The 11th International Conference on Emerging Ubiquitous Systems and Pervasive Networks (EUSPN 2020)

 Ensuring food security has become a challenge in Sub-Saharan Africa (SSA) due to combined effects of climate change, high population growth, and relying on rainfed farming. Governments are establishing shared irrigation infrastructure for smallholder farmers as part of the solutions for food security. However, the irrigated farms often failed to achieve the expected crop yield. This is partly due to lack of water management system in the irrigation infrastructure. In this work, IoT-based irrigation management system is proposed after investigating problems of irrigated farmlands in three SSA countries, Ethiopia, Kenya, and South Africa as case studies. Resource-efficient IoT architecture is developed that monitors soil, microclimate and water parameters and performs appropriate irrigation management. Indigenous farming and expert knowledge, regional weather information, crop and soil specific characteristics are also provided to the system for informed-decision making and efficient operation of the irrigation management system. In SSA, broadband connectivity and cloud services are either unavailable or expensive. To tackle these limitations, data processing, network management and irrigation decisions and communication to the farmers are carried out locally, without the involvement of any back-end servers. Furthermore, the use of green energy sources and resource-aware intelligent data analysis algorithm is studied. The intelligent data analysis helps to discover new knowledge that support further development of agricultural expert knowledge. The proposed IoT-based irrigation management system is expected to contribute towards long term and sustainable high crop yield with minimum resource consumption and impact to the biodiversity around the case farmlands.</p

IoT Based Machine Learning Weather Monitoring and Prediction Using WSN

A novel approach to analysis and prediction is provided by the internet of things-based time monitoring and prediction system using wireless sensor networks (WSN) and machine learning techniques (ML). To give accurate meteorological data in real time, the integrated system uses IoT, WSN, and ML. Making informed decisions requires these insights. Includes strategically positioned infrared points that are used to gather meteorological information, such as temperature, humidity, pressure, and wind speed, among other things.The machine's automatic data processing methods are then used in a central processing unit to collect and analyse the data. By seeing patterns and drawing diagrams utilising previously collected data, ML models are able to comprehend intricate temporal dynamics. An important development in this system is its predictive capabilities. Artificial intelligence has the processing power to precisely forecast short-term weather patterns, enabling the rapid transmission of warnings for extreme localised events and the reduction of potential dangers.The combination of historical data, real-time sensor inputs, and automated analysis produces the predictive potential. The "Internet of Things" architecture used to develop this system makes it simpler to gather meteorological data. A number of industries, including as agriculture, transportation, emergency management, and event planning, are encouraged to make data-based decisions since users can quickly obtain current meteorological conditions and forecasts through user-friendly web interfaces or mobile applications

INTERNET OF THINGS IN SMART AGRICULTURE: APPLICATIONS AND OPEN CHALLENGES

Purpose of Study:&nbsp;The IoT is an emerging field nowadays and that can be used anywhere in automation, agriculture, controlling as well as monitoring of any object, which exists in the real world. We have to make use of IoT in Agriculture to increase productivity. Agro-industry processes could be more efficient by using IoT. It gives automation to agro-industry by reducing human intervention. In the current scenario, the sometime farmer doesn’t know the current status of the soil moisture and other things related to their land and don’t produce productive results towards crops. The purpose of this research study is to explore the usage of IoT devices and application areas that are being used in agriculture.&nbsp; Methodology:&nbsp;The methodology behind this study is to identify trends and review the open challenges, application areas and architectures for IoT in agro-industry. This survey is based on a systematic literature review where related research is grouped into four domains such as monitoring, control, prediction, and logistics.&nbsp; Main Findings: This research study presents a detailed work of the eminent researchers and designs of computer architecture that can be applied in agriculture for smart farming. This research study also highlights various unfolded challenges of IoT in agriculture. Implications:&nbsp;This study can be beneficial for farmers, researchers, and professionals working in agricultural institutions for smart farming. Novelty/Originality of the study:&nbsp;Various eminent researchers have been making efforts for smart farming by using IoT concepts in agriculture. But, a bouquet of unfolded challenges is still in a queue for their effective solution. This study makes some efforts to discuss past research and open challenges in IoT based agriculture

Review—Machine Learning Techniques in Wireless Sensor Network Based Precision Agriculture

