A Systematic Review of Current Trends in Artificial Intelligence for Smart Farming to Enhance Crop Yield

Widianto, Mochammad Haldi; Ardimansyah, Mochamad Iqbal; Pohan, Husni Iskandar; Hermanus, Davy Ronald

doi:10.18196/jrc.v3i3.13760

Cited by 15 publications

(20 citation statements)

References 70 publications

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“…It is difficult to develop a method because the author only uses the existing systematic review method. The PRISMA method was formed to facilitate a systematic search, but several studies have developed its application (inspired by a previous study [83], [84]. In this review, the author applies the development of PRISMA.…”

Section: Methodsmentioning

confidence: 99%

“…Fig. 1 will show the Methodology Flowchart (inspired by a previous study [83], [84]). In Fig 1, the author has outlined the criteria, strategies, and assessments that will be used by applying PRISMA development (Inspired by a previous study [83], [84] ).…”

Section: Methodsmentioning

confidence: 99%

“…So that in the next section, the results of the review selection will be explained in the next chapter. The results of the methodology in the previous explanation, but in this section, the author focuses on proposing results that will be used in proposing PRISMA [82] (and inspired by previous pattern Study [85] and [84]) concept used, as shown in Fig. 2.…”

Section: Quality Assessmentmentioning

confidence: 99%

“…In contrast to previous studies that used PRISMA but were not optimal [84], in this study, the author focuses more on the use of PRISMA than how AI on LPWAN and Short- After that, the author already knows the criteria for the research paper. The author will present a discussion in the next section.…”

Section: Quality Assessmentmentioning

confidence: 99%

“…There have been many systematic review studies, one of which [84] conducted research using PRISMA but only focused on one topic and did not show an increase in systematic reviews. In this review, the author tries to add a little discussion about the development of LPWAN and Short-Range Networks using AI to enhance the quality of IoT…”

Section: Comparison With Other Studiesmentioning

confidence: 99%

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A Systematic Review of LPWAN and Short-Range Network using AI to Enhance Internet of Things

Widianto

Sinaga

Ginting

2022

Journal of Robotics and Control

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Artificial intelligence (AI) has recently been used frequently, especially concerning the Internet of Things (IoT). However, IoT devices cannot work alone, assisted by Low Power Wide Area Network (LPWAN) for long-distance communication and Short-Range Network for a short distance. However, few reviews about AI can help LPWAN and Short-Range Network. Therefore, the author took the opportunity to do this review. This study aims to review LPWAN and Short-Range Networks AI papers in systematically enhancing IoT performance. Reviews are also used to systematically maximize LPWAN systems and Short-Range networks to enhance IoT quality and discuss results that can be applied to a specific scope. The author utilizes selected reporting items for systematic review and meta-analysis (PRISMA). The authors conducted a systematic review of all study results in support of the authors' objectives. Also, the authors identify development and related study opportunities. The author found 79 suitable papers in this systematic review, so a discussion of the presented papers was carried out. Several technologies are widely used, such as LPWAN in general, with several papers originating from China. Many reports from conferences last year and papers related to this matter were from 2020-2021. The study is expected to inspire experimental studies in finding relevant scientific papers and become another review.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Quality Assessmentmentioning

confidence: 99%

Section: Quality Assessmentmentioning

confidence: 99%

Section: Comparison With Other Studiesmentioning

confidence: 99%

See 3 more Smart Citations

A Systematic Review of LPWAN and Short-Range Network using AI to Enhance Internet of Things

Widianto

Sinaga

Ginting

2022

Journal of Robotics and Control

Self Cite

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Information and communication technology in smart agriculture: A scientometric review

Bala,

Kaur

2024

Concurrency and Computation

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SummaryAgriculture has been affected by several global trends raising the concern for food security. Agri‐food demands are amplifying due to the ever‐escalating population numbers. Thus, the notion of extending the use of smart innovative technology in managing agricultural practices has emerged rapidly over the last decade. Technological innovations have contributed significantly to shape modern agriculture as smart agriculture. Smart agriculture unfolds various benefits such as increased production, real time data and production insights and remote monitoring. The rising advancements in Information and Communication Technologies (ICT) have paved the way for researchers to use these technologies in managing agricultural practices, leading to greater benefits for farmers and society. These innovations are the key to establishing agriculture as a research discipline. The purpose of this article is to conduct a scientometric analysis to study the structure and evolution of research activities in the field of smart agriculture. The scientometric analysis aims to empirically map the scientific knowledge and identify any possible challenges in the field. This study performs elementary analysis to study publication growth over the years, impact analysis to assess the leading journals, authors, and countries, and articles analysis for findings patterns among the citations over the years and among the keyword‐based clusters. There has been a considerable increase of more than 200% in the number of publications from 2011 to 2022. However, around 60% of authors have contributed with a single publication. The findings of the study reveal the research trends and hot topics for future research fronts. Deep learning, digital agriculture, object detection, blockchain, and semantic segmentation have been identified as trending topics in smart agriculture. Comprehensively, an intellectual view of the agriculture domain is presented as a scientific field in this article.

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Irrigation with Artificial Intelligence: Problems, Premises, Promises

Wei,

Xu,

Kang

et al. 2024

Hum-Cent Intell Syst

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Protagonists allege that artificial intelligence (AI) is revolutionising contemporaneous mindscapes. Here, we authoritatively review the status quo of AI and machine learning application in irrigated agriculture, evaluating the potential of, and challenges associated with, a wide range of existential AI approaches. We contend that aspiring developers of AI irrigation systems may benefit from human-centred AI, a nascent algorithm that captures diverse end-user views, behaviours and actions, potentially facilitating refinement of proposed systems through iterative stakeholder feedback. AI-guided human–machine collaboration can streamline integration of user needs, allowing customisation towards situational farm management adaptation. Presentation of big data in intuitive, legible and actionable forms for specialists and laypeople also urgently requires attention: here, AI-explainable interpretability may help harness human expertise, enabling end-users to contribute their experience within an AI pipeline for bespoke outputs. Transfer learning holds promise in contextualising place-based AI to agroecological regions, production systems or enterprise mixes, even with limited data inputs. We find that the rate of AI scientific and software development in recent times has outpaced the evolution of adequate legal and institutional regulations, and often social, moral and ethical license to operate, revealing consumer issues associated with data ownership, legitimacy and trust. We opine that AI has great potential to elicit sustainable outcomes in food security, social innovation and environmental stewardship, albeit such potential is more likely to be realised through concurrent development of appropriate ethical, moral and legal dimensions.

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A Systematic Review of Current Trends in Artificial Intelligence for Smart Farming to Enhance Crop Yield

Cited by 15 publications

References 70 publications

A Systematic Review of LPWAN and Short-Range Network using AI to Enhance Internet of Things

A Systematic Review of LPWAN and Short-Range Network using AI to Enhance Internet of Things

Information and communication technology in smart agriculture: A scientometric review

Irrigation with Artificial Intelligence: Problems, Premises, Promises

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