Role of artificial intelligence, sensor technology, big data in agriculture: next-generation farming

Kumar, Pradeep; Singh, Abhishek; Rajput, Vishnu D.; Yadav, Ajit Kumar; Kumar, P. Pramod; Singh, Anil Kumar; Minkina, Tatiana

doi:10.1016/b978-0-323-89778-5.00035-0

Cited by 13 publications

(5 citation statements)

References 36 publications

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“…According to Leigh et al. (2002), the decision support system is an eclectic (romantic) style of decision making that leverages the efficacy of disparate sets of data/models/techniques as opposed to the classical approach and offers relatively enhanced performance accuracy for the postharvest market outlook (Kumar et al. , 2022).…”

Section: Introductionmentioning

confidence: 99%

“…According to Leigh et al (2002), the decision support system is an eclectic (romantic) style of decision making that leverages the efficacy of disparate sets of data/models/techniques as opposed to the classical approach and offers relatively enhanced performance accuracy for the postharvest market outlook (Kumar et al, 2022). In addition, formulation of price function for predictive modelling has been observed as a challenge for agricultural economists, particularly in the case of inefficient agriculture markets as in India and many developing nations.…”

Section: Introductionmentioning

confidence: 99%

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A farmer-centric agricultural decision support system for market dynamics in a volatile agricultural supply chain

Tripathi

Singh

et al. 2022

BIJ

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PurposeIn a volatile agricultural postharvest market, producers require more personalized information about market dynamics for informed decisions on the marketed surplus. However, this adaptive strategy fails to benefit them if the selection of a computational price predictive model to disseminate information on the market outlook is not efficient, and the associated risk of perishability, and storage cost factor are not assumed against the seemingly favourable market behaviour. Consequently, the decision of whether to store or sell at the time of crop harvest is a perennial dilemma to solve. With the intent of addressing this challenge for agricultural producers, the study is focused on designing an agricultural decision support system (ADSS) to suggest a favourable marketing strategy to crop producers.Design/methodology/approachThe present study is guided by an eclectic theoretical perspective from supply chain literature that included agency theory, transaction cost theory, organizational information processing theory and opportunity cost theory in revenue risk management. The paper models a structured iterative algorithmic framework that leverages the forecasting capacity of different time series and machine learning models, considering the effect of influencing factors on agricultural price movement for better forecasting predictability against market variability or dynamics. It also attempts to formulate an integrated risk management framework for effective sales planning decisions that factors in the associated costs of storage, rental and physical loss until the surplus is held for expected returns.FindingsEmpirical demonstration of the model was simulated on the dynamic markets of tomatoes, onions and potatoes in a north Indian region. The study results endorse that farmer-centric post-harvest information intelligence assists crop producers in the strategic sales planning of their produce, and also vigorously promotes that the effectiveness of decision making is contingent upon the selection of the best predictive model for every future market event.Practical implicationsAs a policy implication, the proposed ADSS addresses the pressing need for a robust marketing support system for the socio-economic welfare of farming communities grappling with distress sales, and low remunerative returns.Originality/valueBased on the extant literature studied, there is no such study that pays personalized attention to agricultural producers, enabling them to make a profitable sales decision against the volatile post-harvest market scenario. The present research is an attempt to fill that gap with the scope of addressing crop producer's ubiquitous dilemma of whether to sell or store at the time of harvesting. Besides, an eclectic and iterative style of predictive modelling has also a limited implication in the agricultural supply chain based on the literature; however, it is found to be a more efficient practice to function in a dynamic market outlook.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A farmer-centric agricultural decision support system for market dynamics in a volatile agricultural supply chain

Tripathi

Singh

et al. 2022

BIJ

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“…Furthermore, the trained model is applied to real-world scenarios to achieve predictions and decision support for future data.In summary, model establishment and evaluation are core aspects of big data analysis based on artificial intelligence technology, enabling accurate descriptions and predictions of data and providing scientific basis for decision-making. Through steps such as selecting models, optimizing model parameters, and evaluating model performance, high-quality and reliable data analysis results can be obtained, providing crucial support for intelligent big data applications [5].…”

Section: Model Establishment and Evaluationmentioning

confidence: 99%

Research on Big Data Analysis Methods Based on Artificial Intelligence Technology

2024

autml

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This paper aims to explore big data analysis methods based on artificial intelligence technology and analyze their applications in addressing challenges and issues in the real world. Firstly, an overview of big data analysis and artificial intelligence technology is provided, including their definitions, importance, and application areas. Secondly, a detailed discussion on big data analysis methods based on artificial intelligence technology is presented, covering aspects such as data preprocessing, feature extraction and selection, as well as model establishment and evaluation. Based on this foundation, the paper showcases the practical applications of these methods in domains such as healthcare, finance, and e-commerce through case studies. Lastly, the strengths and limitations of current methods are discussed, along with suggestions for future development directions and methodological improvements. This study aims to provide theoretical guidance and practical experience for research and practice in the field of big data analysis.

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“…Smart farming uses digital technologies to improve outsourcing, procuring, and planning in the supply chain [78], and it may help bring about the needed change in the food supply system that will satisfy the growing demand for sustainable food production globally. AI is believed to play a big role in next-generation farming because the innovative analysis can establish variations in crops and fields, leading to higher yields and less lateral loss of produce in the supply chain [79]. AI can also aid the transition to a drastic rather than small incremental change if a trans-disciplinary model is used to optimise the relationships between technology, humans, and the environment [80].…”

Section: Using Ai To Monitor and Optimise Food Production And Supply ...mentioning

confidence: 99%

Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review

Onyeaka,

Tamasiga,

Nwauzoma

et al. 2023

Sustainability

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Food waste is a global issue with significant economic, social, and environmental impacts. Addressing this problem requires a multifaceted approach; one promising avenue is using artificial intelligence (AI) technologies. This article explores the potential for AI to tackle food waste and enhance the circular economy and discusses the current state of food waste and the circular economy, highlighting specific ways that AI can be used to monitor and optimise food production and supply chains, redistribute excess food to those in need, and support circular economy initiatives. As a result, we can maximise resource efficiency and minimise environmental impact with these applications, ultimately creating a more sustainable and equitable food system.

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Role of artificial intelligence, sensor technology, big data in agriculture: next-generation farming

Cited by 13 publications

References 36 publications

A farmer-centric agricultural decision support system for market dynamics in a volatile agricultural supply chain

A farmer-centric agricultural decision support system for market dynamics in a volatile agricultural supply chain

Research on Big Data Analysis Methods Based on Artificial Intelligence Technology

Using Artificial Intelligence to Tackle Food Waste and Enhance the Circular Economy: Maximising Resource Efficiency and Minimising Environmental Impact: A Review

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