AGRICULTURE 4.0 - THE USE OF SMART TECHNOLOGIES FOR HIGHPERFORMANCE AGRICULTURE

Given that the labor market in Romania has an acute shortage of labor (about 1 million people), in agriculture this lack is felt even more acutely because the population in the villages is declining and aging, thus it is increasingly difficult for Romanian farmers to find labor, let alone skilled labor. One solution can be the digitization of agriculture, ie the introduction of the latest management concepts, sensors, automation, robots, etc. in the modernization of work processes in agriculture, thus reducing the need for labor, while increasing productivity and efficiency in agriculture

Design of Internet of Things (IoT) Based Hydroponic Controlling Device in Pyramid Greenhouse

Smart farming technology was previously implemented at Wedomartani experimental station, Faculty of Agriculture UPN "Veteran" Yogyakarta. It is proven to overcome human resource limitations in hydroponic cultivation. Even so, Smart farming has not been implemented yet in Pyramid Greenhouse, Which is the iconic landmark of the Faculty of Agriculture. Preparing IoT-based devices requires designs with certain specifications. Without an appropriate design, it would be found a failure system. This article’s purpose was to design an Internet of things (IoT) based hydroponic controlling device in Greenhouse Pyramid UPN “Veteran” Yogyakarta. It was built based on a literature study. Expert proofing was performed to ensure the design would work if implemented. The design contained the system overview, hardware description, user interface design, and integration of device system design in hydroponic installations. The design was positively accepted by users (Head of the experimental field and technicians). In the future, the proposed design needs to be realized as a part of greenhouse development

Investigating into the Prevalence of Complex Event Processing and Predictive Analytics in the Transportation and Logistics Sector: Initial Findings From Scientific Literature

As ever new sensor solutions are invading people’s everyday lives and business processes, the use of the signals and events provided by the devices poses a challenge. Innovative ways of handling the large amount of data promise an effective and efficient means to overcome that challenge. With the help of complex event processing and predictive techniques, added value can be created. While complex event processing is able to process the multitude of signals coming from the sensors in a continuous manner, predictive analytics addresses the likelihood of a certain future state or behavior by detecting patterns from the signal database and predicting the future according to the detections. As to the transportation and logistics domain, processing the signal stream and predicting the future promises a big impact on the operations because the transportation and logistics sector is known as a very complex one. The complexity of the sector is linked with the many stakeholders taking part in a variety of operations and the partly high level of automation often being accompanied by manual processes. Hence, predictions help to prepare better for upcoming situations and challenges and, thus, to save resources and cost. The present paper is to investigate the prevalence of complex event processing and predictive analytics in logistics and transportation cases in the research literature in order to motivate a subsequent systematic literature view as the next step in the research endeavor

Crop Management with the IoT: an Interdisciplinary Survey

In this study we analyze how crop management is going to benefit from the Internet of Things providing an overview of its architecture and components from an agronomic and a technological perspective. The present analysis highlights that IoT is a mature enabling technology, with articulated hardware and software components. Cheap networked devices may sense crop fields at a finer grain, to give timeliness warnings on stress conditions and the presence of disease to a wider range of farmers. Cloud computing allows to reliably store and access heterogeneous data, developing and deploy farm services. From this study emerges that IoT is also going to increase attention to sensor quality and placement protocol, while Machine Learning should be oriented to produce understandable knowledge, which is also useful to enhance Cropping System Simulation systems

A Deep Learning Model Compression and Ensemble Approach for Weed Detection

Site-specific weed management is an important practice in precision agriculture. Current advances in artificial intelligence have resulted in the use of large deep convolutional neural networks for weed detection. In this paper, a transfer learning, model compression, and ensemble learning approach is introduced that is suitable for resource-limited hardware such as mobile and embedded devices. The resulting ensemble model achieves 91.2% classification accuracy which is comparable to the performance of state-of-the-art deep learning models (such as the vanilla VGG16, DenseNet, and ResNet) while being about 62.22% smaller in size than DenseNet (the smallest-sized full-sized model). The approach used in this study is beneficial for further development of deep convolutional neural networks on smaller resource-limited hardware typically used in agriculture, as well as other industries such as healthcare and telecommunication

