Research into the Application of ResNet in Soil: A Review
With the rapid advancement of deep learning technology, the residual networks technique (ResNet) has made significant strides in the field of image processing, and its application in soil science has been steadily increasing. ResNet outperforms traditional methods by effectively mitigating the vanis...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-03-01
|
| Series: | Agriculture |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2077-0472/15/6/661 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1849392761497714688 |
|---|---|
| author | Wenjie Wu Lijuan Huo Gaiqiang Yang Xin Liu Hongxia Li |
| author_facet | Wenjie Wu Lijuan Huo Gaiqiang Yang Xin Liu Hongxia Li |
| author_sort | Wenjie Wu |
| collection | DOAJ |
| description | With the rapid advancement of deep learning technology, the residual networks technique (ResNet) has made significant strides in the field of image processing, and its application in soil science has been steadily increasing. ResNet outperforms traditional methods by effectively mitigating the vanishing gradient problem, enabling deeper network training, enhancing feature extraction, and improving accuracy in complex pattern recognition tasks. ResNet, as an efficient deep learning model, can automatically extract features from complex soil image data, enabling accurate soil classification and assessment of soil health. Recent research is increasingly applying ResNet to various fields, including soil type classification and health assessment. Firstly, this manuscript outlines various methods for collecting soil data, highlighting the significance of employing diverse data sources to comprehensively understand soil characteristics. These methods include the acquisition of soil microscopic images, which provide high-resolution insights into the soil’s particulate structure at the cellular level; remote sensing images, which offer valuable information regarding large-scale soil properties and spatial variations through satellite or drone-based technologies; and high-definition images, which capture fine-scale details of soil features, enabling more precise and detailed analysis. By integrating these techniques, a solid foundation is established for subsequent soil image analysis, thereby enhancing the accuracy of soil classification, health assessments, and environmental impact evaluations. Furthermore, this approach contributes to advancements in precision agriculture, land use planning, soil erosion monitoring, and contamination detection, ultimately supporting sustainable soil management and ecological conservation efforts. Then, the advantages of using ResNet in soil science are analyzed, and its performance across different soil image processing tasks is explored. Finally, potential future development directions are proposed. |
| format | Article |
| id | doaj-art-37471aa48fe24a50bb15e33afefaff4f |
| institution | Kabale University |
| issn | 2077-0472 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Agriculture |
| spelling | doaj-art-37471aa48fe24a50bb15e33afefaff4f2025-08-20T03:40:42ZengMDPI AGAgriculture2077-04722025-03-0115666110.3390/agriculture15060661Research into the Application of ResNet in Soil: A ReviewWenjie Wu0Lijuan Huo1Gaiqiang Yang2Xin Liu3Hongxia Li4School of Environment and Resources, Taiyuan University of Science and Technology, Taiyuan 030024, ChinaSchool of Environment and Resources, Taiyuan University of Science and Technology, Taiyuan 030024, ChinaSchool of Environment and Resources, Taiyuan University of Science and Technology, Taiyuan 030024, ChinaSchool of Environment and Resources, Taiyuan University of Science and Technology, Taiyuan 030024, ChinaSchool of Environment and Resources, Taiyuan University of Science and Technology, Taiyuan 030024, ChinaWith the rapid advancement of deep learning technology, the residual networks technique (ResNet) has made significant strides in the field of image processing, and its application in soil science has been steadily increasing. ResNet outperforms traditional methods by effectively mitigating the vanishing gradient problem, enabling deeper network training, enhancing feature extraction, and improving accuracy in complex pattern recognition tasks. ResNet, as an efficient deep learning model, can automatically extract features from complex soil image data, enabling accurate soil classification and assessment of soil health. Recent research is increasingly applying ResNet to various fields, including soil type classification and health assessment. Firstly, this manuscript outlines various methods for collecting soil data, highlighting the significance of employing diverse data sources to comprehensively understand soil characteristics. These methods include the acquisition of soil microscopic images, which provide high-resolution insights into the soil’s particulate structure at the cellular level; remote sensing images, which offer valuable information regarding large-scale soil properties and spatial variations through satellite or drone-based technologies; and high-definition images, which capture fine-scale details of soil features, enabling more precise and detailed analysis. By integrating these techniques, a solid foundation is established for subsequent soil image analysis, thereby enhancing the accuracy of soil classification, health assessments, and environmental impact evaluations. Furthermore, this approach contributes to advancements in precision agriculture, land use planning, soil erosion monitoring, and contamination detection, ultimately supporting sustainable soil management and ecological conservation efforts. Then, the advantages of using ResNet in soil science are analyzed, and its performance across different soil image processing tasks is explored. Finally, potential future development directions are proposed.https://www.mdpi.com/2077-0472/15/6/661residual networksoil image analysissoil classificationsoil health assessment |
| spellingShingle | Wenjie Wu Lijuan Huo Gaiqiang Yang Xin Liu Hongxia Li Research into the Application of ResNet in Soil: A Review Agriculture residual network soil image analysis soil classification soil health assessment |
| title | Research into the Application of ResNet in Soil: A Review |
| title_full | Research into the Application of ResNet in Soil: A Review |
| title_fullStr | Research into the Application of ResNet in Soil: A Review |
| title_full_unstemmed | Research into the Application of ResNet in Soil: A Review |
| title_short | Research into the Application of ResNet in Soil: A Review |
| title_sort | research into the application of resnet in soil a review |
| topic | residual network soil image analysis soil classification soil health assessment |
| url | https://www.mdpi.com/2077-0472/15/6/661 |
| work_keys_str_mv | AT wenjiewu researchintotheapplicationofresnetinsoilareview AT lijuanhuo researchintotheapplicationofresnetinsoilareview AT gaiqiangyang researchintotheapplicationofresnetinsoilareview AT xinliu researchintotheapplicationofresnetinsoilareview AT hongxiali researchintotheapplicationofresnetinsoilareview |