possible weaknesses of this procedure are mainly attributed to the camera’s limitations and the sampling parameters. However, results show that it is possible to establish a suitable simulation environment to implement it in several agricultural applications by utilizing automated unmanned robotic platforms.

Aristotelis Christos Tagarakis, Damianos Kalaitzidis, Evangelia Filippou, Lefteris Benos, Dionysis Bochtis

A Weed Control Unmanned Ground Vehicle Prototype for Precision Farming Activities: The Case of Red Rice

Abstract

The implications of red rice on the total production of commercially cultivated rice are widely documented in the literature. Red rice, due to its genetic similarity with cultivated rice, is not affected by typical herbicides, and thus it is considered as a major weed challenge. Conventional and chemical-based solutions to address red rice are inefficient. In this research, a simulated and a real-world prototype robot system for weed control in paddy fields is developed, which consists of an Unmanned Ground Vehicle (UGV) that is equipped with a specially designed rod mechanism. The rod mechanism is coated with a porous absorbent material (e.g., sponge) that is saturated with herbicide and uses a sensor-based control mechanism for applying the herbicide only to the top of red rice plants thus avoiding the contact with the commercially cultivated rice plants. The rod dynamically reacts to the harsh terrain, via using a slope and a height control automation system, in order to retain the rod mechanism’s height at a certain level and horizontally aligned to the terrain so as to affect only the red rice plants. The method can be applied after the end of the growing season as red rice plants exceed in height the plants of the commercial rice. To that end, the impact of red rice on the cultivation of commercial rice varieties can be limited thus ensuring supply stability downstream the agri-food value network. The prototype robot system operates in a fast and accurate manner and delivers consistent results regardless of the geomorphology of the terrain.

Aristotelis Koulousis, Damianos Kalaitzidis, Dimitrios Bechtsis, Christos Yfoulis, Naoum Tsolakis, Dionysis Bochtis

Decision-Making and Decision Support System for a Successful Weed Management

Abstract

The introduction of Decision Support Systems (DSSs) in weed management poses an attractive option for creating improved and more environmentally friendly control strategies. The aim of the current study was to present key factors affecting decision-making process that need to be taken into account before developing a DSS in terms of weed management. First, attention should be paid to the effects of environmental factors and agronomic practices on weed emergence and the composition of the weed flora in an agricultural field. If weed emergence and timing of weed emergence could be predicted, then a DSS could make accurate suggestions for weed control. Secondly, to develop any weed management program, it is essential to have a deep understanding of weed biology and ecology. The biological traits of weeds, weed growth, the impact of weed competition during crucial growth stages for the crop should be estimated in order to optimize decision-making process. Moreover, a better understanding of seed production and weed seedbank dynamics into the soil would help experts develop DSSs able to provide management strategies also in the long-term period. However, these objectives are quite complex and need to be addressed in the near future. Furthermore, carrying out field surveys, hosting workshops, and group meetings in order to communicate with farmers and help them familiarize with the adoption of DSS methodologies. This is a vital step for persuading farmers to trust the use DSSs for the management of weeds in their fields. Further research and extended experimentation are needed in order to develop effective DSSs in terms of weed management under different soil and climatic conditions, always according to the special needs of each farmer.

P. Kanatas, I. Travlos, A. Tataridas, I. Gazoulis

Zephyrus: Grain Aeration Strategy Based on the Prediction of Temperature and Moisture Fronts

Abstract

Grain aeration is an established low-cost and chemical-free technology for maintaining favorable storage conditions for the safe preservation of grain quality. Currently, the most efficient controllers are based on simulations of the aeration process. They frequently depend on complex programming codes, which limit their implementation for professionals who work in the postharvest sector, resulting in longer computing times. In this work, a new aeration control strategy, called Zephyrus, was proposed based on the prediction of speeds and changes of temperature and moisture fronts while air is passed through a grain bulk. The proposed controller was tested in a pilot study, resulting in a grain cooling of 11.4 °C with a moisture content variation of 0.6%, also maintaining an average temperature gradient of 2.6 °C throughout the grain bulk. Along the 6 months of study, the energy required for cooling 0.42 t of grain was 0.06 kWh t⁻¹ °C⁻¹ (0.30 kWh t⁻¹). The proposed control strategy was also compared with two other controllers by using simulation procedures. Results showed that Zephyrus was more efficient to achieve grain cooling for 56% of the simulated scenarios. When considering the power consumption, Zephyrus required lower electrical energy per mass of cooled grain in 44.5% of the simulated scenarios. Zephyrus control strategy can be used with different aeration system designs, automatically adjusting its set points according to the geographic region and season.

D. C. Lopes, A. J. Steidle Neto

Decision-Making Applications on Smart Livestock Farming

Abstract

Smart livestock farming systems may provide real-time on-farm scenarios enabling fast interventions that benefit the current herd or flock. Smart decision-making technologies refer to more precise control over livestock production processes, helping farmers improve their productivity and profitability. Livestock process parameters are often faced with inaccurate, incomplete, or even conflicting data, and a way of minimizing this effect when processing data is the use of non-classical logic. The use of conceptual non-classical logic might improve smart tools allowing for non-intrusive assessment of health status and welfare, where information can be collected without the stress of disturbing or handling animals. Continuous monitoring can also offer a more complete picture of the overall health and/or well-being of animals rather than a view in time, as provided by traditional assessment. Alerting farmers to problems as they arise in real-time allows for immediate and targeted interventions to benefit the current herds or flocks. This book chapter introduces the fundamentals of managerial processes using non-classic logic and data mining and offers several applications to improve the decision-making of smart livestock farming.