Towards a Cost-Benefit-Analysis of Data-Driven Business Models

The emergence of data-driven business models calls for their systematic design and evaluation. In this paper, we focus on a first step towards a Cost-Benefit-Analysis of data-driven business models. Within data-driven business models, data act as enabler for the development of innovative services. However, to justify internal funding of new services, an assessment of the financial impact for the service at hand is often required. We approach this by identifying drivers of cost and benefit based on the Service Business Model Canvases of twenty cases. Based on the results, all drivers and their associated models for quantification were consolidated into a single meta-model. With this, we provide a basis for the economic assessment of service ideas and their refinement during the design process

Talent recommendation system in big data era

1人才推荐系统的应用背

Effort Estimation for Service-Oriented Computing Environments

The concept of service in Service-Oriented Architecture (SOA) makes possible to introduce other ideas like service composition, governance and virtualization. Each of these ideas, when exercised to an enterprise level, provides benefits in terms of cost and performance. These ideas bring many new opportunities for the project managers in making the estimates of effort required to produce SOA systems. This is because the SOA systems are different from traditional software projects and there is a lack of efficient metrics and models for providing a high level of confidence in effort estimation. Thus, in this paper, an efficient estimation methodology has been presented based on analyzing the development phases of past SOA based software systems. The objective of this paper is twofold: first, to study and analyze the development phases of some past SOA based systems; second, to propose estimation metrics based on these analyzed parameters. The proposed methodology is facilitated from the use of four regression(s) based estimation models. The validation of the proposed methodology is cross checked by comparing the predictive accuracy, using some commonly used performance measurement indicators and box-plots evaluation. The evaluation results of the study (using industrial data collected from 10 SOA based software systems) show that the effort estimates obtained using the multiple linear regression model are more accurate and indicate an improvement in performance than the other used regression models

Towards a method to quantitatively measure toolchain interoperability in the engineering lifecycle: A case study of digital hardware design

The engineering lifecycle of cyber-physical systems is becoming more challenging than ever. Multiple engineering disciplines must be orchestrated to produce both a virtual and physical version of the system. Each engineering discipline makes use of their own methods and tools generating different types of work products that must be consistently linked together and reused throughout the lifecycle. Requirements, logical/descriptive and physical/analytical models, 3D designs, test case descriptions, product lines, ontologies, evidence argumentations, and many other work products are continuously being produced and integrated to implement the technical engineering and technical management processes established in standards such as the ISO/IEC/IEEE 15288:2015 "Systems and software engineering-System life cycle processes". Toolchains are then created as a set of collaborative tools to provide an executable version of the required technical processes. In this engineering environment, there is a need for technical interoperability enabling tools to easily exchange data and invoke operations among them under different protocols, formats, and schemas. However, this automation of tasks and lifecycle processes does not come free of charge. Although enterprise integration patterns, shared and standardized data schemas and business process management tools are being used to implement toolchains, the reality shows that in many cases, the integration of tools within a toolchain is implemented through point-to-point connectors or applying some architectural style such as a communication bus to ease data exchange and to invoke operations. In this context, the ability to measure the current and expected degree of interoperability becomes relevant: 1) to understand the implications of defining a toolchain (need of different protocols, formats, schemas and tool interconnections) and 2) to measure the effort to implement the desired toolchain. To improve the management of the engineering lifecycle, a method is defined: 1) to measure the degree of interoperability within a technical engineering process implemented with a toolchain and 2) to estimate the effort to transition from an existing toolchain to another. A case study in the field of digital hardware design comprising 6 different technical engineering processes and 7 domain engineering tools is conducted to demonstrate and validate the proposed method.The work leading to these results has received funding from the H2020-ECSEL Joint Undertaking (JU) under grant agreement No 826452-“Arrowhead Tools for Engineering of Digitalisation Solutions” and from specific national programs and/or funding authorities. Funding for APC: Universidad Carlos III de Madrid (Read & Publish Agreement CRUE-CSIC 2023)

Transition towards sustainability in agriculture and food systems: Role of information and communication technologies

Food sustainability transitions refer to transformation processes necessary to move towards sustainable food systems. Digitization is one of the most important ongoing transformation processes in global agriculture and food chains. The review paper explores the contribution of information and communication technologies (ICTs) to transition towards sustainability along the food chain (production, processing, distribution, consumption). A particular attention is devoted to precision agriculture as a food production model that integrates many ICTs. ICTs can contribute to agro-food sustainability transition by increasing resource productivity, reducing inefficiencies, decreasing management costs, and improving food chain coordination. The paper also explores some drawbacks of ICTs as well as the factors limiting their uptake in agriculture. Keywords: Sustainability transitions, ICT, Agriculture digitization, Food supply chain, Food processing, Distribution, Consumptio