A concept for application of integrated digital technologies to enhance future smart agricultural systems

Future agricultural systems should increase productivity and sustainability of food production and supply. For this, integrated and efficient capture, management, sharing, and use of agricultural and environmental data from multiple sources is essential. However, there are challenges to understand and efficiently use different types of agricultural and environmental data from multiple sources, which differ in format and time interval. In this regard, the role of emerging technologies is considered to be significant for integrated data gathering, analyses and efficient use. In this study, a concept was developed to facilitate the full integration of digital technologies to enhance future smart and sustainable agricultural systems. The concept has been developed based on the results of a literature review and diverse experiences and expertise which enabled the identification of stat-of-the-art smart technologies, challenges and knowledge gaps. The features of the proposed solution include: data collection methodologies using smart digital tools; platforms for data handling and sharing; application of Artificial Intelligent for data integration and analysis; edge and cloud computing; application of Blockchain, decision support system; and a governance and data security system. The study identified the potential positive implications i.e. the implementation of the concept could increase data value, farm productivity, effectiveness in monitoring of farm operations and decision making, and provide innovative farm business models. The concept could contribute to an overall increase in the competitiveness, sustainability, and resilience of the agricultural sector as well as digital transformation in agriculture and rural areas. This study also provided future research direction in relation to the proposed concept. The results will benefit researchers, practitioners, developers of smart tools, and policy makers supporting the transition to smarter and more sustainable agriculture systems

The Digitalisation of African Agriculture Report 2018-2019

An inclusive, digitally-enabled agricultural transformation could help achieve meaningful livelihood improvements for Africa’s smallholder farmers and pastoralists. It could drive greater engagement in agriculture from women and youth and create employment opportunities along the value chain. At CTA we staked a claim on this power of digitalisation to more systematically transform agriculture early on. Digitalisation, focusing on not individual ICTs but the application of these technologies to entire value chains, is a theme that cuts across all of our work. In youth entrepreneurship, we are fostering a new breed of young ICT ‘agripreneurs’. In climate-smart agriculture multiple projects provide information that can help towards building resilience for smallholder farmers. And in women empowerment we are supporting digital platforms to drive greater inclusion for women entrepreneurs in agricultural value chains

Applications of Emerging Smart Technologies in Farming Systems: A Review

The future of farming systems depends mainly on adopting innovative intelligent and smart technologies The agricultural sector s growth and progress are more critical to human survival than any other industry Extensive multidisciplinary research is happening worldwide for adopting intelligent technologies in farming systems Nevertheless when it comes to handling realistic challenges in making autonomous decisions and predictive solutions in farming applications of Information Communications Technologies ICT need to be utilized more Information derived from data worked best on year-to-year outcomes disease risk market patterns prices or customer needs and ultimately facilitated farmers in decision-making to increase crop and livestock production Innovative technologies allow the analysis and correlation of information on seed quality soil types infestation agents weather conditions etc This review analysis highlights the concept methods and applications of various futuristic cognitive innovative technologies along with their critical roles played in different aspects of farming systems like Artificial Intelligence AI IoT Neural Networks utilization of unmanned vehicles UAV Big data analytics Blok chain technology et

Architecture and Applications of IoT Devices in Socially Relevant Fields

Number of IoT enabled devices are being tried and introduced every year and there is a healthy competition among researched and businesses to capitalize the space created by IoT, as these devices have a great market potential. Depending on the type of task involved and sensitive nature of data that the device handles, various IoT architectures, communication protocols and components are chosen and their performance is evaluated. This paper reviews such IoT enabled devices based on their architecture, communication protocols and functions in few key socially relevant fields like health care, farming, firefighting, women/individual safety/call for help/harm alert, home surveillance and mapping as these fields involve majority of the general public. It can be seen, to one's amazement, that already significant number of devices are being reported on these fields and their performance is promising. This paper also outlines the challenges involved in each of these fields that require solutions to make these devices reliableComment: 1

