Amol D. Vibhute, Karbhari V. Kale, Sandeep V. Gaikwad
{"title":"Machine learning-enabled soil classification for precision agriculture: a study on spectral analysis and soil property determination","authors":"Amol D. Vibhute, Karbhari V. Kale, Sandeep V. Gaikwad","doi":"10.1007/s12518-023-00546-3","DOIUrl":null,"url":null,"abstract":"<div><p>Surface soil type classification is essential to enhance food production in precision farming. However, soil classification is time-consuming, laborious, and costly through the traditional methods. Recently, artificial intelligence-based methods, especially machine learning, have played a vigorous role in soil classification and its mapping. However, machine learning still makes exterior soil type classification and its mapping difficult due to various features and spatio-temporal inconsistencies. Therefore, the present study has tried to determine soil properties and sort accordingly using hyperspectral datasets and machine learning methods. We used field spectra generated by ASD Field Spec 4 device and satellite image. The proposed approach has identified three prominent soil types, <i>Regur</i> soil, <i>Lateritic</i> soil, and <i>sand dunes</i> according to soil taxonomy, with more than 95% success rate using satellite hyperspectral image and machine learning models. Thus, the outcome of the present study can be effectively utilized in healthy agricultural practices to increase global food production. In addition, the suggested strategy can be used in precision agriculture and environmental management.</p></div>","PeriodicalId":46286,"journal":{"name":"Applied Geomatics","volume":null,"pages":null},"PeriodicalIF":2.3000,"publicationDate":"2024-01-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Geomatics","FirstCategoryId":"1085","ListUrlMain":"https://link.springer.com/article/10.1007/s12518-023-00546-3","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"REMOTE SENSING","Score":null,"Total":0}
引用次数: 0
Abstract
Surface soil type classification is essential to enhance food production in precision farming. However, soil classification is time-consuming, laborious, and costly through the traditional methods. Recently, artificial intelligence-based methods, especially machine learning, have played a vigorous role in soil classification and its mapping. However, machine learning still makes exterior soil type classification and its mapping difficult due to various features and spatio-temporal inconsistencies. Therefore, the present study has tried to determine soil properties and sort accordingly using hyperspectral datasets and machine learning methods. We used field spectra generated by ASD Field Spec 4 device and satellite image. The proposed approach has identified three prominent soil types, Regur soil, Lateritic soil, and sand dunes according to soil taxonomy, with more than 95% success rate using satellite hyperspectral image and machine learning models. Thus, the outcome of the present study can be effectively utilized in healthy agricultural practices to increase global food production. In addition, the suggested strategy can be used in precision agriculture and environmental management.
期刊介绍:
Applied Geomatics (AGMJ) is the official journal of SIFET the Italian Society of Photogrammetry and Topography and covers all aspects and information on scientific and technical advances in the geomatics sciences. The Journal publishes innovative contributions in geomatics applications ranging from the integration of instruments, methodologies and technologies and their use in the environmental sciences, engineering and other natural sciences.
The areas of interest include many research fields such as: remote sensing, close range and videometric photogrammetry, image analysis, digital mapping, land and geographic information systems, geographic information science, integrated geodesy, spatial data analysis, heritage recording; network adjustment and numerical processes. Furthermore, Applied Geomatics is open to articles from all areas of deformation measurements and analysis, structural engineering, mechanical engineering and all trends in earth and planetary survey science and space technology. The Journal also contains notices of conferences and international workshops, industry news, and information on new products. It provides a useful forum for professional and academic scientists involved in geomatics science and technology.
Information on Open Research Funding and Support may be found here: https://www.springernature.com/gp/open-research/institutional-agreements