Smart Agriculture Solutions: Harnessing AI and IoT for Crop Management

Alazzai, Waleed Khalid; Obaid, Mohammed Kadhim; Abood, Baydaa Sh.Z.; Jasim, Laith

doi:10.1051/e3sconf/202447700057

Cited by 2 publications

(1 citation statement)

References 14 publications

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“…This technological evolution has not only improved the efficiency of farming but also enhanced the quality of seeds, resources, irrigation, and fertilizers used, thereby increasing crop yields and farmers' profitability. Alazzai et al (2024) explore the integration of IoT, Cloud Computing, and AI in enhancing sustainable farming practices. The combination of these technologies enables real-time monitoring of agricultural conditions using sensors and drones, while Cloud Computing provides robust data processing capabilities.…”

Section: Milestones In Ai Integration Into Agriculturementioning

confidence: 99%

Ai-Driven Predictive Analytics in Agricultural Supply Chains: A Review: Assessing the Benefits and Challenges of Ai in Forecasting Demand and Optimizing Supply in Agriculture

Oluwafunmi Adijat Elufioye,

Chinedu Ugochukwu Ike,

Olubusola Odeyemi

et al. 2024

Comput. sci. IT res. j.

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This study provides a comprehensive review of the integration and impact of Artificial Intelligence (AI) in agricultural supply chains, focusing on its role in enhancing demand forecasting and optimizing supply. The primary objective was to assess how AI-driven predictive analytics transforms agricultural practices, addressing challenges, and shaping future trends. A systematic literature review and content analysis methodology were employed, utilizing academic databases and digital libraries to source peer-reviewed articles and conference papers published between 2014 and 2024. The inclusion criteria focused on studies related to AI applications in agricultural supply chains, while exclusion criteria filtered out non-peer-reviewed and irrelevant literature. Key findings reveal that AI significantly improves the accuracy and efficiency of demand forecasting and supply chain operations in agriculture. AI technologies, including machine learning and big data analytics, have led to advancements in real-time data analysis, predictive maintenance, and resource optimization. However, challenges such as data quality, infrastructure development, and skill gaps among agricultural professionals persist. The future landscape of AI in agriculture is marked by growth opportunities and challenges, including the need for equitable AI technology access and ethical considerations. The study recommends that industry leaders and policymakers invest in infrastructure, promote AI research and development, and provide training to facilitate AI adoption. Future research should focus on developing robust AI models tailored to agriculture, exploring AI's integration with emerging technologies, and assessing AI's long-term socio-economic impacts. This study contributes to understanding AI's current applications and future potential in transforming agricultural supply chains, offering valuable insights for stakeholders in the agricultural sector. Keywords: Artificial Intelligence, Agricultural Supply Chains, Predictive Analytics, Demand Forecasting.

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Section: Milestones In Ai Integration Into Agriculturementioning

confidence: 99%

Ai-Driven Predictive Analytics in Agricultural Supply Chains: A Review: Assessing the Benefits and Challenges of Ai in Forecasting Demand and Optimizing Supply in Agriculture

Oluwafunmi Adijat Elufioye,

Chinedu Ugochukwu Ike,

Olubusola Odeyemi

et al. 2024

Comput. sci. IT res. j.

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Harnessing Agricultural Data

Babu,

K.,

et al. 2024

Advances in Business Information Systems and Analytics

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This chapter emphasizes the Find S algorithm to explore the use of cutting-edge computational techniques to improve agri-business sustainability. Precision farming, data analytics, and machine learning are all combined in “agriculture 4.0” to maximize productivity and promote sustainability. Case examples from real-world situations are provided to illustrate the usefulness of using cutting-edge computational techniques in agriculture. The chapter also covers the value of cooperation and government assistance, tackles issues related to technological adoption, and provides solutions for broader acceptance in the farming community. This chapter intends to contribute to the continuing conversation on data-driven decision-making and productivity improvement in the agricultural sector by offering insightful information to researchers, practitioners, policymakers, and stakeholders who are interested in using computational methods to improve sustainability in the industry.

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Smart Agriculture Solutions: Harnessing AI and IoT for Crop Management

Cited by 2 publications

References 14 publications

Ai-Driven Predictive Analytics in Agricultural Supply Chains: A Review: Assessing the Benefits and Challenges of Ai in Forecasting Demand and Optimizing Supply in Agriculture

Ai-Driven Predictive Analytics in Agricultural Supply Chains: A Review: Assessing the Benefits and Challenges of Ai in Forecasting Demand and Optimizing Supply in Agriculture

Harnessing Agricultural Data

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