A survey on smart farming data, applications and techniques

Alwis, Sandya De; Hou, Ziwei; Zhang, Yishuo; Na, Myung Hwan; Ofoghi, Bahadorreza; Sajjanhar, Atul

doi:10.1016/j.compind.2022.103624

Cited by 87 publications

(46 citation statements)

References 63 publications

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“…This study looks at innovative ways to use new technology in the future. The survey study done by Alwis et al [19] outlines key problems in smart farming through by big data analysis. Smart farming data is used for futuristic yield, analysis of growth, control of quality, and supervision of farm.…”

Section: -Literature Reviewmentioning

confidence: 99%

Emerging Technological Methods for Effective Farming by Cloud Computing and IoT

Rajak¹

2022

Emerg Sci J

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Agriculture provides a solution to the vast majority of problems that threaten human existence. When it comes to agriculture, new or contemporary technology can have a significant impact on a number of factors, including how much food is produced and how long it stays edible. The application of best management practices, for instance, is very common these days in the quest to improve agriculture. New hybrids are resistant to illnesses, use fewer pesticides, have natural defenses against pests, and may be grown in methods that minimize the number of diseases and pests that can affect them. Plants are capable of producing oxygen and medicines in addition to the food that they provide. Consequently, agriculture depends on plants that are in good health. A plant needs water, sunlight, and crucial fertilizer in order to receive the nutrients it needs to have a healthy plant. So, it is necessary to keep an eye on the health of the plant. The article discusses various technological solutions that can be implemented to automate the plant monitoring system. The Internet of Things and cloud computing are two technologies that are contributing to the development of intelligent technology by supplanting traditional agricultural practices. This clever device checks on the well-being of the plants. In order to enable intelligent agriculture, the technology relies on sensors that are dependent on IoT sensors. These sensors monitor the temperature, soil moisture, intensity of the sun's light, air quality value of the soil, vibration, and humidity in the immediate environment of the plant. The networking of these sensors ensures that the plant will continue to be healthy and will function in the appropriate manner. The findings that have been obtained up to this point are encouraging for the continuance of this strategy, which results in the highest possible profit for farmers. Doi: 10.28991/ESJ-2022-06-05-07 Full Text: PDF

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Section: -Literature Reviewmentioning

confidence: 99%

Emerging Technological Methods for Effective Farming by Cloud Computing and IoT

Rajak¹

2022

Emerg Sci J

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“…Greece, both in the Digital Economy & Society Index -DESI and the Digital Transformation Scorecard, lags behind its European partners in most areas related to the digitization and digital education of the workforce, while it is relatively high in the individual indicators concerning businesses (Alwis et al, 2022). The gradual increase of their digital maturity and the decrease of their distance from the European average is an encouraging element and possibly indicates that the Greek companies have already started their digital transformation.…”

Section: Resultsmentioning

confidence: 99%

“…This strategy will ensure that all European economic sectors have the opportunity to take full advantage of digital innovations. In the context of the strategy for the digitization of European Industry, the Digital Transformation Scorecard was created, complementary to the DESI index, which deepens the digital maturity of the economies of the EU Member States (Alwis et al, 2022).…”

Section: Resultsmentioning

confidence: 99%

“…In addition, companies in the industry can combine their physical store with the e-shop or even create two different companies. For example, an online store may be based in Greece, but its suppliers, warehouses and customers may be located abroad (Alwis et al, 2022). However, the realization of new possibilities brings the entrepreneur faced with new needs: from the transition to the "cloud" of business's data, to the modification of marketing methods and the adoption of a "multi-channel" approach to its sales.…”

Section: Introductionmentioning

confidence: 99%

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Green Entrepreneurship and Digital Transformation of SMEs in Food Industry: Α Bibliometric Analysis

Sklavos

Duquenne

Theodossiou

2022

Sci. Ann. Econ. Bus.

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The emerging of green entrepreneurship and digital transformation improve businesses’ efficiency and meet consumers’ demand for environmentally sustainable products, reducing the environmental footprint and strengthening corporate responsibility to society. Moreover, the COVID-19 pandemic has become a key event changing our lives while businesses have to change their daily operations and working from home has become the norm. So, it is possible to say that business activities and business models have undergone some form of digital transformation due to the COVID-19 pandemic. In Greece, the Food Industry can be characterized as one of the most dynamic and competitive economic sectors which is distinguished for its growth prospects. The aim of this study is twofold: (i) to investigate the impact of green entrepreneurship and digital transformation into the performance of Greek SMEs in the food sector and (ii) to highlight the new trends integrated in new business models in the sector. To meet the research purpose, a bibliometric and co-citation analysis was used based on the R package and graphene as a subject of research for bibliometric analysis. The knowledge gained in this article shows how the digital transformation changed the functioning of the companies in the food industry. The conclusions of this article are mainly for the enterprises that are considering their own digitalization, which contributes to the long-term sustainability of them.

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“…Deep learning has found ample applications to the processing of image and sound data in life-science fields like precision medicine 4 , precision livestock farming 5 and smart farming 6 . The analysis of non-tabular data is a task at which deep learning excels.…”

Section: Introductionmentioning

confidence: 99%

Stacked kinship CNN vs. GBLUP for genomic predictions of additive and complex continuous phenotypes

Biscarini

2022

Sci Rep

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Deep learning is impacting many fields of data science with often spectacular results. However, its application to whole-genome predictions in plant and animal science or in human biology has been rather limited, with mostly underwhelming results. While most works focus on exploring alternative network architectures, in this study we propose an innovative representation of marker genotype data and tested it against the GBLUP (Genomic BLUP) benchmark with linear and nonlinear phenotypes. From publicly available cattle SNP genotype data, different types of genomic kinship matrices are stacked together in a 3D pile from where 2D grayscale slices are extracted and fed to a deep convolutional neural network (DNN). We simulated nine phenotype scenarios with combinations of additivity, dominance and epistasis, and compared the DNN to GBLUP-A (computed using only the additive kinship matrix) and GBLUP-optim (additive, dominance, and epistasis kinship matrices, as needed). Results varied depending on the accuracy metric employed, with DNN performing better in terms of root mean squared error (1–12% lower than GBLUP-A; 1–9% lower than GBLUP-optim) but worse in terms of Pearson’s correlation (0.505 for DNN compared to 0.672 and 0.669 of GBLUP-A and GBLUP-optim for fully additive case; 0.274 for DNN, 0.279 for GBLUP-A, and 0.477 for GBLUP-optim for fully dominant case). The proposed approach offers a basis to explore further the application of DNN to tabular data in whole-genome predictions.

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A survey on smart farming data, applications and techniques

Cited by 87 publications

References 63 publications

Emerging Technological Methods for Effective Farming by Cloud Computing and IoT

Emerging Technological Methods for Effective Farming by Cloud Computing and IoT

Green Entrepreneurship and Digital Transformation of SMEs in Food Industry: Α Bibliometric Analysis

Stacked kinship CNN vs. GBLUP for genomic predictions of additive and complex continuous phenotypes

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