Arquitectura de microservicios para una plataforma de gestión remota para la cría de aves en pastoreo utilizando Amazon Web Services y redes inalámbricas de sensores de malla

Introduction: A variety of innovative solutions known as Precision Livestock Farming (PLF) technologies have been developed for the management of animal production industries, including Wireless Sensor Networks (WSN) for poultry farming. Problem: Current WSN-based systems for poultry farming lack the design of robust but flexible software architectures that ensure the integrity and proper delivery of data. Objective: Designing a microservice-based software architecture (MSA) for a multiplatform remote environmental management system based on Wireless Mesh Sensor Networks (WMSN) to be deployed in pastured poultry farming spaces. Methodology: A review about MSAs designed for animal farming was conducted, to synthesize key factors considered for the design process of the system data flow, microservice definition and the environmental monitoring system technology selection. Results: A cloud MSA with a multi layered scheme using the Amazon Web Services (AWS) platform was developed, validating the persistence of environmental data transmitted from WMSN prototype nodes to be deployed in mobile chicken coops. Conclusion: Defining an End-to-End data flow facilitates the organization of tasks by domains, allowing efficient event communication between components and network reliability both at the hardware and software levels. Originality: This study presents a novel design for a remote environmental monitoring system based on WMSN for mobile coops used in pastured poultry and a multi layered MSA cloud management platform for this specific type of food production industry. Limitations: Software architecture technology selection was based only on services offered, to the date of the study, in the free tier of the Amazon Web Service platform.Introducción: arquitectura de microservicios para una plataforma de gestión remota para la avicultura en pastoreo utilizando Amazon Web Services y redes inalámbricas de sensores de malla, Universidad Tecnológica de Panamá, 2023. Problema: las tecnologías de ganadería de precisión (PLF) ayudan a la gestión de las industrias de producción animal, como el uso de redes de sensores inalámbricos (WSN) en la cría de aves de corral. Los sistemas actuales basados en WSN para la cría de aves de corral carecen de arquitecturas de software sólidas pero flexibles para garantizar la integridad y la entrega adecuada de los datos. Objetivo: diseñar una arquitectura de software basada en microservicios (MSA) para un sistema de gestión ambiental remota basado en Wireless Mesh Sensor Networks (WMSN) para aves en pastoreo. Metodología: se realizó una revisión de MSA para la cría de animales para sintetizar los factores clave considerados en el proceso de diseño del flujo de datos del sistema, la definición de microservicios y la selección de tecnología del sistema de monitoreo ambiental. Resultados: se desarrolló un MSA en la nube con esquema multicapa utilizando la plataforma Amazon Web Services (AWS), validando la persistencia de datos ambientales de nodos prototipo WMSN para ser desplegados en gallineros móviles. Conclusión: definir un flujo de datos End-to-End facilita la organización de tareas por dominio, permitiendo una comunicación eficiente de eventos entre componentes y confiabilidad de la red tanto a nivel de hardware como de software. Originalidad: este estudio presenta un diseño novedoso para un sistema de monitoreo ambiental remoto basado en WMSN para cooperativas móviles utilizadas en aves de pastoreo y una plataforma de administración en la nube MSA de varias capas para esta industria.

Disruptive Technologies in Smart Farming: An Expanded View with Sentiment Analysis

Smart Farming (SF) is an emerging technology in the current agricultural landscape. The aim of Smart Farming is to provide tools for various agricultural and farming operations to improve yield by reducing cost, waste, and required manpower. SF is a data-driven approach that can mitigate losses that occur due to extreme weather conditions and calamities. The influx of data from various sensors, and the introduction of information communication technologies (ICTs) in the field of farming has accelerated the implementation of disruptive technologies (DTs) such as machine learning and big data. Application of these predictive and innovative tools in agriculture is crucial for handling unprecedented conditions such as climate change and the increasing global population. In this study, we review the recent advancements in the field of Smart Farming, which include novel use cases and projects around the globe. An overview of the challenges associated with the adoption of such technologies in their respective regions is also provided. A brief analysis of the general sentiment towards Smart Farming technologies is also performed by manually annotating YouTube comments and making use of the pattern library. Preliminary findings of our study indicate that, though there are several barriers to the implementation of SF tools, further research and innovation can alleviate such risks and ensure sustainability of the food supply. The exploratory sentiment analysis also suggests that most digital users are not well-informed about such technologies

