Machine Learning Enabled Crop Recommendation System for Arid Land

Batool Alsowaiq, Noura Almusaynid, Esra Albhnasawi, Wadha Alfenais, Suresh Sankaranarayanan
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Abstract

The agriculture industry plays a significant role in the economy of many countries, and the population is regarded as an essential profession. To increase agricultural production, crops are recommended based on soil, weather, humidity, rainfall, and other variables which are beneficial to farmers as well as the nation. This paper explores the use of “machine learning” algorithms to recommend crops in for Arid land based on features selected from tropical climate where crops grow effectively. Five “machine learning” models have been validated for recommendation of crops for arid land which resulted in “Random Forest” topping as the best model.
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基于机器学习的干旱地区作物推荐系统
农业在许多国家的经济中起着重要的作用,人口被认为是一个重要的职业。为了提高农业产量,根据土壤、天气、湿度、降雨量等对农民和国家都有利的变量来推荐作物。本文探索了使用“机器学习”算法,根据作物生长有效的热带气候特征,为干旱地区推荐作物。五个“机器学习”模型已经被验证用于推荐干旱地区的作物,结果“随机森林”成为最佳模型。
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