Towards smart farming: Systems, frameworks and exploitation of multiple sources

Lytos, Anastasios; Λάγκας, Θωμάς; Sarigiannidis, Panagiotis; Zervakis, M.; Livanos, George

doi:10.1016/j.comnet.2020.107147

Cited by 82 publications

(24 citation statements)

References 65 publications

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“…The evolution of agriculture systems involves the adoption of incoming data from various sources [27] and also the application of big data applications in smart farming [26]. Together these big data technologies and the capability of machine learning algorithms in forecasting certain outcomes will cause major changes in the scope and organization of smart farming [16], [17], [26].…”

Section: Literature Reviewmentioning

confidence: 99%

Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

et al. 2021

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Big Data (BD), Machine Learning (ML) and Internet of Things (IoT) are expected to have a large impact on Smart Farming and involve the whole supply chain, particularly for rice production. The increasing amount and variety of data captured and obtained by these emerging technologies in IoT offer the rice smart farming strategy new abilities to predict changes and identify opportunities. The quality of data collected from sensors greatly influences the performance of the modelling processes using ML algorithms. These three elements (e.g., BD, ML and IoT) have been used tremendously to improve all areas of rice production processes in agriculture, which transform traditional rice farming practices into a new era of rice smart farming or rice precision agriculture. In this paper, we perform a survey of the latest research on intelligent data processing technology applied in agriculture, particularly in rice production. We describe the data captured and elaborate role of machine learning algorithms in paddy rice smart agriculture, by analyzing the applications of machine learning in various scenarios, smart irrigation for paddy rice, predicting paddy rice yield estimation, monitoring paddy rice growth, monitoring paddy rice disease, assessing quality of paddy rice and paddy rice sample classification. This paper also presents a framework that maps the activities defined in rice smart farming, data used in data modelling and machine learning algorithms used for each activity defined in the production and post-production phases of paddy rice. Based on the proposed mapping framework, our conclusion is that an efficient and effective integration of all these three technologies is very crucial that transform traditional rice cultivation practices into a new perspective of intelligence in rice precision agriculture. Finally, this paper also summarizes all the challenges and technological trends towards the exploitation of multiple sources in the era of big data in agriculture.

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Section: Literature Reviewmentioning

confidence: 99%

Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

et al. 2021

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“…They can be encountered in the areas of intelligent or smart cities, healthcare, agriculture as well as industry and production. For example, the implementation of information and technology systems in agriculture through the intensive use of data can improve agricultural productivity [36]. Informatization in I4.0 enables us to monitor and control the physical world with the help of information technology.…”

Section: Research Theme From Scientific Journals Articlesmentioning

confidence: 99%

The Fourth Industrial Revolution and the Sustainability Practices: A Comparative Automated Content Analysis Approach of Theory and Practice

Roblek¹,

Thorpe²,

Meško³

et al. 2020

Preprint

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Background (1) In the time of the 4th Industrial Revolution or Industry 4.0, a conglomerate of technical and social inventions, political contexts, socio-cultural circumstances, environmental policies, business models, and economic policies has emerged. Sustainability policy in theory and practice aims to deal with the effects of all these factors and to try to make decisions that ensure both social and economic development sustainably. The question is how to familiarize oneself with the current knowledge about the relationship between Industry 4.0 and sustainability?; (2) Methods: This research utilizes an automated content analysis method to analyses scientific journals, newspapers and magazines. The comparison of results of both research group shows that the scientific literature focuses more on changes in business models, production processes and technologies that enable sustainable development; (3) We found that the scientific literature focuses more on changes in business models, production processes and technologies that enable sustainable development. Newspapers and magazines articles write more about sustainable or green investment, sustainable standards and sustainable reporting. Newspapers and magazines articles write more about sustainable or green investment, sustainable standards and sustainable reporting. Newspapers, as well as some latest research journals, include articles of the COVID-19 outbreak and its effect on the economy and the environment. Indeed, the outbreak of the virus brings a new thought to the reorganization of the complex relationships between consumers, businesses and the state; (4) Conclusions: According to the comparison of the analyses of the results, it can is that the analyses of both types of literature, both scientific and professional, shows that there are common topics they write about, which are related to the field of clean production, emissions, renewable energy, climate change, sustainable investments and corporate sustainability. An urgent global issue that extends all over the world is the promotion of energy-saving technologies and reduction of carbon dioxide emissions.

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“…Designing and applying integrated farming systems has severe constraints. Towards the technological direction, a severe constraint is the lack of long-term, structured, concrete data [43]. In the best scenarios so far, end users' actions heavily depend on short-term, biased data.…”

Section: Introductionmentioning

confidence: 99%

Towards Climate Smart Farming—A Reference Architecture for Integrated Farming Systems

Kakamoukas

Sarigiannidis

Maropoulos

et al. 2021

Telecom

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Climate change is emerging as a major threat to farming, food security and the livelihoods of millions of people across the world. Agriculture is strongly affected by climate change due to increasing temperatures, water shortage, heavy rainfall and variations in the frequency and intensity of excessive climatic events such as floods and droughts. Farmers need to adapt to climate change by developing advanced and sophisticated farming systems instead of simply farming at lower intensity and occupying more land. Integrated agricultural systems constitute a promising solution, as they can lower reliance on external inputs, enhance nutrient cycling and increase natural resource use efficiency. In this context, the concept of Climate-Smart Agriculture (CSA) emerged as a promising solution to secure the resources for the growing world population under climate change conditions. This work proposes a CSA architecture for fostering and supporting integrated agricultural systems, such as Mixed Farming Systems (MFS), by facilitating the design, the deployment and the management of crop–livestock-=forestry combinations towards sustainable, efficient and climate resilient agricultural systems. Propelled by cutting-edge technology solutions in data collection and processing, along with fully autonomous monitoring systems, e.g., smart sensors and unmanned aerial vehicles (UAVs), the proposed architecture called MiFarm-CSA, aims to foster core interactions among animals, forests and crops, while mitigating the high complexity of these interactions, through a novel conceptual framework.

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Towards smart farming: Systems, frameworks and exploitation of multiple sources

Cited by 82 publications

References 65 publications

Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

Towards Paddy Rice Smart Farming: A Review on Big Data, Machine Learning, and Rice Production Tasks

The Fourth Industrial Revolution and the Sustainability Practices: A Comparative Automated Content Analysis Approach of Theory and Practice

Towards Climate Smart Farming—A Reference Architecture for Integrated Farming Systems

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