For the last decade, intensive and integrative studies have been performed by applying information technology to the research fields of agriculture and environmental sciences. This research trend aims to improve the agricultural process using information technology and at the same time making this process more sustainable. Applying sensor technology and combining information technologies creates the opportunity to obtain more precise information on agriculture processes and environmental phenomena. Indeed, the Internet of Things paradigm opens new frames to collect large amounts of agricultural and environmental data through different kinds of sensor networks [1].

The agricultural control starts with analyzing the current situation using chronological data. Traditionally, growers observe plants and detect problems based on their experience and symptoms of diseases. Most of such cases, the detection critical point is usually late in sense of optimization. An automatic method to detect symptoms and provide an early warning would at least help to improve the efficiency of agricultural processes. The correlations between environmental factors are the keys for evaluating agricultural characteristics. In this sense, a large amount of heterogeneous data is essential for integration studies of automated agriculture. There is a high demand for a large quantity of accurate spatiotemporal data for automated agriculture. Whilst the digitalization of the agriculture is helping to fulfill this demand, new challenges are posed related to the intelligent management of the data gathered from sensors to help farmers and environmental managers make decisions. It is in these challenges where the Intelligent Environment (IE) paradigm comes into play to guide the design of systems, techniques and algorithms able to analyze the data and provide recommendations that can be put into practice by farmers, managers and other stakeholders.

Some examples of applications of IE in this research field range from smart farming (see for example [2–4]) to intelligent applications for ecological disaster management (see for example [5] where a social sensor is developed for detecting floods through natural language processing (NLP)). Specific examples of IE applications in agriculture can be found for resource allocation and decision making in agricultural systems [6,7], farming system design [8] or data fusion for characterizing key agricultural system attributes [9]. Likewise, other IE proposals for environmental management can be found in different areas such as waste management [10] or environmental assessment and rehabilitation [11]. It is also noteworthy the increasing number of conferences and workshops devoted to the development of IEs in agriculture: approximately 30 events were found in the WikiCFP webpage in 2018 tagged with “agriculture” and “intelligent environments” as keywords, as for example the International Workshop on Intelligent Systems for Agriculture Production and Environment Protection (ISAPEP).

From our experience, we believe there is still a need for bridging the gap among the needs of farmers, environmental managers and stakeholders in the agriculture and environmental sectors and the solutions offered by the Information and Communication Technology field. The eventual goal of applying IE to agriculture is to replace, or significantly reduce, the human labor for the agricultural production. Most of contemporary research on Artificial Intelligence (AI) technology focuses on finding methods to simulate human perception, cognition and reasoning process. However, still a long way to reach the human ability remains. When the AI technology reaches maturity, the dramatic transformation on devices, equipment and machinery of agriculture production will begin. The motivation of editing this book was to foster the collaboration among researchers from computer science, agriculture engineering and environmental science fields who are in a privileged position to provide new and valuable insights on intelligent applications for agriculture and environmental management systems. As a result, this book presents several reviews and examples of the latest developments in the application of IEs to agriculture and environmental sciences through 5 chapters.

The editors of this book would like to thank the authors who contributed articles to this initiative. The diversity of the topics included in the chapters showcases the relevance in the research and development of Intelligent Environments applied to agriculture and environmental problems. We look forward to a future where IE will make a difference in creating efficient and really helpful systems for farmers, environmental managers and the entire society.

Outline of the book

Chapter 1 explores the use of intelligent systems in Controlled Environment Agriculture (CEA) facilities as an alternative to offer low cost sensing systems to gather data on how plants evolve in these facilities. At the moment, data collected in CEAs are restricted to recording the general environmental control responses. In this chapter the author relates his experiences within a farm company when developing more fine-grained methods to gather data, especially focused on those related to growing methods such as hydroponics or aeroponics and on intelligent techniques for measuring nutrients.

Chapter 2 reviews the adoption of intelligent systems in the research field of biomonitoring. In this chapter is described the evolution of environmental information systems and their application in biomonitoring, starting from the use of biosensors to the analysis of current environmental information systems to the introduction of intelligent techniques and methods in these systems. Several examples of applications of intelligent biomonitoring systems are analyzed, from systems to monitor algae to applications for monitoring fish. The authors also include a discussion on the limitations and future perspectives of intelligent biomonitoring systems.

Chapter 3 proposes an intelligent system to acquire and preprocess data for precision agriculture applications. It is composed of two modules, the first aimed to measure different environmental factors on the target plants and the second for refining time-series raw data previously collected in order to reduce noise in the data. The main goal of this system is to facilitate the acquisition of different type of data to farmers, since it allows any analog sensor to be connected with minimal effort of calibration. In this manner, the system is presented as a user-friendly device for multi-data collection which may help in the creation of big data sources for new applications.

Chapter 4 illustrates the use of intelligent algorithms to make a more efficient use of a scarce resource such as water. These algorithms predict the intake of pharmaceutical contaminant elements such as diclofenac in reclaimed water for irrigation of lettuces. As a result, this proposal allows farm managers to automatically control the quality of the reclaimed water and effectively use it when it is safe. The authors compare several machine learning algorithms to this end, proving that the Random Forest algorithm can reach a 97% of fitness when predicting the presence of contaminants in the water.

Chapter 5 focuses on the generation of intelligent models to predict frosts in crops in Southeastern Spain. These models not only use data from physical sensors placed in the land or trees, but they also rely on the utilization of open data from weather stations. By using different rule and decision trees techniques, the authors are able to identify the most relevant features for predicting frosts, such as dew point, vapor pressure deficit and maximum relative humidity. The results show that the proposed model is able to predict a frost with 98% of confidence and obtain an error of less than 0.5°C. in the prediction of the minimum temperature.

Andrés Muñoz (Andrés Muñoz would like to thank the Spanish Ministry of Economy and Competitiveness for its support under the project TIN2016-78799-P (AEI/FEDER, UE)) and Jaehwa Park

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