Aggregate Farming in the Cloud: The AFarCloud ECSEL project

Farming is facing many economic challenges in terms of productivity and cost-effectiveness. Labor shortage partly due to depopulation of rural areas, especially in Europe, is another challenge. Domain specific problems such as accurate monitoring of soil and crop properties and animal health are key factors for minimizing economical risks, and not risking human health. The ECSEL AFarCloud (Aggregate Farming in the Cloud) project will provide a distributed platform for autonomous farming that will allow the integration and cooperation of agriculture Cyber Physical Systems in real-time in order to increase efficiency, productivity, animal health, food quality and reduce farm labor costs. Moreover, such a platform can be integrated with farm management software to support monitoring and decision-making solutions based on big data and real-time data mining techniques.publishedVersio

The AFarCloud ECSEL Project

Farming is facing many economic challenges in terms of productivity and cost-effectiveness. Labor shortage partly due to depopulation of rural areas, especially in Europe, is another challenge. Domain specific problems such as accurate identification and proper quantification of pathogens affecting plant and animal health are key factors for minimizing economical risks, and not risking human health. The ECSEL AFarCloud (Aggregate FARming in the CLOUD) project will provide a distributed platform for autonomous farming that will allow the integration and cooperation of agriculture Cyber Physical Systems in real-time in order to increase efficiency, productivity, animal health, food quality and reduce farm labour costs. This platform will be integrated with farm management software and will support monitoring and decision-making solutions based on big data and real-time data mining techniques.The AFarCloud project is funded from the ECSEL Joint Undertaking under grant agreement n° 783221, and from National funding

UAV Cloud Platform for Precision Farming

A new application for Unmanned Aerial Vehicles comes to light daily to solve some of modern society’s problems. One of the mentioned predicaments is the possibility for optimization in agricultural processes. Due to this, a new area arose in the last years of the twentieth century, and it is in constant progression called Precision Farming. Nowadays, a division of this field growth is relative to Unmanned Aerial Vehicles applications. Most traditional methods employed by farmers are ineffective and do not aid in the progression and solution of these issues. However, there are some fields that have the possibility to enhance many agriculture methods, such fields are Cyber-Physical Systems and Cloud Computing. Given its capabilities like aerial surveillance and mapping, Cyber- Physical Systems like Unmanned Aerial Vehicles are being used to monitor vast crops, to gather insightful data thatwould take a lot more time if being collected by hand. However, these systems typically lack computing power and storage capacity, meaning that much of its gathered data cannot be stored and further analyzed locally. That is the obstacle that Cloud Computing can solve. With the possibility to offload computing power by sending the collected data to a cloud, it is possible to leverage the enormous computing power and storage capabilities of remote data-centers to gather and analyze these datasets. This dissertation proposes an architecture for this use case by leveraging the advantages of Cloud Computing to aid the obstacles of Unmanned Aerial Vehicles. Moreover, this dissertation is a collaboration with an on-going Horizon 2020 European project that deals with precision farming and agriculture enhanced by Cyber-Physical Systems.A cada dia que passa, novas aplicações para Veículos aéreos não tripulados são inventadas, de forma a resolver alguns dos problemas actuais da sociedade. Um desses problemas, é a possibilidade de otimização em processos agrículas. Devido a isto, nos últimos anos do século 20 nasceu uma nova área de investigação intitulada Agricultura de alta precisão. Hoje em dia, uma secção desta área diz respeito à inovação nas aplicações com recurso a Veículos aéreos não tripulados. A maioria dos métodos tradicionais usados por agricultores são ineficientes e não auxiliam nem a evolução nem a resolução destes problemas. Contudo, existem algumas áreas científicas que permitem a evoluçao de algumos métodos agrículas, estas áreas são os Sistemas Ciber-Físicos e a Computação na Nuvem. Dadas as suas capacidades tais como a vigilância e mapeamento aéreo, certos Sistemas Ciber-Físicos como os Veículos aéreos não tripulados estão a ser usados para monitorizar vastas culturas de forma a recolher dados que levariam muito mais tempo caso fossem recolhidos manualmente. No entanto, estes sistemas geralmente não detêm grandes capacidades de computação e armazenamento, o que significa que muitos dos dados recolhidos não podem ser armazenados e analisados localmente. É aí que a Computação na Nuvem é útil, com a possibilidade de enviar estes dados para uma nuvem, é possível aproveitar o enorme poder de computação e os recursos de armazenamento dos datacenters remotos para armazenar e analisar estes conjuntos de dados. Esta dissertação propõe uma arquitetura para este caso de uso ao fazer uso das vantagens da Computação na Nuvem de forma a combater os obstáculos dos Veículos aéreos não tripulados. Além disso, esta dissertação é também uma colaboração com um projecto Europeu Horizonte 2020 na área da Agricultura de alta precisão com recurso a Veículos aéreos não tripulados

