Analysis of demand forecasting of agriculture using machine learning algorithm

Chelliah, Balika J.; Latchoumi, T. P.; Senthilselvi, A.

doi:10.1007/s10668-022-02783-9

Cited by 16 publications

(5 citation statements)

References 17 publications

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“…This real-time diagnosis and predictive analytics capability is crucial for early disease detection and effective crop management, ultimately contributing to better yield forecasting and resource allocation. Chelliah, Latchoumi, and Senthilselvi (2022) focus on the analysis of demand forecasting in agriculture using machine learning algorithms. Their study underscores the importance of accurate demand forecasting in agriculture, which is essential for optimizing production, reducing waste, and ensuring food security.…”

Section: Emerging Trends and Future Prospects In Ai For Agricultural ...mentioning

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: Emerging Trends and Future Prospects In Ai For Agricultural ...mentioning

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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“…Employing data analytics and Python's Machine Learning (ML) libraries facilitates demand prognostication [30]. This dynamic procedure converges historical sales data, seasonal patterns, and other relevant variables to yield forecasted demand-a fundamental input for the MRP calculations [31].…”

Section: Demand Predictionmentioning

confidence: 99%

Exploring Applications and Practical Examples by Streamlining Material Requirements Planning (MRP) with Python

Reis

2023

Logistics

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Background: Material Requirements Planning (MRP) is critical in Supply Chain Management (SCM), facilitating effective inventory management and meeting production demands in the manufacturing sector. Despite the potential benefits of automating the MRP tasks to meet the demand for expedited and efficient management, the field appears to be lagging behind in harnessing the advancements offered by Artificial Intelligence (AI) and sophisticated programming languages. Consequently, this study aims to address this gap by exploring the applications of Python in simplifying the MRP processes. Methods: This article offers a twofold approach: firstly, it conducts research to uncover the potential applications of the Python code in streamlining the MRP operations, and the practical examples serve as evidence of Python’s efficacy in simplifying the MRP tasks; secondly, this article introduces a conceptual framework that showcases the Python ecosystem, highlighting libraries and structures that enable efficient data manipulation, analysis, and optimization techniques. Results: This study presents a versatile framework that integrates a variety of Python tools, including but not limited to Pandas, Matplotlib, and Plotly, to streamline and actualize an 8-step MRP process. Additionally, it offers preliminary insights into the integration of the Python-based MRP solution (MRP.py) with Enterprise Resource Planning (ERP) systems. Conclusions: While the article focuses on demonstrating the practicality of Python in MRP, future endeavors will entail empirically integrating MRP.py with the ERP systems in small- and medium-sized companies. This integration will establish real-time data synchronization between the Python and ERP systems, leading to accurate MRP calculations and enhanced decision-making processes.

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“…On these timescales, one would be interested in the rate at which a cold front arrives or the progression and spread of particular thunderstorm formations. Because cloud platforms require very elevated simulations with horizontally spaced geographical units of the scale of 1 km [1][2][3], this is a difficult problem to solve. Because of this, short-range estimate techniques will invariably have a regional focus rather than a global approach when they are applied to processing capabilities.…”

Section: Introductionmentioning

confidence: 99%

Proper Weather Forecasting Internet of Things Sensor Framework with Machine Learning

Turukmane,

Pande

2024

EAI Endorsed Trans IoT

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Recent times have seen a rise in the amount of focus placed on the configurations of big data and the Internet of Things (IoT). The primary focus of the researchers was the development of big data analytics solutions based on machine learning. Machine learning is becoming more prevalent in this sector because of its ability to unearth hidden traits and patterns, even within exceedingly complicated datasets. This is one reason why this is the case. For the purpose of this study, we applied our Big Data and Internet of Things (IoT)-based system to a use case that involved the processing of weather information. We put climate clustering and sensor identification algorithms into practice by using data that was available to the general public. For this particular application, the execution information was shown as follows:every single level of the construction. The training method that we've decided to use for the package is a k-means cluster that's based on Scikit-Learn. According to the results of the information analyses, our strategy has the potential to be utilized in usefully retrieving information from a database that is rather complicated.

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Analysis of demand forecasting of agriculture using machine learning algorithm

Cited by 16 publications

References 17 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

Exploring Applications and Practical Examples by Streamlining Material Requirements Planning (MRP) with Python

Proper Weather Forecasting Internet of Things Sensor Framework with Machine Learning

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