Soil and Tillage Research, Journal Year: 2024, Volume and Issue: 248, P. 106397 - 106397
Published: Dec. 5, 2024
Language: Английский
Soil and Tillage Research, Journal Year: 2024, Volume and Issue: 248, P. 106397 - 106397
Published: Dec. 5, 2024
Language: Английский
Land, Journal Year: 2025, Volume and Issue: 14(2), P. 329 - 329
Published: Feb. 6, 2025
Soil organic matter (SOM) and total nitrogen (TN) are critical indicators for assessing soil fertility. Although laboratory chemical analysis methods can accurately measure their contents, these techniques time-consuming labor-intensive. Spectral technology, characterized by its high sensitivity convenience, has been increasingly integrated with machine learning algorithms nutrient monitoring. However, the process of spectral data remains complex requires further optimization simplicity efficiency to improve prediction accuracy. This study proposes a novel model enhance accuracy SOM TN predictions in northeast China’s black soil. Visible/Shortwave Near-Infrared Spectroscopy (Vis/SW-NIRS) within 350–1070 nm range were collected, preprocessed, dimensionality-reduced. The scores first nine principal components after partial least squares (PLS) dimensionality reduction selected as inputs, measured contents used outputs build back-propagation neural network (BPNN) model. results show that processed combination standard normal variate (SNV) multiple scattering correction (MSC) have best modeling performance. To stability this model, three named random search (RS), grid (GS), Bayesian (BO) introduced. demonstrate Vis/SW-NIRS provides reliable PLS-RS-BPNN achieving performance (R2 = 0.980 0.972, RMSE 1.004 0.006 TN, respectively). Compared traditional models such forests (RF), one-dimensional convolutional networks (1D-CNNs), extreme gradient boosting (XGBoost), proposed improves R2 0.164–0.344 predicting 0.257–0.314 respectively. These findings confirm potential technology effective tools prediction, offering valuable insights application sensing information.
Language: Английский
Citations
1CATENA, Journal Year: 2024, Volume and Issue: 245, P. 108312 - 108312
Published: Aug. 12, 2024
Language: Английский
Citations
5Geoderma, Journal Year: 2023, Volume and Issue: 438, P. 116657 - 116657
Published: Sept. 4, 2023
Soil health has gained increasing attention under the rapid development of industrialization and requirement for green agriculture. Therefore, up-to-date soil information related to is urgently needed ensure food security biodiversity protection. Previous studies have shown potential proximal sensing in measuring information, while it remains challenging get cost-efficient robust estimates multiple indicators simultaneously via sensor fusion. In this study, we investigated visible near-infrared (vis-NIR), mid-infrared (MIR) spectroscopy as well three model averaging methods predicting properties, including organic matter (SOM), pH, cation exchange capacity (CEC). The are not only used fusion but also high-level fusion, which include Granger-Ramanathan (GR), Bayesian Model Averaging Spectral-Guided Ensemble Modelling (S-GEM). Here, S-GEM a recently proposed algorithm that can improve spectroscopic prediction by spectral ensemble modelling. Four widely models were evaluated, partial least square regression, Cubist, memory based learning convolutional neural network. For SOM, on algorithms was comparable Sensorsingle + Modelmultiple (MIR singly S-GEM) with R2 0.86. However, MIR performed best among all (LCCC 0.92, RMSE 3.66 g kg−1 RPIQ 3.68). 10-fold cross-validation results indicated 0.84, LCCC 0.90, 0.45 3.65. CEC, Sensormultiple GR 0.66, 0.80, 3.48 cmol 2.22. Our showed failed when performance sensors differed lot (△R2 > 0.2), use single therefore suggested case. When close < recommended. outcome study provide reference determining validity domain improving accuracy prediction.