Cyber-Agricultural Systems for Crop Breeding and Sustainable Production

The Cyber-Agricultural System (CAS) Represents an overarching Framework of Agriculture that Leverages Recent Advances in Ubiquitous Sensing, Artificial Intelligence, Smart Actuators, and Scalable Cyberinfrastructure (CI) in Both Breeding and Production Agriculture. We Discuss the Recent Progress and Perspective of the Three Fundamental Components of CAS – Sensing, Modeling, and Actuation – and the Emerging Concept of Agricultural Digital Twins (DTs). We Also Discuss How Scalable CI is Becoming a Key Enabler of Smart Agriculture. in This Review We Shed Light on the Significance of CAS in Revolutionizing Crop Breeding and Production by Enhancing Efficiency, Productivity, Sustainability, and Resilience to Changing Climate. Finally, We Identify Underexplored and Promising Future Directions for CAS Research and Development

Agriculture 4.0: A systematic literature review on the paradigm, technologies and benefits

Demographics will increase the demand for food and reduce the availability of labour in many countries all over the world. Moreover, scarcity of natural resources, climate change and food waste these are issues that are strongly impacting the agricultural sector and undermining sus-tainability. Digitalisation is expected to be a driving force in tackling these problems that are characterising agriculture. In particular, the adoption of digital technologies to support processes in the primary sector goes by the name of Agriculture 4.0. Although the number of contributions related to these issues is constantly growing, several areas are still unexplored or not fully addressed. This paper addresses the adoption of digital technologies and investigates the appli-cation domain of these technologies, presenting a systematic review of the literature on this subject. Moreover, this research shed light on the technologies adopted and related benefits. Hence, the research has turned its attention to the description of the main pillars, such as the categorisation of its main application domains and enabling technologies. The results of the research show that the different technologies applied in the various fields of application provide benefits both in terms of efficiency (cost reduction, farm productivity) and reduced environ-mental impact and increased sustainability

Unmanned Ground Vehicles for Smart Farms

Forecasts of world population increases in the coming decades demand new production processes that are more efficient, safer, and less destructive to the environment. Industries are working to fulfill this mission by developing the smart factory concept. The agriculture world should follow industry leadership and develop approaches to implement the smart farm concept. One of the most vital elements that must be configured to meet the requirements of the new smart farms is the unmanned ground vehicles (UGV). Thus, this chapter focuses on the characteristics that the UGVs must have to function efficiently in this type of future farm. Two main approaches are discussed: automating conventional vehicles and developing specifically designed mobile platforms. The latter includes both wheeled and wheel-legged robots and an analysis of their adaptability to terrain and crops

Training Evaluation in a Smart Farm using Kirkpatrick Model: A Case Study of Chiang Mai

Farmers can now use IoT to improve farm efficiency and productivity by using sensors for farm monitoring to enhance decision-making in areas such as fertilization, irrigation, climate forecast, and harvesting information. Local farmers in Chiang Mai, Thailand, on the other hand, continue to lack knowledge and experience with smart farm technology. As a result, the 'SUNSpACe' project, funded by the European Union's Erasmus+ Program, was launched to launch a training course which improve the knowledge and performance of Thai farmers. To assess the effectiveness of the training, The Kirkpatrick model was used in this study. Eight local farmers took part in the training, which was divided into two sections: mobile learning and smart farm laboratory. During the training activities, different levels of the Kirkpatrick model were conducted and tested: reaction (satisfaction test), learning (knowledge test), and behavior (performance test). The overall result demonstrated the participants' positive reaction to the outcome. The paper also discusses the limitations and suggestions for training activities

The Fourth Industrial Revolution and Precision Agriculture

The Fourth Industrial Revolution will see the convergence of artificial intelligence and data technology as a new solution to address industrial and social problems across the globe, by integrating cyber and physical fields. The Fourth Industrial Revolution will send a ripple effect of far-reaching repercussions throughout the labor-intensive field of agriculture. Combining artificial intelligence and big data will evolve into a high-tech industry that operates itself. These technologies allow for precision agriculture, such as yield monitoring, diagnosing insect pests, measuring soil moisture, diagnosing harvest time, and monitoring crop health status. In particular, the Internet of things (IoT) will measure the temperature, humidity, and amount of sunlight in production farms, making it possible for remote control via mobile devices. It will not only boost the production of the farms but also add to their value

Internet of services-based business model: a case study in the livestock industry

Purpose – Considering the relevance of innovative business models in the digitally transformed market andthe lack of clarity on the internet of services (IoS) contribution for a business model deployment in currentliterature, this study aims to fill this gap by evaluating a business model that converges to an IoS adoption ina direct sale of free-range eggs from farmers to consumers.Design/methodology/approach – From the bibliographical research regarding the IoS and businessmodel, the authors developed an IoS-based model framework. The framework has been evaluated in a realbusiness scenario by using a single case study through an interview with the entrepreneur and documentalanalysis.Findings – As the main result, a framework with the attributes can be considered a tool for an IoS-basedbusiness model deployment. The case study concluded that the business is aligned with the IoS adoption, andthe framework presents adherence to it.Research limitations/implications – The case study was limited to only one company owing to theIoS’s novelty and the lack of correlated business models. Although the case study limits to the agriculturefield, the proposed framework may be broadly applied.Originality/value – Considering that the lack of a comprehensive business model causes newbusinesses to face challenges, it is relevant bringing up the present case study of the IoS-based businessmodel, which correlates these two subjects, still poorly explored in the scientific literature: IoS andbusiness models