Research into the Application of ResNet in Soil: A Review

Wenjie Wu, Lijuan Huo, Gaiqiang Yang (), Xin Liu and Hongxia Li
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Wenjie Wu: School of Environment and Resources, Taiyuan University of Science and Technology, Taiyuan 030024, China
Lijuan Huo: School of Environment and Resources, Taiyuan University of Science and Technology, Taiyuan 030024, China
Gaiqiang Yang: School of Environment and Resources, Taiyuan University of Science and Technology, Taiyuan 030024, China
Xin Liu: School of Environment and Resources, Taiyuan University of Science and Technology, Taiyuan 030024, China
Hongxia Li: School of Environment and Resources, Taiyuan University of Science and Technology, Taiyuan 030024, China

Agriculture, 2025, vol. 15, issue 6, 1-29

Abstract: 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.

Keywords: residual network; soil image analysis; soil classification; soil health assessment (search for similar items in EconPapers)
JEL-codes: Q1 Q10 Q11 Q12 Q13 Q14 Q15 Q16 Q17 Q18 (search for similar items in EconPapers)
Date: 2025
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