A Novel Bio-inspired Optimization Framework for Effective Crop Land Allocation and Utilization

N. Thilagavathi, Swamynathan Ramakrishnan, T. Amudha
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引用次数: 2

Abstract

Land availability for agriculture has become scarce in the present technological world. Effective usage of the cultivable land is constrained due to less availability of water and cost-effective land allocation for cultivation has turned out more challenging. Crop planning optimization is termed as a combinatorial optimization problem, in which effective utilization of land plays a major role. The primary objective of this paper is to perform optimal land allocation to improve agricultural income. Ant colony optimization algorithm (ACO), Social Spider Algorithm (SSA), and LINGO global solver are applied for optimizing the agricultural land in this work. The study area chosen for this research is agricultural areas in Coimbatore, situated in Tamilnadu state, India. This research outcome has shown that planned land allocation through optimization algorithms could significantly increase the profit in agriculture to a considerable extent.
一种新的基于生物的作物土地有效配置和利用优化框架
在当今技术发达的世界里,农业用地已变得稀缺。水资源短缺制约了耕地的有效利用,经济有效地分配耕地也变得更加困难。作物规划优化是一个组合优化问题,其中土地的有效利用起着重要作用。本文的主要目标是通过优化土地配置来提高农业收入。采用蚁群优化算法(ACO)、社会蜘蛛算法(SSA)和LINGO全局求解器对农地进行优化。本研究选择的研究区域是位于印度泰米尔纳德邦哥印拜陀的农业区。研究结果表明,通过优化算法进行土地规划配置,可以在相当程度上显著提高农业利润。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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