Language: Английский
Citations
13Environmental Sciences Europe, Journal Year: 2024, Volume and Issue: 36(1)
Published: April 21, 2024
Abstract Obtaining accurate spatial maps of soil organic carbon (SOC) in farmlands is crucial for assessing quality and achieving precision agriculture. The cropping system an important factor that affects the cycle farmlands, different agricultural managements under systems lead to heterogeneity SOC. However, current research often ignores differences main controlling factors SOC systems, especially when pattern complex, which not conducive farmland zoning management. This study aims (i) obtain distribution map six by using multi-phase HJ-CCD satellite images; (ii) explore stratified heterogeneous relationship between environmental variables Cubist model; (iii) predict Xiantao, Tianmen, Qianjiang cities, are core areas Jianghan Plain, were selected as area. Results showed content rice–wheat rotation was highest among systems. model outperformed random forest, ordinary kriging, multiple linear regression mapping. results system, climate, attributes, vegetation index influencing farmlands. different. Specifically, summer crop types had a greater influence on variations than winter crops. Paddy–upland more affected river distance NDVI, while upland–upland irrigation-related factors. work highlights differentiated provides data support can improve prediction accuracy complex
Language: Английский
Citations
5Remote Sensing, Journal Year: 2023, Volume and Issue: 15(23), P. 5571 - 5571
Published: Nov. 30, 2023
This paper explores the application and advantages of remote sensing, machine learning, mid-infrared spectroscopy (MIR) as a popular proximal sensing tool in estimation soil organic carbon (SOC). It underscores practical implications benefits integrated approach combining for SOC prediction across range applications, including comprehensive health mapping credit assessment. These advanced technologies offer promising pathway, reducing costs resource utilization while improving precision estimation. We conducted comparative analysis between MIR-predicted values laboratory-measured using 36 samples. The results demonstrate strong fit (R² = 0.83), underscoring potential this approach. While acknowledging that our is based on limited sample size, these initial findings promise serve foundation future research. will be providing updates when we obtain more data. Furthermore, commercialising Australia, with aim helping farmers harness markets. Based study’s findings, coupled insights from existing literature, suggest adopting measurement could significantly benefit local economies, enhance farmers’ ability to monitor changes health, promote sustainable agricultural practices. outcomes align global climate change mitigation efforts. approach, supported by other research, offers template regions worldwide seeking similar solutions.
Language: Английский
Citations
10Journal of Hydrology, Journal Year: 2025, Volume and Issue: unknown, P. 132980 - 132980
Published: Feb. 1, 2025
Language: Английский
Citations
0Scientific Data, Journal Year: 2025, Volume and Issue: 12(1)
Published: March 1, 2025
Abstract Since soil spectroscopy is considered to be a fast, simple, accurate and non-destructive analytical method, its application can integrated with wet analysis as an alternative. Therefore, development of national-level spectral libraries containing information about all types represented in country continuously increasing serve basis for calibrated predictive models capable assessing physical chemical parameters soils at multiple spatial scales. In this article, we present database laboratory visible-near infrared data legacy samples from the Hungarian Soil Degradation Observation System (HSDOS). The published set includes following measured 5,490 samples: pH KCl , organic matter (SOM), calcium carbonate (CaCO 3 ), total salt content (TSC), nitrogen (TN), soluble phosphorus (P 2 O 5 -AL), potassium (K O-AL), plasticity index according standard (PLI), profile depth reflectance between 350 2,500 nm wavelength. presented complement further related research on continental, national or regional scales support sustainable management.
Language: Английский
Citations
0Ecological Indicators, Journal Year: 2024, Volume and Issue: 165, P. 112246 - 112246
Published: June 14, 2024
Language: Английский
Citations
3Soil and Tillage Research, Journal Year: 2024, Volume and Issue: 246, P. 106357 - 106357
Published: Nov. 13, 2024
Language: Английский
Citations
2Computers and Electronics in Agriculture, Journal Year: 2023, Volume and Issue: 214, P. 108325 - 108325
Published: Oct. 14, 2023
Language: Английский
Citations
5