Precision Agriculture With Technologies for Smart Farming Towards Agriculture 5.0

Siddharth, Dhirendra; Saini, Dilip Kumar Jang Bahadur; Kumar, Ajay

doi:10.1002/9781119769170.ch14

Cited by 8 publications

(4 citation statements)

References 12 publications

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“…Although individual technological solutions exist, they have not yet been integrated autonomously; for the achievement of this, it is agreed with Gackstetter et al (2023) [45] that synergy is required in the face of societal commitment, public acceptance, legal frameworks, and the development of the crucial role as a success factor for farmers. In developing countries, access to technology is limited due to the digital divide, as cited by Engås et al (2023) [18]; this coincides with Siddharth et al (2021) [198], in which the foundations of future sustainable agriculture are laid in artificial intelligence strategies that integrate agricultural data. However, the heterogeneity of agricultural technologies should not be underestimated, as mentioned by Straete et al (2022) [46].…”

Section: Discussionmentioning

confidence: 53%

Agro-Technological Systems in Traditional Agriculture Assistance: A Systematic Review

Montalvo-Romero,

Montiel-Rosales,

Purroy-Vásquez

et al. 2023

IEEE Access

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Guaranteeing food security from agriculture in an uncertain context, derived from the effects of multiple factors, is a challenge. Traditional agricultural production is the one that faces the greatest challenges, derived from the scarce evolution in agricultural practices, despite being the one that contributes the most to the availability of food, at 80%. This systematic review aims to identify and analyze agrotechnological systems belonging to precision agriculture, which may be potentially adaptable to traditional rural agriculture. Contributions that improved crop yields from scientific and technological studies were analyzed. The PRISMA statement was used as a formal outline to collect and analyze 114 studies from the period 2018-2023. From the review, it was identified that there is a growing trend in the adoption of intelligent systems that help producers in the management of crops, accentuated in the increase of crop yield, in the determination of product quality, and in the management of water resources, mainly. Likewise, it was identified that the preponderant approach is the monitoring and control of crop development. This is achieved through emerging technologies, such as the Internet of Things, artificial intelligence, and machine learning, with information mainly collected by sensors embedded in drones, algorithms, decision support systems, sensors, and Arduino technology systems. Finally, this review shows that there are five viable systems that can be adapted to traditional agriculture to strengthen agricultural production. Therefore, the adoption of scientific-technological contributions from precision agriculture contributes to ensuring food security.INDEX TERMS Agricultural applications, food security, precision agriculture.

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Section: Discussionmentioning

confidence: 53%

Agro-Technological Systems in Traditional Agriculture Assistance: A Systematic Review

Montalvo-Romero,

Montiel-Rosales,

Purroy-Vásquez

et al. 2023

IEEE Access

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show abstract

“…In the coming years, manufacturers of off-road equipment can make a substantial impact on the shift towards Agriculture 5.0 [83].…”

Section: Siddharth Et Almentioning

confidence: 99%

Bridging Industry 5.0 and Agriculture 5.0: Historical Perspectives, Opportunities, and Future Perspectives

Haloui,

Oufaska,

Oudani

et al. 2024

Sustainability

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The agricultural industry has undergone several significant changes over the past few centuries, influenced by the industrial revolutions that have occurred. These changes have progressed from Indigenous agriculture to mechanized farming and the current precision agriculture. While the industrial farming model has increased output, it has also faced various challenges in recent years. Industry 5.0 is expected to have a significant impact on the agriculture sector and potentially lead to a fifth agricultural revolution. In this paper, we examine the motivation behind the industrial and agricultural revolutions 4.0 and 5.0, review the phases of these revolutions that have occurred so far, and offer suggestions for the future. We also provide an overview of the concepts of Industry 4.0 and 5.0, as well as Agriculture 4.0 and 5.0, and discuss the smart strategies that are being implemented in different countries to advance these sectors. Additionally, we focus on the potential applications of Industry 5.0 technologies in the agriculture industry and the research challenges associated with them. Our goal is to provide industrial and agricultural professionals with new research opportunities.

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“…Often including errors, inconsistencies, redundancies, or other abnormalities unnecessarily make access to essential information in a timely and complete manner difficult. The data optimization uses extensive quality by the availability of precise data, better access, organization, and cleaning, irrespective of the source, for maximizing the speed and comprehensivity of the collection, analysis, and application of relevant information [19]. Data optimization has been aimed at reducing the lifespan of information in IoTcloud systems.…”

Section: Agricultural Information Managementmentioning

confidence: 99%

Information-Centric IoT-Based Smart Farming with Dynamic Data燨ptimization

Pal¹,

VijayKumar²,

Akila³

et al. 2023

Computers, Materials &Amp; Continua

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Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies, including big data, the cloud, and the Internet of Things (IoT). Many researchers try to integrate IoT-based smart farming on cloud platforms effectively. They define various frameworks on smart farming and monitoring system and still lacks to define effective data management schemes. Since IoT-cloud systems involve massive structured and unstructured data, data optimization comes into the picture. Hence, this research designs an Information-Centric IoT-based Smart Farming with Dynamic Data Optimization (ICISF-DDO), which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved lifetime. Here, a conceptual framework of the proposed scheme and statistical design model has been well defined. The information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data optimization. The simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%, reliability ratio of 98.63%, a coverage ratio of 99.67%, least sensor error rate of 8.96%, and efficient energy consumption ratio of 4.84%.

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Precision Agriculture With Technologies for Smart Farming Towards Agriculture 5.0

Cited by 8 publications

References 12 publications

Agro-Technological Systems in Traditional Agriculture Assistance: A Systematic Review

Agro-Technological Systems in Traditional Agriculture Assistance: A Systematic Review

Bridging Industry 5.0 and Agriculture 5.0: Historical Perspectives, Opportunities, and Future Perspectives

Information-Centric IoT-Based Smart Farming with Dynamic Data燨ptimization

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