An IoT architecture for decision support system in precision livestock

Sustainable animal production is a primary goal of technological development in the livestock industry. However, it is crucial to master the livestock environment due to the susceptibility of animals to variables such as temperature and humidity, which can cause illness, production losses, and discomfort. Thus, livestock production systems require monitoring, reasoning, and mitigating unwanted conditions with automated actions. The principal contribution of this study is the introduction of a self-adaptive architecture named e-Livestock to handle animal production decisions. Two case studies were conducted involving a system derived from the e-Livestock architecture, encompassing a Compost Barn production system - an environment and technology where bovine milk production occurs. The outcomes demonstrate the effectiveness of e-Livestock in three key aspects: (i) abstraction of disruptive technologies based on the Internet of Things (IoT) and Artificial Intelligence and their incorporation into a single architecture specific to the livestock domain, (ii) support for the reuse and derivation of an adaptive self-architecture to support the engineering of a decision support system for the livestock subdomain, and (iii) support for empirical studies in a real smart farm to facilitate future technology transfer to the industry. Therefore, our research’s main contribution is developing an architecture combining machine learning techniques and ontology to support more complex decisions when considering a large volume of data generated on farms. The results revealed that the e-Livestock architecture could support monitoring, reasoning, forecasting, and automated actions in a milk production/Compost Barn environment.Na indústria pecuária, a produção animal sustentável é o principal objetivo do desenvolvimento tecnológico. Porém, é fundamental manter boas condições no ambiente devido à suscetibilidade dos animais a variáveis como temperatura e umidade, que podem causar doenças, perdas de produção e desconforto. Assim, os sistemas de produção pecuária requerem monitoramento, controle e mitigação das condições indesejadas através de ações automatizadas. A principal contribuição deste estudo é a introdução de uma arquitetura auto-adaptativa denominada e-Livestock para apoiar as decisões relacionadas à produção animal. Foram conduzidos dois estudos de caso, envolvendo a arquitetura e-Livestock, que foi utilizada no sistema de produção Compost Barn - ambiente e tecnologia onde ocorre a produção de gado leiteiro. Os resultados demonstraram a utilidade do e-Livestock para avaliar três aspectos principais: (i) abstração de tecnologias disruptivas baseadas em Internet das Coisas (IoT) e Inteligência Artificial, e sua incorporação em uma arquitetura única, específica para o domínio da pecuária, (ii) suporte para a reutilização e derivação de uma arquitetura auto-adaptativa para apoiar o desenvolvimento de uma aplicação de apoio à decisão para o subdomínio da pecuária e (iii) suporte para estudos empíricos em uma fazenda inteligente real para facilitar a transferência de tecnologia para a indústria. Portanto, a principal contribuição dessa pesquisa é o desenvolvimento de uma arquitetura combinando técnicas de machine learning e ontologia para apoiar decisões mais complexas ao considerar um grande volume de dados gerados nas fazendas. Os resultados revelaram que a arquitetura e-Livestock pode apoiar monitoramento, controle, previsão e ações automatizadas em um ambiente de produção de leite/Compost Barn.CAPES - Coordenação de Aperfeiçoamento de Pessoal de Nível Superio

DIGITISING AGRIFOOD Pathways and Challenges. November 2019

As climate change increasingly poses an existential risk for the Earth, scientists and policymakers turn to agriculture and food as areas for urgent and bold action, which need to return within acceptable Planet Boundaries. The links between agriculture, biodiversity and climate change have become so evident that scientists propose a Great Food Transformation towards a healthy diet by 2050 as a major way to save the planet. Achieving these milestones, however, is not easy, both based on current indicators and on the gloomy state of global dialogue in this domain. This is why digital technologies such as wireless connectivity, the Internet of Things, Arti cial Intelligence and blockchain can and should come to the rescue. This report looks at the many ways in which digital solutions can be implemented on the ground to help the agrifood chain transform itself to achieve more sustainability. Together with the solution, we identify obstacles, challenges, gaps and possible policy recommendations. Action items are addressed at the European Union both as an actor of change at home, and in global governance, and are spread across ten areas, from boosting connectivity and data governance to actions aimed at empowering small farmers and end users

A concept for application of integrated digital technologies to enhance future smart agricultural systems

Publication history: Accepted - 16 may 2023; Published - 17 May 2023.Future agricultural systems should increase productivity and sustainability of food production and supply. For this, integrated and efficient capture, management, sharing, and use of agricultural and environmental data from multiple sources is essential. However, there are challenges to understand and efficiently use different types of agricultural and environmental data from multiple sources, which differ in format and time interval. In this regard, the role of emerging technologies is considered to be significant for integrated data gathering, analyses and efficient use. In this study, a concept was developed to facilitate the full integration of digital technologies to enhance future smart and sustainable agricultural systems. The concept has been developed based on the results of a literature review and diverse experiences and expertise which enabled the identification of stat-of-the-art smart technologies, challenges and knowledge gaps. The features of the proposed solution include: data collection methodologies using smart digital tools; platforms for data handling and sharing; application of Artificial Intelligent for data integration and analysis; edge and cloud computing; application of Blockchain, decision support system; and a governance and data security system. The study identified the potential positive implications i.e. the implementation of the concept could increase data value, farm productivity, effectiveness in monitoring of farm operations and decision making, and provide innovative farm business models. The concept could contribute to an overall increase in the competitiveness, sustainability, and resilience of the agricultural sector as well as digital transformation in agriculture and rural areas. This study also provided future research direction in relation to the proposed concept. The results will benefit researchers, practitioners, developers of smart tools, and policy makers supporting the transition to smarter and more sustainable agriculture systems

