Crop yield forecasting using data mining

Pallavi Kamath, Pallavi Patil, Shrilatha S, Sushma, Sowmya S
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引用次数: 28

Abstract

India is a heavily reliant on agriculture. Organic, economic, and seasonal factors all influence agricultural yield. Estimating agricultural production is a difficult task for our country, particularly given the current population situation. Crop production assumptions made far in advance can help farmers make the necessary planning for things like storing and marketing. Crop production prediction involves a huge amount of data, making it a perfect candidate for data mining methods. Data mining is method of accumulating previously unseen anticipated information from vast database. Data mining assists in the analysis of future patterns and character, enabling companies to make informed decisions. For a specific region, this research provides a fast inspection of agricultural yield forecast using the Random Forest approach.

利用数据挖掘进行作物产量预测
印度是一个严重依赖农业的国家。有机因素、经济因素和季节因素都影响农业产量。估计农业产量对我国来说是一项艰巨的任务,特别是考虑到目前的人口状况。提前对作物产量的假设可以帮助农民对储存和销售等事情做出必要的规划。作物产量预测涉及大量数据,使其成为数据挖掘方法的完美候选。数据挖掘是从庞大的数据库中积累以前未见过的预期信息的方法。数据挖掘有助于分析未来的模式和特征,使公司能够做出明智的决策。针对特定区域,本研究提供了一种利用随机森林方法对农业产量预测进行快速检验的方法。
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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