The use of sensors and the Internet of Things (IoT) is key to moving the world\u27s agriculture to a more productive and sustainable path. Recent advancements in IoT, Wireless Sensor Networks (WSN), and Information and Communication Technology (ICT) have the potential to address some of the environmental, economic, and technical challenges as well as opportunities in this sector. As the number of interconnected devices continues to grow, this generates more big data with multiple modalities and spatial and temporal variations. Intelligent processing and analysis of this big data are necessary to developing a higher level of knowledge base and insights that results in better decision making, forecasting, and reliable management of sensors. This paper is a comprehensive review of the application of different machine learning algorithms in sensor data analytics within the agricultural ecosystem. It further discusses a case study on an IoT based data-driven smart farm prototype as an integrated food, energy, and water (FEW) system

Internet of Things Applications in Precision Agriculture: A Review

The goal of this paper is to review the implementation of an Internet of Things (IoT)-based system in the precision agriculture sector. Each year, farmers suffer enormous losses as a result of insect infestations and a lack of equipment to manage the farm effectively. The selected article summarises the recommended systematic equipment and approach for implementing an IoT in smart farming. This review's purpose is to identify and discuss the significant devices, cloud platforms, communication protocols, and data processing methodologies. This review highlights an updated technology for agricultural smart management by revising every area, such as crop field data and application utilization. By customizing their technology spending decisions, agriculture stakeholders can better protect the environment and increase food production in a way that meets future global demand. Last but not least, the contribution of this research is that the use of IoT in the agricultural sector helps to improve sensing and monitoring of production, including farm resource usage, animal behavior, crop growth, and food processing. Also, it provides a better understanding of the individual agricultural circumstances, such as environmental and weather conditions, the growth of weeds, pests, and diseases

Sustainable and Intelligent Phytoprotection in Photovoltaic Agriculture: New Challenges and Opportunities

Photovoltaic Agriculture (PA) is a new management system combining industry with modern agriculture that can effectively reduce the competition for limited land resource usage between electric power production and agricultural production. However, PA has been facing the challenge of managing plant protection measures because it is difficult to monitor plants grown under the photovoltaic panels by remote sensing satellites and pesticide applications using drones. To overcome this challenge, Solar Insecticidal Lamps (SILs) can be used for phytoprotection in PA. However, to effectively use SILs in PA, it is important to identify a suitable field location to maintain strong wireless communication signals. In this paper, two testbeds were designed and a series of experiments in PA was performed. The results indicate that there is considerable interference exists around the confluence box. A higher interference seriously reduces the Packet Reception Rate (PRR) of the nearby node, which is an important constraint for deploying wireless sensors in PA. Finally, new challenges and future research opportunities are proposed

A Systematic Review of IoT Solutions for Smart Farming

The world population growth is increasing the demand for food production. Furthermore, the reduction of the workforce in rural areas and the increase in production costs are challenges for food production nowadays. Smart farming is a farm management concept that may use Internet of Things (IoT) to overcome the current challenges of food production. This work uses the preferred reporting items for systematic reviews (PRISMA) methodology to systematically review the existing literature on smart farming with IoT. The review aims to identify the main devices, platforms, network protocols, processing data technologies and the applicability of smart farming with IoT to agriculture. The review shows an evolution in the way data is processed in recent years. Traditional approaches mostly used data in a reactive manner. In more recent approaches, however, new technological developments allowed the use of data to prevent crop problems and to improve the accuracy of crop diagnosis.info:eu-repo/semantics/publishedVersio

Review of research progress on soil moisture sensor technology

Soil moisture is directly related to the amount of irrigation in agriculture and influences the yield of crops. Accordingly, a soil moisture sensor is an important tool for measuring soil moisture content. In this study, the previous research conducted in recent 2-3 decades on soil moisture sensors was reviewed and the principles of commonly used soil moisture sensor and their various applications were summarized. Furthermore, the advantages, disadvantages, and influencing factors of various measurement methods employed were compared and analyzed. The improvements were presented by several scholars have established the major applications and performance levels of soil moisture sensors, thereby setting the course for future development. These studies indicated that soil moisture sensors in the future should be developed to achieve high-precision, low-cost, non-destructive, automated, and highly integrated systems. Also, it was indicated that future studies should involve the development of specialized sensors for different applications and scenarios. This review research aimed to provide a certain reference for application departments and scientific researchers in the process of selecting soil moisture sensor products and measuring soil moisture

Photovoltaics and Electrification in Agriculture

Integration of photovoltaics and electrification in agriculture. Works on the integration of photovoltaics in agriculture, as well as electrification and microgrids in agriculture. In addition, some works on sustainability in agriculture are added