The Internet of Things (IoT) has opened productive ways to cultivate soil with the use of low-cost hardware (sensors/actuators) and communication (Internet) technologies. Remote equipment and crop monitoring, predictive analytic, weather forecasting for crops or smart logistics and warehousing are some examples of these new opportunities. Nevertheless, farmers are agriculture experts but, usually, do not have experience in IoT applications. Users who use IoT applications must participate in its design, improving the integration and use. In this work, different industrial agricultural facilities are analysed with farmers and growers to design new functionalities based on IoT paradigms deployment. User-centred design model is used to obtain knowledge and experience in the process of introducing technology in agricultural applications. Internet of things paradigms are used as resources to facilitate the decision making. IoT architecture, operating rules and smart processes are implemented using a distributed model based on edge and fog computing paradigms. A communication architecture is proposed using these technologies. The aim is to help farmers to develop smart systems both, in current and new facilities. Different decision trees to automate the installation, designed by the farmer, can be easily deployed using the method proposed in this document.

/pdf/precision-agriculture-design-method-using-a-distributed-xwhe9hw86d.pdf

Precision Agriculture Design Method Using a Distributed Computing Architecture on Internet of Things Context.

An Internet of Things-based health prescription assistant and its security system design

Digital transformation of manufacturing through cloud services and resource virtualization

Freshwater is essential for irrigation and the supply of nutrients for plant growth, in order to compensate for the inadequacies of rainfall. Agricultural activities utilize around 70% of the available freshwater. This underscores the importance of responsible management, using smart agricultural water technologies. The focus of this paper is to investigate research regarding the integration of different machine learning models that can provide optimal irrigation decision management. This article reviews the research trend and applicability of machine learning techniques, as well as the deployment of developed machine learning models for use by farmers toward sustainable irrigation management. It further discusses how digital farming solutions, such as mobile and web frameworks, can enable the management of smart irrigation processes, with the aim of reducing the stress faced by farmers and researchers due to the opportunity for remote monitoring and control. The challenges, as well as the future direction of research, are also discussed.

/pdf/precision-irrigation-management-using-machine-learning-and-upkfj22m.pdf

Precision Irrigation Management Using Machine Learning and Digital Farming Solutions

The Internet of Things (IoT) technology has the means to shape the future of many industries. Data is the language of the communication between different nodes through the network; the networks are the communication channel. The cloud is the home and destination of the data which adds intelligence through data analytics software, Precision agriculture uses the IoT features to help in managing crops production, by optimizing the quality of the crops through applying required nutrients and reduce the harmful impacts on the environment due to the application of excess pesticides. In this paper, we deployed a sensing network to gather the field data of some crops (Potatoes, Tomatoes, etc.), then fed these data to a machine learning algorithm to get a warning message finally displaying both the data and the warning message through a Graphical User Interface (GUI).

Smart IoT Monitoring System for Agriculture with Predictive Analysis

Design and implementation of an IoT gateway to create smart environments

This paper introduces a new way of managing water in irrigation systems, which can be applied to gardens or agricultural fields, replacing human intervention with Wireless Sensor Networks. A typical irrigation system wastes on average 30% of the water used, due to poor management and configuration. This sustainable irrigation system allows a better efficiency in the process of irrigation that can lead to savings for the end user, not only monetary but also in natural resources, such as water and energy, leading to a more sustainable environment. The system can retrieve real time data and use them to determinate the correct amount of water to be used in a garden. With this solution, it is possible to save up to 34% of water when using sensor data from temperature, humidity and soil moisture, or up to 26% when using only temperature inputs. Besides a detailed system architecture, this paper includes a real case scenario implementation and results discussion.

Water Management for Sustainable Irrigation Systems Using Internet-of-Things.

Internet of Things (IoT) uses the connection between devices to improve their efficiency and user experience, being the communication one of the main elements for a proper IoT network. This paper presents a review of the most common wired and wireless communication protocols, discusses their characteristics, advantages and disadvantages as well as a comparison study to choose the best bidirectional sensor network composed by low power devices such as Arduino, ESP-12 and Raspberry Pi.

Comparison of communication protocols for low cost Internet of Things devices

Water is a crucial natural resource, and it is widely mishandled, with an estimated one third of world water utilities having loss of water of around 40% due to leakage. This paper presents a proposal for a system based on a wireless sensor network designed to monitor water distribution systems, such as irrigation systems, which, with the help of an autonomous learning algorithm, allows for precise location of water leaks. The complete system architecture is detailed, including hardware, communication, and data analysis. A study to discover the best machine learning algorithm between random forest, decision trees, neural networks, and Support Vector Machine (SVM) to fit leak detection is presented, including the methodology, training, and validation as well as the obtained results. Finally, the developed system is validated in a real-case implementation that shows that it is able to detect leaks with a 75% accuracy.

Precise Water Leak Detection Using Machine Learning and Real-Time Sensor Data

Presently, saving natural resources is increasingly a concern, and water scarcity is a fact that has been occurring in more areas of the globe. One of the main strategies used to counter this trend is the use of new technologies. On this topic, the Internet of Things has been highlighted, with these solutions being characterized by offering robustness and simplicity, while being low cost. This paper presents the study and development of an automatic irrigation control system for agricultural fields. The developed solution had a wireless sensors and actuators network, a mobile application that offers the user the capability of consulting not only the data collected in real time but also their history and also act in accordance with the data it analyses. To adapt the water management, Machine Learning algorithms were studied to predict the best time of day for water administration. Of the studied algorithms (Decision Trees, Random Forest, Neural Networks, and Support Vectors Machines) the one that obtained the best results was Random Forest, presenting an accuracy of 84.6%. Besides the ML solution, a method was also developed to calculate the amount of water needed to manage the fields under analysis. Through the implementation of the system it was possible to realize that the developed solution is effective and can achieve up to 60% of water savings.

Andre Gloria

Papers

Design and implementation of an IoT gateway to create smart environments

Water Management for Sustainable Irrigation Systems Using Internet-of-Things.

Comparison of communication protocols for low cost Internet of Things devices

Precise Water Leak Detection Using Machine Learning and Real-Time Sensor Data

Sustainable Irrigation System for Farming Supported by Machine Learning and Real-Time Sensor Data.