A Study on Experimental Analysis of Best Fit Machine Learning Approach for Smart Agriculture

被引：0

作者：

Lovesum J. ^{[1
]}

Prince B. ^{[2
]}

机构：

[1] Christ University, Bangalore

[2] Presidency University, Bangalore

来源：

SN Computer Science | / 4卷 / 2期

关键词：

Agriculture; AI techniques; Best fit; CNN; Deep learning; KNN; Machine learning; Random forest; SVM;

D O I：

10.1007/s42979-022-01612-0

中图分类号：

学科分类号：

摘要：

By 2050, the population is projected to exceed nine billion, necessitating a 70% increase in agricultural output to meet the need. Land, water, and other resources are running out due to the growing world population, making it impossible to maintain the demand–supply cycle. The yield of cultivation is also declining as a result of people's ignorance of the growing crop illnesses. Given that food is the most basic human requirement, future research should focus on revitalizing the agricultural sector. Farming may be made more productive for farmers by applying the right artificial intelligence technologies and datasets. Agronomics can benefit greatly from artificial intelligence. So that we can farm more effectively and be as productive as possible, we need to adopt a better strategy. The objective of this paper is to experimentally analyze the machine learning algorithms and methods already in use and forecast the most effective approach to use in each agricultural sector. In this article, we will present the challenges farmers face when using traditional farming methods and how artificial intelligence is revolutionizing agriculture by replacing the traditional methods. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

引用

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