Artificial Intelligence and Agriculture 5. 0

Murugesan, Rajkumar; Kidambi, Sudarsanam S.; G, Malathi; Varadarajan, Vijayakumar; Neelanarayanan, Venkataraman; Rajaraman, Mr. Venugopal; Rekha, D.

doi:10.35940/ijrte.b1510.078219

Cited by 22 publications

(7 citation statements)

References 7 publications

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“…Notably, the employment of robotic chefs to predict and ameliorate food quality as showcased by Zhu and Chang (2020) testify that AI holds a transformative power. Future studies could focus on deepening the knowledge of AI-driven technologies' role in business ecosystems (Yang et al, 2020), and understanding how machine learning could improve yield prediction (Murugesan et al, 2019).…”

Section: Discussion: An Interpretive Frameworkmentioning

confidence: 99%

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Unleashing the value of artificial intelligence in the agri-food sector: where are we?

Trabelsi,

Casprini,

Fiorini

et al. 2023

BFJ

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PurposeThis study analyses the literature on artificial intelligence (AI) and its implications for the agri-food sector. This research aims to identify the current research streams, main methodologies used, findings and results delivered, gaps and future research directions.Design/methodology/approachThis study relies on 69 published contributions in the field of AI in the agri-food sector. It begins with a bibliographic coupling to map and identify the current research streams and proceeds with a systematic literature review to examine the main topics and examine the main contributions.FindingsSix clusters were identified: (1) AI adoption and benefits, (2) AI for efficiency and productivity, (3) AI for logistics and supply chain management, (4) AI for supporting decision making process for firms and consumers, (5) AI for risk mitigation and (6) AI marketing aspects. Then, the authors propose an interpretive framework composed of three main dimensions: (1) the two sides of AI: the “hard” side concerns the technology development and application while the “soft” side regards stakeholders' acceptance of the latter; (2) level of analysis: firm and inter-firm; (3) the impact of AI on value chain activities in the agri-food sector.Originality/valueThis study provides interpretive insights into the extant literature on AI in the agri-food sector, paving the way for future research and inspiring practitioners of different AI approaches in a traditionally low-tech sector.

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Section: Discussion: An Interpretive Frameworkmentioning

confidence: 99%

“…Future studies could focus on deepening the knowledge of AI-driven technologies' role in business ecosystems (Yang et al. , 2020), and understanding how machine learning could improve yield prediction (Murugesan et al. , 2019).…”

Section: Discussion: An Interpretive Frameworkmentioning

confidence: 99%

Unleashing the value of artificial intelligence in the agri-food sector: where are we?

Trabelsi,

Casprini,

Fiorini

et al. 2023

BFJ

View full text Add to dashboard Cite

show abstract

“…The correlation be-tween Farming 4.0 and the 4.0 industrial revolution, regarding the improvement of the mechanization of fieldwork, is observed in [39]. Experts in the field of Farming 5.0 and food security have found that the use of artificial intelligence, robotics, machine learning (ML), and deep learning (DL) can predict crop yields with a 75% probability, detect agricultural diseases that affect crop quality, and increase the profitability of farms [40]. In crop production, the transition from smart farming to the agricultural revolution 5.0 should be associated with more significant gain in yield, sustainability, and environmental conservation [41].…”

Section: Agricultural Revolutionmentioning

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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“…The use of sequential Gaussian Simulation for modelling water table spatial variables has been investigated and also measure the risk present in the shallow water tables is calculated as 95% [14]. Murugesan et al [15] demonstrate that crop prediction models may be achieved using ML with an accuracy of up to 75%. This paper achieved 100% prediction of crop production with 14 micronutrient soil features which can be used by expert advisory to harvesting stage from seed stage.…”

Section: Literature Reviewmentioning

confidence: 99%

A Machine Learning Approach for Stochastic Pattern Analysis for the Measurement of Time-Series Datasets

Ahmed¹,

Kamalakannan²,

Kavitha³

2022

I2M

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Time series data analysis is used in many domains of applications to perform efficient prediction, anomaly detection and forecasting using various techniques. Recently, there is a surge in demand for comprehensive data mining techniques that can be applied on agricultural prediction. Data mining techniques can be used effectively for predicting the agricultural growth using stochastic model sensing concept which is used to perform better analysis for predicting the dataset. The proposed work introduces a stochastic model that can be applied for agricultural data to predict the growth of any crops using primary time series datasets The framework has assisted in taking correct decisions to classify the dataset based on the threshold value of micro and macro nutrient obtained from National Food Security Mission (NFSM). This paper focus on analyzing the stochastic pattern that assist the proportion of micronutrient elements and also predicts the feature that affect the growth of agriculture by increase or decrease the level of micronutrient elements. Thus, the Expected Growth of Agriculture (EGA) has increased or decreased based on the strength of soil nutrients. Moreover, it will recommend chemical fertilizers and nutrients range to improvise the agriculture growth for each crop. The processed soil sample dataset based on the threshold value of micro nutrient level is obtained from National Food Security Mission (NFSM). There are several numerical illustrations that are performed for micronutrient data prediction as well as analyze the increase or decrease of growth level in agriculture based on micronutrient levels like Fe, Mn, Zn and Cu using Stochastic Weibull Distribution (SWD) model.

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Artificial Intelligence and Agriculture 5. 0

Cited by 22 publications

References 7 publications

Unleashing the value of artificial intelligence in the agri-food sector: where are we?

Unleashing the value of artificial intelligence in the agri-food sector: where are we?

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

A Machine Learning Approach for Stochastic Pattern Analysis for the Measurement of Time-Series Datasets

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