Irenilza de Alencar Nääs, Jair Minoro Abe

Environment

Frontmatter

Programmable Process Structures of Unified Elements for Model-Based Planning and Operation of Complex Agri-environmental Processes

Abstract

Nowadays, complex agri-environmental systems are supported by new generations of sensors and data acquisition methodologies of Information Technology. However, the decisions about the integrated development of agriculture-related sustainable and resilient process networks require the consideration of the causally determined balances of the underlying complex interactions. Accordingly, the computer-assisted planning and operation of agri-environmental processes inspire the development of new, easily modifiable, extensible, and connectable modeling and simulation methodologies. In the past years, the method of Programmable Process Structures (PPS) has been implemented for studying of various complex agri-environmental processes. Here, PPS will be illustrated by case studies for a recirculation aquaculture system, for a complex food web-involved fishpond, and for an agroforestry system. The chapter aims to illustrate the role of dynamic modeling and simulation in support of planning and decisions at various levels, utilizing the same general framework of PPS.

Monika Varga, Gergo Gyalog, Janos Raso, Balazs Kucska, Bela Csukas

Monitoring and Estimation of Sugarcane Burning in the Middle Paranapanema Basin, Brazil, Using Linear Mixed Models

Abstract

Studies on sugarcane burning demonstrate that the use of fire in agriculture has been condemned for centuries by soil conservation manuals since it increases the temperature and decreases the natural moisture of the soil, leading to greater compaction, loss of porosity, erosion, and consequently soil infertility.

The objective of research was to evaluate the spatial and temporal distribution of fire incidences in the period from 2000 to 2018 in the Water Resources Management Unit of the Middle Paranapanema, located in the state of São Paulo—Brazil, and to carry out the future estimate of this activity through mixed linear models. For this purpose, images from the Landsat 5/TM (year 2000), 7/TM (years 2006 and 2012), and 8/OLI (year 2018) satellites and 2018 were used. Numerical data (regarding area and fire incidences) and categorical data (terrain slope) were also employed. Statistical model was used to evaluate data and was possible to identify a decrease in fires in smooth undulating terrains, corresponding to 99.9% per year, and characterized by the increase in agricultural machinery in these areas. In these lands, the model made it possible to carry out the forecast for the next 6 years, in which timeframe, considering causes/effects, there would be a decrease over 100%. On the other hand, in strong undulating terrain there was an increase of 2.07% per year, which in the next 6 years represents an increase of 12.45%, a result contrary to what the established laws provide.

Jéssica Alves da Silva, Edinéia Aparecida dos Santos Galvanin, Daniela Fernanda da Silva Fuzzo

A Decision Support System for Green Crop Fertilization Planning

Abstract

Energy consumption in primary food production systems that are intended to be used in food industry is very high because of the increased use of machinery equipment. The calculation of the energy inputs in such systems is complicated due to the number and quality of the data background that are required, which are not always available. Energy reduction to produce 1 kg of any agri-food product is very significant in terms of environmental protection and natural resources management but also in better promotion of the product under a “green” label. In this work, a decision support system is presented in terms of the energy assessment of crop fertilization operation. For the demonstration of the system, two crops were selected, namely industrial tomato and Arundo donax. The energy inputs of fertilization that were extracted were based on farmers’ data and other scientific data. According to the results, the total annual energy consumption for the total field area (about 28 ha) of the presented crops was up to 227.50 GJ for Arundo donax while in terms of tomato was up to 468.71 GJ. This high distribution in energy consumption shows the significant necessity in better field operations’ process management and use of agricultural machinery for optimization of the total energy cost of the system.

Efthymios Rodias, Eleftherios Evangelou, Maria Lampridi, Dionysis Bochtis

Knowledge Elicitation and Modeling of Agroecological Management Strategies

Abstract

Agroecology applies ecological principles to the design and management of agricultural systems to improve environmental outcomes and livelihoods for farmers. However, little research to date has focused on cognitive tools that can facilitate the exploration, design, and increased adoption of agroecological management practices. This chapter is a preliminary attempt to develop guidelines to describe and bring to light the management behavior of farmers engaged in a participatory project of agroecological system design. Management strategies are explicitly defined using key decision-relevant concepts of activity, resource, goal, plan, and preference. These declarative structures make it possible to perform simulation-based experimentation of operational decision-making at farm scale. The modeling framework facilitates the collective development and analysis of new management strategies in the face of knowledge gaps about the likely results of these strategies (especially highly innovative ones) and uncertainties about uncontrollable factors (weather in particular). Used in participatory workshops the presented approach supports learning, sharing, and dissemination of agroecological knowledge.

Roger Martin-Clouaire

Titel: Information and Communication Technologies for Agriculture—Theme III: Decision
herausgegeben von: Dionysis D. Bochtis
Claus Grøn Sørensen
Spyros Fountas
Vasileios Moysiadis
Panos M. Pardalos
Verlag: Springer International Publishing
Electronic ISBN: 978-3-030-84152-2
Print ISBN: 978-3-030-84151-5
DOI: https://doi.org/10.1007/978-3-030-84152-2