An image-based soil type classification method considering the impact of image acquisition distance factor

Wang, Dejiang; Si, Yuning; Shu, Zhan; Ai, Wu; Wu, Yajun; Li, Yifan

doi:10.1007/s11368-023-03474-2

Cited by 4 publications

(1 citation statement)

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“…The machine learning-based methods using digital image analysis have been widely applied as a non-contact and on-site indirect measurement approach in studying soil properties, such as the hydraulic conductivity of soils [18], soil roughness [19], soil type [20], soil texture [21], soil bulk density [22], and total soil nitrogen content [23]. Many studies have focused on utilizing soil surface images to estimate SWC [13,24].…”

Section: Introductionmentioning

confidence: 99%

Advancing Digital Image-Based Recognition of Soil Water Content: A Case Study in Bailu Highland, Shaanxi Province, China

Zhang,

Lan

et al. 2024

Water

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Soil water content (SWC) plays a vital role in agricultural management, geotechnical engineering, hydrological modeling, and climate research. Image-based SWC recognition methods show great potential compared to traditional methods. However, their accuracy and efficiency limitations hinder wide application due to their status as a nascent approach. To address this, we design the LG-SWC-R3 model based on an attention mechanism to leverage its powerful learning capabilities. To enhance efficiency, we propose a simple yet effective encoder–decoder architecture (PVP-Transformer-ED) designed on the principle of eliminating redundant spatial information from images. This architecture involves masking a high proportion of soil images and predicting the original image from the unmasked area to aid the PVP-Transformer-ED in understanding the spatial information correlation of the soil image. Subsequently, we fine-tune the SWC recognition model on the pre-trained encoder of the PVP-Transformer-ED. Extensive experimental results demonstrate the excellent performance of our designed model (R2 = 0.950, RMSE = 1.351%, MAPE = 0.081, MAE = 1.369%), surpassing traditional models. Although this method involves processing only a small fraction of original image pixels (approximately 25%), which may impact model performance, it significantly reduces training time while maintaining model error within an acceptable range. Our study provides valuable references and insights for the popularization and application of image-based SWC recognition methods.

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