The Impact of Data Analytics in Digital Agriculture: A Review

Chergui, Nabila; Kechadi, Tahar; McDonnell, Michael

doi:10.1109/octa49274.2020.9151851

Cited by 22 publications

(12 citation statements)

References 57 publications

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“…In the following sections, we discuss the data analysis process. This discussion is structured based on the main categories of the data analysis; classification, and clustering [24]. Note that, for high quality results, the data needs to be pre-processed, as discussed in the previous section.…”

Section: Digital Agriculture Datamentioning

confidence: 99%

“…There are two major categories of data analysis: Classification and Clustering. In the work of [24], authors studied applications of data mining techniques in crop management and proposed a classification of these applications. They found that the classification and clustering are the main used categories, where the classification includes prediction, detection, protection, and categorisation).…”

Section: Classification For Crop Monitoringmentioning

confidence: 99%

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Data analytics for crop management: a big data view

Chergui

Kechadi

2022

J Big Data

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Recent advances in Information and Communication Technologies have a significant impact on all sectors of the economy worldwide. Digital Agriculture appeared as a consequence of the democratisation of digital devices and advances in artificial intelligence and data science. Digital agriculture created new processes for making farming more productive and efficient while respecting the environment. Recent and sophisticated digital devices and data science allowed the collection and analysis of vast amounts of agricultural datasets to help farmers, agronomists, and professionals understand better farming tasks and make better decisions. In this paper, we present a systematic review of the application of data mining techniques to digital agriculture. We introduce the crop yield management process and its components while limiting this study to crop yield and monitoring. After identifying the main categories of data mining techniques for crop yield monitoring, we discuss a panoply of existing works on the use of data analytics. This is followed by a general analysis and discussion on the impact of big data on agriculture.

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Section: Digital Agriculture Datamentioning

confidence: 99%

Section: Classification For Crop Monitoringmentioning

confidence: 99%

Data analytics for crop management: a big data view

Chergui

Kechadi

2022

J Big Data

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“…According to ref. [19], digital farming approaches can provide farmers with useful information about (I) the use of fertilizers, chemicals, seeds, and irrigation management strategies, (II) the environment protection, (III) pest, climate, and crop monitoring management solutions, (IV) market demands and business conditions. However, agricultural production systems are complex, dynamic, and require sophisticated management [20].…”

Section: Introductionmentioning

confidence: 99%

Toward the Next Generation of Digitalization in Agriculture Based on Digital Twin Paradigm

Nasirahmadi

Hensel

2022

Sensors

157

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Digitalization has impacted agricultural and food production systems, and makes application of technologies and advanced data processing techniques in agricultural field possible. Digital farming aims to use available information from agricultural assets to solve several existing challenges for addressing food security, climate protection, and resource management. However, the agricultural sector is complex, dynamic, and requires sophisticated management systems. The digital approaches are expected to provide more optimization and further decision-making supports. Digital twin in agriculture is a virtual representation of a farm with great potential for enhancing productivity and efficiency while declining energy usage and losses. This review describes the state-of-the-art of digital twin concepts along with different digital technologies and techniques in agricultural contexts. It presents a general framework of digital twins in soil, irrigation, robotics, farm machineries, and food post-harvest processing in agricultural field. Data recording, modeling including artificial intelligence, big data, simulation, analysis, prediction, and communication aspects (e.g., Internet of Things, wireless technologies) of digital twin in agriculture are discussed. Digital twin systems can support farmers as a next generation of digitalization paradigm by continuous and real-time monitoring of physical world (farm) and updating the state of virtual world.

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“…Grieves and Vickers worked together on adapting the concept of digital twins in the manufacturing sector to improve product lifecycle management [ 8 ]. Recently, in the Industry 4.0 technological umbrella, digital twins was presented as a way to increase productivity, efficiency, and adaptability [ 3 , 9 , 10 , 11 , 12 , 13 ]. The use of digital twins has been reported in the manufacturing process [ 14 ] of the automotive and energy [ 15 ] or even aerospace and health [ 13 ] industries.…”

Section: Introductionmentioning

confidence: 99%

The Digital Twin Paradigm Applied to Soil Quality Assessment: A Systematic Literature Review

Silva

Rodríguez‐Sedano

Baptista

et al. 2023

Sensors

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This article presents the results regarding a systematic literature review procedure on digital twins applied to precision agriculture. In particular, research and development activities aimed at the use of digital twins, in the context of predictive control, with the purpose of improving soil quality. This study was carried out through an exhaustive search of scientific literature on five different databases. A total of 158 articles were extracted as a result of this search. After a first screening process, only 11 articles were considered to be aligned with the current topic. Subsequently, these articles were categorised to extract all relevant information, using the preferred reporting items for systematic reviews and meta-analyses methods. Based on the obtained results, there are two main conclusions to draw: First, when compared with industrial processes, there is only a very slight rising trend regarding the use of digital twins in agriculture. Second, within the time frame in which this work was carried out, it was not possible to find any published paper on the use of digital twins for soil quality improvement within a model predictive control context.

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The Impact of Data Analytics in Digital Agriculture: A Review

Cited by 22 publications

References 57 publications

Data analytics for crop management: a big data view

Data analytics for crop management: a big data view

Toward the Next Generation of Digitalization in Agriculture Based on Digital Twin Paradigm

The Digital Twin Paradigm Applied to Soil Quality Assessment: A Systematic Literature Review

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