THE AGRICULTURE 4.0 PARADIGM: STATE OF THE ART, INNOVATIONS AND FUTURE AGENDA

Nei prossimi decenni, il settore agricolo globale dovrà affrontare sfide importanti. L'agricoltura subirà cambiamenti sostanziali e trasformativi dovuti all'introduzione di nuove tecnologie, alla digitalizzazione dei processi e delle catene del valore e all'imperativo della sostenibilità. Secondo questa visione, le aziende (agricole) possono sfruttare gli straordinari miglioramenti delle tecnologie (e delle soluzioni) digitali la cui adozione ha portato alla cosiddetta quarta rivoluzione industriale, che in questo campo di applicazione è nota come "Agricoltura 4.0" (A4.0). In effetti, numerosi lavori in letteratura hanno analizzato le tecnologie abilitanti A4.0 per quanto riguarda le loro specifiche tecniche, l'architettura e il dominio di utilizzo; ne sono un esempio l'Internet delle cose (IoT), l'analisi dei dati e i Big Data, il Cloud Computing e la tecnologia dei sistemi cyber-fisici (CPS), l'intelligenza artificiale (AI) e l'apprendimento automatico (ML), la realtà virtuale e aumentata (VR e AR), la robotica e l'automazione, i droni e i veicoli aerei senza pilota (UAV), l'elaborazione delle immagini, il sistema informativo geografico (GIS) e l'analisi. Tuttavia, un'analisi e una descrizione completa del paradigma e delle modalità di adozione nelle aziende agricole sono meno importanti, così come l'evidenza di studi empirici sull'impatto di A4.0 sulle aziende agricole. Per colmare questa lacuna, questa tesi offrirà contributi da tre prospettive (A, B e C). Il primo contributo è una revisione sistematica della letteratura (SLR), che esplora 1): quali sono i domini applicativi in cui trova applicazione l'Agricoltura 4.0, 2): quali tecnologie consentono l'implementazione dell'Agricoltura 4.0; 3): descrive i vantaggi chiave associati all'adozione dell'Agricoltura 4.0 (contributo A). Il secondo e il terzo contributo sono studi descrittivi e di indagine longitudinale, che 1) indagano lo stato dell'arte del paradigma A4.0 nelle aziende italiane (contributo B), 2) confrontano l'avanzamento dello stato dell'arte dell'I4.0 in un intervallo di due anni nel settore agricolo italiano (contributo C); questi contributi sono principalmente volti a fornire evidenze empiriche su come le soluzioni A4.0 stanno impattando sulle aziende e su come il paradigma si sta evolvendo. I risultati di questo progetto di ricerca contribuiscono alla letteratura sull'agricoltura 4.0. In particolare, la SLR per le applicazioni I4.0 nel contesto manifatturiero fornisce una descrizione dettagliata e olistica dei casi d'uso delle tecnologie abilitanti I4.0 nei processi del ciclo di vita delle aziende manifatturiere. In secondo luogo, l'indagine descrittiva sullo stato dell'arte dell'I4.0 nelle aziende manifatturiere italiane fornisce una descrizione concreta di come l'I4.0 è conosciuto e adottato dalle aziende, nonché dei benefici e degli ostacoli corrispondenti. In terzo luogo, attraverso uno studio dinamico dello stato dell'arte, confrontando i dati raccolti nel 2021 e nel 2023, viene dimostrata la caratteristica evolutiva dell'A4.0In the upcoming decades, the global agricultural sector will grapple with significant challenges. Agriculture will experience substantial and transformative changes due to the introduction of new technologies, digitalization of processes and value chains, and the imperative of sustainability. According to this view, companies (farms) can exploit the outstanding improvements in digital technologies (and solutions) whose adoption has brought to the so-called fourth industrial revolution, which in this field of application is known as “Agriculture 4.0” (A4.0). Indeed, numerous literature has investigated A4.0 enabling technologies with regards to their technical specification, architecture, and domain of use, examples are Internet of Things (IoT), Data Analytics and Big Data, Cloud Computing and Cyber-Physical Systems (CPS) technology, Artificial Intelligence (AI) and Machine Learning (ML), Virtual and Augmented Reality (VR & AR), Robotics and Automation, Drones and Unmanned Aerial Vehicles (UAVs), Image Processing, Geographic information system (GIS) and analytics. However, a comprehensive analysis and description of the paradigm, as well as the adoption mode in farms is of less concern, as well as the evidence from empirical studies on how farms are impacted by A4.0. To fill this gap, this dissertation will make contributions from three perspectives (A, B and C). The first contribution is a systematic literature review (SLR), which explores 1): what are the application domains where Agriculture 4.0 finds its applications, 2): which technologies enable the implementation of Agriculture 4.0; 3): describing the key advantages associated with adopting Agriculture 4.0 (contribution A). The second and third contributions are descriptive and longitudinal survey studies, which 1) investigate the state-of-the-art of A4.0 paradigm in Italian companies (contribution B), 2) compare the I4.0 state-of-the-art advancement in a 2-year gap in Italian agricultural sector (contribution C); these contributions are mainly to provide empirical evidence on how A4.0 solutions are impacting on companies and how the paradigm is evolving. The results of this research project contribute to Agriculture 4.0 literature. In particular, the SLR for I4.0 applications in the manufacturing context provides a detailed and holistic description of the use cases of I4.0 enabling technologies in the lifecycle processes of manufacturing companies. Second, the descriptive survey of I4.0 state-of-the-art in Italian manufacturing companies provides a concrete description of how I4.0 is known and adopted by companies, as well as the corresponded benefits and obstacles. Third, through a dynamic state-of-the-art study, comparing with the data collected from 2021 and 2023, the evolvement feature of A4.0 is demonstrate

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