A Decision Support Tool for District Level Planning of Agricultural Crops for Maximizing Profits of Farmers

Bhardwaj, MR and Chaudhary, A and Enaganti, I and Sagar, K and Narahari, Y (2023) A Decision Support Tool for District Level Planning of Agricultural Crops for Maximizing Profits of Farmers. In: 19th IEEE International Conference on Automation Science and Engineering, CASE 2023Auckland26 August 2023through 30 August 2023Code 193105, 19th IEEE International Conference on Automation Science and Engineering, CASE 2023, Auckland.

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Abstract

A mismatch between the crops produced by farmers and the respective market demands leads to large-scale crop dumping. Year on year, this leads to wastage as well as huge financial losses for the farmers. To alleviate this problem, we address the macro-level problem of district level agricultural crop planning. Our interest is in how the Government or any state administration could make an informed recommendation on which crop acreages (number of acres cultivated under each crop in each district) to grow in which districts or geospatial regions in a given state or country, so as to match the demand for the crops and maximize the profits of the farmers. In this paper, we develop a tool CROP-S (CROp Planning System) to determine an assignment of crop acreages to districts so as to maximize the profits of the farmers while simultaneously ensuring required crop security levels for each district. The tool uses data about predicted demands, transportation costs, compliance ratios, and historical data about yields and prices to arrive at a nearly optimal assignment of crop acreages to districts. CROP-S uses a methodology based on genetic algorithms. We believe CROP-S will provide an effective decision support tool for the Government to issue crop recommendations to the district administrations, who in turn issue advisories to farmers. To demonstrate the effectiveness of CROP-S, we have considered the problem of allocating crop acreages to the 30 districts of Karnataka state in India. We have used real-world data on the top 7 crops grown in Karnataka. We show that assigning crop acreages among the various districts, as recommended by CROP-S, leads to a significant increase in the overall profits of the farmers. © 2023 IEEE.

Item Type:	Conference Paper
Publication:	IEEE International Conference on Automation Science and Engineering
Publisher:	IEEE Computer Society
Additional Information:	The copyright for this article belongs to the IEEE Computer Society.
Keywords:	Decision support systems; Genetic algorithms; Losses; Profitability; Zoning, Agricultural crops; Crop planning; Decision supports; Financial loss; Karnataka; Large-scales; Market demand; Planning systems; State administration; Support tool, Crops
Department/Centre:	Division of Electrical Sciences > Computer Science & Automation
Date Deposited:	03 Dec 2023 06:30
Last Modified:	03 Dec 2023 06:30
URI:	https://eprints.iisc.ac.in/id/eprint/83463

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