Utilization and Impact of Internet of Things (IoT) in Food Supply Chains from the Context of Food Loss/Waste Reduction, Shelf-Life Extension and Environmental Impact

openThe Internet of Things (IoT) sensor-based technologies are transforming the realm of food production and consumption by offering the potential to enable real-time tracking and data sharing, thus improving communication in the food supply chain. Specifically, real-time information on the location and state of food products as they travel from farms to processing plants, distribution hubs, and eventually consumers can be provided via IoT-enabled sensors and devices. This enables prompt reaction to deviations from ideal circumstances, delaying spoiling and minimizing food loss and waste (FLW). This approach also allows for dynamic inventory management, mitigating issues of overstocking and understocking often linked to food loss. However, the extent to which the implementation of such technologies can contribute to the mitigation of FLW remains uncertain. Thus, this study explores several IoT applications for food supply chains, including real-time monitoring of temperature, humidity, and other important variables. The research also looks at how IoT may help food goods last longer on the shelf. Moreover, IoT technologies have significant environmental impacts, and it is crucial to carefully consider its total environmental effect. IoT promotes energy-efficient transportation, lessens overstocking and understocking, and decreases the carbon footprint related to food production and distribution by optimizing supply chain processes. Therefore, this study also examines the effects of IoT adoption on the environment, including the manufacturing and decommissioning of IoT infrastructure and devices. It evaluates rigorously whether the possible negative consequences of technological production and waste exceed the beneficial environmental benefits, such as energy-efficient transportation and decreased carbon footprints. Shortly, It is aimed to deeply analyse the use and effects of IoT in the food supply chains, with an emphasis on how it may decrease food loss and waste, increase shelf life, and environmental impacts of its use through an extensive literature search in this study.The Internet of Things (IoT) sensor-based technologies are transforming the realm of food production and consumption by offering the potential to enable real-time tracking and data sharing, thus improving communication in the food supply chain. Specifically, real-time information on the location and state of food products as they travel from farms to processing plants, distribution hubs, and eventually consumers can be provided via IoT-enabled sensors and devices. This enables prompt reaction to deviations from ideal circumstances, delaying spoiling and minimizing food loss and waste (FLW). This approach also allows for dynamic inventory management, mitigating issues of overstocking and understocking often linked to food loss. However, the extent to which the implementation of such technologies can contribute to the mitigation of FLW remains uncertain. Thus, this study explores several IoT applications for food supply chains, including real-time monitoring of temperature, humidity, and other important variables. The research also looks at how IoT may help food goods last longer on the shelf. Moreover, IoT technologies have significant environmental impacts, and it is crucial to carefully consider its total environmental effect. IoT promotes energy-efficient transportation, lessens overstocking and understocking, and decreases the carbon footprint related to food production and distribution by optimizing supply chain processes. Therefore, this study also examines the effects of IoT adoption on the environment, including the manufacturing and decommissioning of IoT infrastructure and devices. It evaluates rigorously whether the possible negative consequences of technological production and waste exceed the beneficial environmental benefits, such as energy-efficient transportation and decreased carbon footprints. Shortly, It is aimed to deeply analyse the use and effects of IoT in the food supply chains, with an emphasis on how it may decrease food loss and waste, increase shelf life, and environmental impacts of its use through an extensive literature search in this study

A module placement scheme for fog-based smart farming applications

As in Industry 4.0 era, the impact of the internet of things (IoT) on the advancement of the agricultural sector is constantly increasing. IoT enables automation, precision, and efficiency in traditional farming methods, opening up new possibilities for agricultural advancement. Furthermore, many IoT-based smart farming systems are designed based on fog and edge architecture. Fog computing provides computing, storage, and networking services to latency-sensitive applications (such as Agribots-agricultural robots-drones, and IoT-based healthcare monitoring systems), instead of sending data to the cloud. However, due to the limited computing and storage resources of fog nodes used in smart farming, designing a modules placement scheme for resources management is a major challenge for fog based smart farming applications. In this paper, our proposed module placement algorithm aims to achieve efficient resource utilization of fog nodes and reduce application delay and network usage in Fog-based smart farming applications. To evaluate the efficacy of our proposal, the simulation was done using iFogSim. Results show that the proposed approach is able to achieve significant reductions in latency and network usage

Utilization of Internet of Things and wireless sensor networks for sustainable smallholder agriculture

