Analysis of demand forecasting of agriculture using machine learning algorithm

Balika J. Chelliah, T. P. Latchoumi and A. Senthilselvi ()
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Balika J. Chelliah: SRM Institute of Science and Technology
T. P. Latchoumi: SRM Institute of Science and Technology
A. Senthilselvi: SRM Institute of Science and Technology

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2024, vol. 26, issue 1, No 71, 1747 pages

Abstract: Abstract The state of India was situated on fertile land and river deltas with appropriate agricultural land. In 2019, agricultural fields, primarily cropland, occupy more than 40% of the country's total surface area. Moreover, agricultural industry revenues less than 3% of the country's Gross Provincial Product (GPP). While manufacturing has become the country's major financial activity, accounting countries represent half of GPP's revenues. The objective of the study is to find a way to improve the financial profitability and efficiency of farming supply chain networks as follows: (1) Fixation of national-level targets for zonal-level groups information after assessment and forecasts that affect production and distribution of agriculture. (2) Producers risk can be reduced by directing people to multiple factory and industrialization options based on market assessment. (3) Insurance costs and reduction of bank borrowing by standardizing the connection between bankers and producers using the structure to centralise land information. (4), Stabilize the agricultural sector by looking at nearby potential destinations for production and regulation of the Public Distribution System (PDS) flow for the security stock. In this paper, the novel ML target prediction algorithm to inform the farmers about the market target product and improve the relationships between the farmer and bankers for centralizing the information about recent government plans. The crop prediction ML algorithm proposed to improve the revenue of agriculture field.

Keywords: Crop rotation; ML algorithm; Farming sector; Crop cultivation; Tracking and Forecasting (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1007/s10668-022-02783-9

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