Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming

Shaikh, Tawseef Ayoub; Rasool, Tabasum; Lone, Faisal Rasheed

doi:10.1016/j.compag.2022.107119

Cited by 303 publications

(105 citation statements)

References 127 publications

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“…Machine learning can be used to achieve artificial intelligence as it simulates the artificial neural network architecture that occurs in the human brain. The idea of machine learning is that it learns from new information, thereby generating new thought patterns (Shaikh et al, 2022). Similar to a human brain, machine learning allows for complex patterns to develop that lead to new outcomes.…”

Section: Main Subfields Of Artificial Intelligencementioning

confidence: 99%

Artificial Intelligence, Digital Trends and Globalization: Future Research Trends

Ratten

2024

FIIB Business Review

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Artificial intelligence is rapidly changing businesses based on digital trends and globalization. The aim of this article is to focus on emerging research trends regarding artificial intelligence that will influence global business management. This is significant as there have been rapid changes recently regarding artificial intelligence, including the surge in interest in generative forms. This article summarizes the trends taking place in the adoption process of artificial intelligence and what business managers need to do in order to increase their competitiveness. A brief history of how artificial intelligence has developed is stated, along with the main international business uses for artificial intelligence. Implications at the managerial and policy levels are stated that highlight the relevance and interesting nature of artificial intelligence in international business.

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Section: Main Subfields Of Artificial Intelligencementioning

confidence: 99%

Artificial Intelligence, Digital Trends and Globalization: Future Research Trends

Ratten

2024

FIIB Business Review

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“…Sustainable solutions can also be developed using data science, including efficient irrigation and fertilizer application, which can help minimize costs and optimize resource utilization while also improving crop yields. Finally, data science can support decision-making by generating evidence-based recommendations for interventions and management strategies [ 164 ].…”

Section: The Advantages and Applications Of Data Science In Plant Met...mentioning

confidence: 99%

Data Science and Plant Metabolomics

Kisiel

Krzemińska²,

Cembrowska-Lech

et al. 2023

Metabolites

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The study of plant metabolism is one of the most complex tasks, mainly due to the huge amount and structural diversity of metabolites, as well as the fact that they react to changes in the environment and ultimately influence each other. Metabolic profiling is most often carried out using tools that include mass spectrometry (MS), which is one of the most powerful analytical methods. All this means that even when analyzing a single sample, we can obtain thousands of data. Data science has the potential to revolutionize our understanding of plant metabolism. This review demonstrates that machine learning, network analysis, and statistical modeling are some techniques being used to analyze large quantities of complex data that provide insights into plant development, growth, and how they interact with their environment. These findings could be key to improving crop yields, developing new forms of plant biotechnology, and understanding the relationship between plants and microbes. It is also necessary to consider the constraints that come with data science such as quality and availability of data, model complexity, and the need for deep knowledge of the subject in order to achieve reliable outcomes.

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“…The results showed that as a result of water storage, the percentage of profit reduction is less than the percentage of water consumption reduction, and the presented models provide motivation for the farmer in water storage without much change in profit. Shaikh et al (2022), using linear programming methods, determined the optimal pattern of cultivation in the direction of water resources management in the Mashhad-Chenaran plain. The results showed that in the optimal model, the amount of water consumption decreased, and the cultivated area of crops, such as sugar beet, beans, and sunflower, could be increased.…”

Section: Introductionmentioning

confidence: 99%

A novel optimization approach for rural development based on sustainable agriculture planning, considering the energy and water consumption nexus

Luo

Chang

Kaliyaperumal

2023

Energy Exploration & Exploitation

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In today's world, there are many changes and transformations in different sectors of the economy, and today's world economy has become more dependent on each other. The economy of many developing countries is highly dependent on agriculture. In addition, in developed countries, the agricultural sector is of special importance, so they are currently turning to the production of more organic and healthy products. One of the reasons that caused the agricultural sector to receive more attention in the economy is the existential importance of this sector in providing the raw materials of a part of the industry and the food industry needed in that country. Accordingly, in this research, the optimal cultivation pattern is determined using a linear multi-objective mathematical model, including economic, social, and environmental objectives. Moreover, the uncertainty in the input parameters of the proposed model is considered, and a robust optimization approach is applied to deal with this uncertainty. After collecting the research data for the crop year 2021-2022, the proposed robust counterpart formulation is optimized using GAMS software. The numerical results show that increasing the level of conservatism in the robust model cause to decrease in the profit of agriculture as well as increasing the total consumed water. Moreover, the results of the current research can help the decision-makers of the agricultural sector in achieving the goal of obtaining the highest profit.

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Towards leveraging the role of machine learning and artificial intelligence in precision agriculture and smart farming

Cited by 303 publications

References 127 publications

Artificial Intelligence, Digital Trends and Globalization: Future Research Trends

Artificial Intelligence, Digital Trends and Globalization: Future Research Trends

Data Science and Plant Metabolomics

A novel optimization approach for rural development based on sustainable agriculture planning, considering the energy and water consumption nexus

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