Agriculture is the economy’s backbone for most developing countries. Most of these countries suffer from insufficient agricultural production. The availability of real-time, reliable and farm-specific information may significantly contribute to more sufficient and sustained production. Typically, such information is usually fragmented and often does fit one-on-one with the farm or farm plot. Automated, precise and affordable data collection and dissemination tools are vital to bring such information to these levels. The tools must address details of spatial and temporal variability. The Internet of Things (IoT) and wireless sensor networks (WSNs) are useful technology in this respect. This paper investigates the usability of IoT and WSN for smallholder agriculture applications. An in-depth qualitative and quantitative analysis of relevant work over the past decade was conducted. We explore the type and purpose of agricultural parameters, study and describe available resources, needed skills and technological requirements that allow sustained deployment of IoT and WSN technology. Our findings reveal significant gaps in utilization of the technology in the context of smallholder farm practices caused by social, economic, infrastructural and technological barriers. We also identify a significant future opportunity to design and implement affordable and reliable data acquisition tools and frameworks, with a possible integration of citizen science

Practical Experiences of a Smart Livestock Location Monitoring System leveraging GNSS, LoRaWAN and Cloud Services.

Livestock farming is, in most cases in Europe, unsupervised, thus making it difficult to ensure adequate control of the position of the animals for the improvement of animal welfare. In addition, the geographical areas involved in livestock grazing usually have difficult access with harsh orography and lack of communications infrastructure, thus the need to provide a low-power livestock localization and monitoring system is of paramount importance, which is crucial not for a sustainable agriculture, but also for the protection of native breeds and meats thanks to their controlled supervision. In this context, this work presents an Internet of things (IoT)-based system integrating low-power wide area (LPWA) technology, cloud and virtualization services to provide real-time livestock location monitoring. Taking into account the constraints coming from the environment in terms of energy supply and network connectivity, our proposed system is based on a wearable device equipped with inertial sensors, Global Positioning System (GPS) receiver and LoRaWAN transceiver, which can provide a satisfactory compromise between performance, cost and energy consumption. At first, this article provides the state-of-the-art localization techniques and technologies applied to smart livestock. Then, we proceed to provide the hardware and firmware co-design to achieve very low energy consumption, thus providing a significant positive impact to the battery life. The proposed platform has been evaluated in a pilot test in the Northern part of Italy, evaluating different configurations in terms of sampling period, experimental duration and number of devices. The results are analyzed and discussed for packe delivery ratio, energy consumption, localization accuracy, battery discharge measurement and delay

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

Evaluation of Non-linearity in MIR Spectroscopic Data for Compressed Learning

Mid-Infrared (MIR) spectroscopy has emerged as the most economically viable technology to determine milk values as well as to identify a set of animal phenotypes related to health, feeding, well-being and environment. However, Fourier transform-MIR spectra incurs a significant amount of redundant data. This creates critical issues such as increased learning complexity while performing Fog and Cloud based data analytics in smart farming. These issues can be resolved through data compression using unsupervisory techniques like PCA, and perform analytics in the compressed-domain i.e. without de-compressing. Compression algorithms should preserve non-linearity of MIRS data (if exists), since emerging advanced learning algorithms can improve their prediction accuracy. This study has investigated the non-linearity between the feature variables in the measurement-domain as well as in two compressed domains using standard Linear PCA and Kernel PCA. Also the non-linearity between the feature variables and the commonly used target milk quality parameters (Protein, Lactose, Fat) has been analyzed. The study evaluates the prediction accuracy using PLS and LS-SVM respectively as linear and non-linear predictive models

Assessment of Smart Mechatronics Applications in Agriculture: A Review

Smart mechatronics systems in agriculture can be traced back to the mid-1980s, when research into automated fruit harvesting systems began in Japan, Europe, and the United States. Impressive advances have been made since then in developing systems for use in modern agriculture. The aim of this study was to review smart mechatronics applications introduced in agriculture to date, and the different areas of the sector in which they are being employed. Various literature search approaches were used to obtain an overview of the current state-of-the-art, benefits, and drawbacks of smart mechatronics systems. Smart mechatronics modules and various networks applied in the processing of agricultural products were examined. Finally, relationships in the data retrieved were tested using a one-way analysis of variance on keywords and sources. The review revealed limited use of sophisticated mechatronics in the agricultural industry in practice at a time of falling production rates and a dramatic decline in the reliability of the global food supply. Smart mechatronics systems could be used in different agricultural enterprises to overcome these issues