Data-Driven Soil Analysis and Evaluation for Smart Farming Using Machine Learning Approaches

IF 3.3 2区 农林科学 Q1 AGRONOMY Agriculture-Basel Pub Date : 2023-09-07 DOI:10.3390/agriculture13091777
Yixin Huang, Rishi Srivastava, Chloe Ngo, Jerry Gao, Jane Wu, Sen Chiao
{"title":"Data-Driven Soil Analysis and Evaluation for Smart Farming Using Machine Learning Approaches","authors":"Yixin Huang, Rishi Srivastava, Chloe Ngo, Jerry Gao, Jane Wu, Sen Chiao","doi":"10.3390/agriculture13091777","DOIUrl":null,"url":null,"abstract":"Food shortage issues affect more and more of the population globally as a consequence of the climate crisis, wars, and the COVID-19 pandemic. Increasing crop output has become one of the urgent priorities for many countries. To raise the productivity of the crop product, it is necessary to monitor and evaluate farmland soil quality by analyzing the physical and chemical properties of soil since the soil is the base to provide nutrition to the crop. As a result, soil analysis contributes greatly to maintaining the sustainability of soil in producing crops regularly. Recently, some agriculture researchers have started using machine learning approaches to conduct soil analysis, targeting the different soil analysis needs separately. The optimal method is to consider all those features (climate, soil chemicals, nutrition, and geolocations) based on the growing crops and production cycle for soil analysis. The contribution of this project is to combine soil analysis, including crop identification, irrigation recommendations, and fertilizer analysis, with data-driven machine learning models and to create an interactive user-friendly system (Soil Analysis System) by using real-time satellite data and remote sensor data. The system provides a more sustainable and efficient way to help farmers harvest with better usages of land, water, and fertilizer. According to our analysis results, this combined approach is promising and efficient for smart farming.","PeriodicalId":48587,"journal":{"name":"Agriculture-Basel","volume":null,"pages":null},"PeriodicalIF":3.3000,"publicationDate":"2023-09-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Agriculture-Basel","FirstCategoryId":"97","ListUrlMain":"https://doi.org/10.3390/agriculture13091777","RegionNum":2,"RegionCategory":"农林科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AGRONOMY","Score":null,"Total":0}
引用次数: 0

Abstract

Food shortage issues affect more and more of the population globally as a consequence of the climate crisis, wars, and the COVID-19 pandemic. Increasing crop output has become one of the urgent priorities for many countries. To raise the productivity of the crop product, it is necessary to monitor and evaluate farmland soil quality by analyzing the physical and chemical properties of soil since the soil is the base to provide nutrition to the crop. As a result, soil analysis contributes greatly to maintaining the sustainability of soil in producing crops regularly. Recently, some agriculture researchers have started using machine learning approaches to conduct soil analysis, targeting the different soil analysis needs separately. The optimal method is to consider all those features (climate, soil chemicals, nutrition, and geolocations) based on the growing crops and production cycle for soil analysis. The contribution of this project is to combine soil analysis, including crop identification, irrigation recommendations, and fertilizer analysis, with data-driven machine learning models and to create an interactive user-friendly system (Soil Analysis System) by using real-time satellite data and remote sensor data. The system provides a more sustainable and efficient way to help farmers harvest with better usages of land, water, and fertilizer. According to our analysis results, this combined approach is promising and efficient for smart farming.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
使用机器学习方法的智能农业数据驱动土壤分析和评估
由于气候危机、战争和2019冠状病毒病大流行,粮食短缺问题影响着全球越来越多的人口。提高作物产量已成为许多国家的当务之急之一。土壤是为作物提供营养的基础,为了提高作物产品的生产力,有必要通过分析土壤的理化性质对农田土壤质量进行监测和评价。因此,土壤分析有助于保持土壤的可持续性,定期生产作物。最近,一些农业研究人员开始使用机器学习方法进行土壤分析,分别针对不同的土壤分析需求。最优的方法是考虑所有这些特征(气候、土壤化学物质、营养和地理位置),基于作物生长和生产周期进行土壤分析。该项目的贡献是将土壤分析(包括作物识别、灌溉建议和肥料分析)与数据驱动的机器学习模型相结合,并通过使用实时卫星数据和遥感数据创建一个交互式用户友好系统(土壤分析系统)。该系统提供了一种更可持续、更有效的方式,帮助农民更好地利用土地、水和肥料来收获。根据我们的分析结果,这种组合方法对于智能农业来说是有希望的和有效的。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Agriculture-Basel
Agriculture-Basel Agricultural and Biological Sciences-Food Science
CiteScore
4.90
自引率
13.90%
发文量
1793
审稿时长
11 weeks
期刊介绍: Agriculture (ISSN 2077-0472) is an international and cross-disciplinary scholarly and scientific open access journal on the science of cultivating the soil, growing, harvesting crops, and raising livestock. We will aim to look at production, processing, marketing and use of foods, fibers, plants and animals. The journal Agriculturewill publish reviews, regular research papers, communications and short notes, and there is no restriction on the length of the papers. Our aim is to encourage scientists to publish their experimental and theoretical research in as much detail as possible. Full experimental and/or methodical details must be provided for research articles.
期刊最新文献
The Association of Socio-Economic Factors and Indigenous Crops on the Food Security Status of Farming Households in KwaZulu-Natal Province. Effects of Dietary Galla Chinensis Tannin Supplementation on Antioxidant Capacity and Intestinal Microbiota Composition in Broilers Genetic Variability of Oil Palm in Mexico: An Assessment Based on Microsatellite Markers Zinc Absorption through Leaves and Subsequent Translocation to the Grains of Bread Wheat after Foliar Spray Data-Driven Soil Analysis and Evaluation for Smart Farming Using Machine Learning Approaches
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1