The Discrete Taxonomic Classification of Soils Subjected to Diverse Treatment Modalities and Varied Fertility Grades Utilizing Machine Olfaction
Soil classification stands as a pivotal aspect in the domain of agricultural practices and environmental research, wielding substantial influence over decisions related to real-time soil management and precision agriculture. Nevertheless, traditional methods of assessing soil conditions, primarily g...
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| Format: | Article |
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MDPI AG
2024-02-01
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| Series: | Agriculture |
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| Online Access: | https://www.mdpi.com/2077-0472/14/2/291 |
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| author | Shuyan Liu Xuegeng Chen Dongyan Huang Jingli Wang Xinming Jiang Xianzhang Meng Xiaomei Gao |
| author_facet | Shuyan Liu Xuegeng Chen Dongyan Huang Jingli Wang Xinming Jiang Xianzhang Meng Xiaomei Gao |
| author_sort | Shuyan Liu |
| collection | DOAJ |
| description | Soil classification stands as a pivotal aspect in the domain of agricultural practices and environmental research, wielding substantial influence over decisions related to real-time soil management and precision agriculture. Nevertheless, traditional methods of assessing soil conditions, primarily grounded in labor-intensive chemical analyses, confront formidable challenges marked by substantial resource demands and spatial coverage limitations. This study introduced a machine olfaction methodology crafted to emulate the capabilities of the human olfactory system, providing a cost-effective alternative. In the initial phase, volatile gases produced during soil pyrolysis were propelled into a sensor array comprising 10 distinct gas sensors to monitor changes in gas concentration. Following the transmission of response data, nine eigenvalues were derived from the response curve of each sensor. Given the disparate sample counts for the two distinct classification criteria, this computational procedure yields two distinct eigenspaces, characterized by dimensions of 112 or 114 soil samples, each multiplied by 10 sensors and nine eigenvalues. The determination of the optimal feature space was guided by the “overall feature information” derived from mutual information. Ultimately, the inclusion of random forest (RF), multi-layer perceptron (MLP), and multi-layer perceptron combined with random forest (MLP-RF) models was employed to classify soils under four treatments (tillage and straw management) and three fertility grades. The assessment of model performance involved metrics such as overall accuracy (OA) and the Kappa coefficient. The findings revealed that the optimal classification model, MLP-RF, achieved impeccable performance with an OA of 100.00% in classifying soils under both criteria, which showed almost perfect agreement with the actual results. The approach proposed in this study provided near-real-time data on the condition of the soil and opened up new possibilities for advancing precision agriculture management. |
| first_indexed | 2024-03-07T22:46:35Z |
| format | Article |
| id | doaj.art-6523c5c813864378b6b243ef8204b716 |
| institution | Directory Open Access Journal |
| issn | 2077-0472 |
| language | English |
| last_indexed | 2024-03-07T22:46:35Z |
| publishDate | 2024-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj.art-6523c5c813864378b6b243ef8204b7162024-02-23T15:03:49ZengMDPI AGAgriculture2077-04722024-02-0114229110.3390/agriculture14020291The Discrete Taxonomic Classification of Soils Subjected to Diverse Treatment Modalities and Varied Fertility Grades Utilizing Machine OlfactionShuyan Liu0Xuegeng Chen1Dongyan Huang2Jingli Wang3Xinming Jiang4Xianzhang Meng5Xiaomei Gao6Key Laboratory of Bionics Engineering, Ministry of Education, Jilin University, Changchun 130022, ChinaCollege of Mechanical and Electrical Engineering, Shihezi University, Shihezi 832003, ChinaThe College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, ChinaThe College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, ChinaThe College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, ChinaThe College of Engineering and Technology, Jilin Agricultural University, Changchun 130118, ChinaKey Laboratory of Bionics Engineering, Ministry of Education, Jilin University, Changchun 130022, ChinaSoil classification stands as a pivotal aspect in the domain of agricultural practices and environmental research, wielding substantial influence over decisions related to real-time soil management and precision agriculture. Nevertheless, traditional methods of assessing soil conditions, primarily grounded in labor-intensive chemical analyses, confront formidable challenges marked by substantial resource demands and spatial coverage limitations. This study introduced a machine olfaction methodology crafted to emulate the capabilities of the human olfactory system, providing a cost-effective alternative. In the initial phase, volatile gases produced during soil pyrolysis were propelled into a sensor array comprising 10 distinct gas sensors to monitor changes in gas concentration. Following the transmission of response data, nine eigenvalues were derived from the response curve of each sensor. Given the disparate sample counts for the two distinct classification criteria, this computational procedure yields two distinct eigenspaces, characterized by dimensions of 112 or 114 soil samples, each multiplied by 10 sensors and nine eigenvalues. The determination of the optimal feature space was guided by the “overall feature information” derived from mutual information. Ultimately, the inclusion of random forest (RF), multi-layer perceptron (MLP), and multi-layer perceptron combined with random forest (MLP-RF) models was employed to classify soils under four treatments (tillage and straw management) and three fertility grades. The assessment of model performance involved metrics such as overall accuracy (OA) and the Kappa coefficient. The findings revealed that the optimal classification model, MLP-RF, achieved impeccable performance with an OA of 100.00% in classifying soils under both criteria, which showed almost perfect agreement with the actual results. The approach proposed in this study provided near-real-time data on the condition of the soil and opened up new possibilities for advancing precision agriculture management.https://www.mdpi.com/2077-0472/14/2/291machine olfactionsoil classificationsoil treatmentssoil fertility gradesmachine learning |
| spellingShingle | Shuyan Liu Xuegeng Chen Dongyan Huang Jingli Wang Xinming Jiang Xianzhang Meng Xiaomei Gao The Discrete Taxonomic Classification of Soils Subjected to Diverse Treatment Modalities and Varied Fertility Grades Utilizing Machine Olfaction Agriculture machine olfaction soil classification soil treatments soil fertility grades machine learning |
| title | The Discrete Taxonomic Classification of Soils Subjected to Diverse Treatment Modalities and Varied Fertility Grades Utilizing Machine Olfaction |
| title_full | The Discrete Taxonomic Classification of Soils Subjected to Diverse Treatment Modalities and Varied Fertility Grades Utilizing Machine Olfaction |
| title_fullStr | The Discrete Taxonomic Classification of Soils Subjected to Diverse Treatment Modalities and Varied Fertility Grades Utilizing Machine Olfaction |
| title_full_unstemmed | The Discrete Taxonomic Classification of Soils Subjected to Diverse Treatment Modalities and Varied Fertility Grades Utilizing Machine Olfaction |
| title_short | The Discrete Taxonomic Classification of Soils Subjected to Diverse Treatment Modalities and Varied Fertility Grades Utilizing Machine Olfaction |
| title_sort | discrete taxonomic classification of soils subjected to diverse treatment modalities and varied fertility grades utilizing machine olfaction |
| topic | machine olfaction soil classification soil treatments soil fertility grades machine learning |
| url | https://www.mdpi.com/2077-0472/14/2/291 |
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