Soil Organic Carbon Prediction Based on Vis–NIR Spectral Classification Data Using GWPCA–FCM Algorithm

Sensors, Год журнала: 2024, Номер 24(15), С. 4930 - 4930

Опубликована: Июль 30, 2024

Soil visible and near-infrared reflectance spectroscopy is an effective tool for the rapid estimation of soil organic carbon (SOC). The development spectroscopic technology has increased application spectral libraries SOC research. However, direct prediction remains challenging due to high variability in types soil-forming factors. This study aims address this challenge by improving accuracy through classification. We utilized European Land Use Cover Area frame Survey (LUCAS) large-scale library employed a geographically weighted principal component analysis (GWPCA) combined with fuzzy c-means (FCM) clustering algorithm classify spectra. Subsequently, we used partial least squares regression (PLSR) Cubist model prediction. Additionally, classified data land cover compared classification results those obtained from showed that (1) GWPCA-FCM-Cubist yielded best predictions, average R2 = 0.83 RPIQ 2.95, representing improvements 10.33% 18.00% RPIQ, respectively, unclassified full sample modeling. (2) modeling based on GWPCA-FCM was significantly superior type Specifically, there 7.64% 14.22% improvement under PLSR, 13.36% 29.10% Cubist. (3) Overall, models better than PLSR models. These findings indicate GWPCA FCM conjunction technique can enhance libraries.

Язык: Английский

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Deep Learning CNN-Based Architecture Applied to Intelligent Near-Infrared Analysis of Water Pollution from Agricultural Irrigation Resources

Smart innovation, systems and technologies, Год журнала: 2024, Номер unknown, С. 65 - 74

Опубликована: Янв. 1, 2024

Язык: Английский

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Winter Wheat SPAD Prediction Based on Multiple Preprocessing, Sequential Module Fusion, and Feature Mining Methods

Agriculture, Год журнала: 2024, Номер 14(12), С. 2258 - 2258

Опубликована: Дек. 10, 2024

Chlorophyll is a crucial indicator for monitoring crop growth and assessing nutritional status. Hyperspectral remote sensing plays an important role in precision agriculture, offering non-destructive approach to predicting leaf chlorophyll. However, canopy spectra often face background noise data redundancy challenges. To tackle these issues, this study develops integrated processing strategy incorporating multiple preprocessing techniques, sequential module fusion, feature mining methods. Initially, the original spectrum (OS) from 2021, 2022, fusion year underwent through Fast Fourier Transform (FFT) smoothing, scattering correction (MSC), first derivative (FD), second (SD). Secondly, was conducted using Competitive Adaptive Reweighted Sampling (CARS), Iterative Retention of Information Variables (IRIV), Principal Component Analysis (PCA) based on optimal order data. Finally, Partial Least Squares Regression (PLSR) used construct prediction model winter wheat SPAD compare effects different years stages. The findings show that FFT-MSC (firstly pre-processing FFT, secondly secondary FFT spectral MSC) effectively reduced issues such as noisy signals baseline drift. FFT-MSC-IRIV-PLSR (based combined preprocessed data, screening IRIV, then combining with PLSR model) predicts highest overall accuracy, R2 0.79–0.89, RMSE 4.51–5.61, MAE 4.01–4.43. performed best 0.84–0.89 4.51–6.74. during stages occurred early filling stage, 0.75 0.58. On basis research, future work will focus optimizing process richer environmental so further enhance predictive capability applicability model.

Язык: Английский

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Construction of Hyperspectral Reflectance and Spectral Exponential Inversion Model for the Water Content of Catalpa Bungei Leaves

Опубликована: Янв. 1, 2023

Real-time monitoring of leaf water content is an important indicator drought resistance in plants. In this study, hyperspectral reflectance and derived data are used to build inversion model for Catalpa bungei. Rapid, non-destructive real-time provides a high-throughput method assessing tree seedlings. The mature leaves were determined several models built evaluate the optimal combination using different variable selection construction methods. results show that PLS regression constructed with as input best test series. MC-UVE all models. With method, approach optimal. MC-UVE-PLS set coefficient (R2) maximum (0.7903) , mean square root error (RMSE) minimum (1.7352). SR (1466nm, 2128nm) spectral index highest correlation. First order differencing can effectively improve correlation between content, but cannot be optimised. Using screening was which technical support

Язык: Английский

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Recent Trends on the use of Infrared Spectroscopy for Soil Assessment

Journal of Biomedical Research & Environmental Sciences, Год журнала: 2023, Номер 4(11), С. 1618 - 1623

Опубликована: Ноя. 1, 2023

Infrared spectroscopy has emerged as a powerful tool to assess soil properties for both environmental science and agriculture. Here, we explore its recent trends developments assessment. This technique is an alternative that counters the limitations of traditional laboratory methods, offering cost-effective non-destructive approach. latest in innovation landscape infrared assessment are explored, providing insights on broad range applications into future trajectory this technology. Firstly, delve agriculture, highlighting potential prediction many attributes. Next, carbon assessment, emphasizing importance estimating organic stock quality. Soil pollution elemental contents addressed, focusing potentially toxic elements concentrations soil, strongly relevant monitoring. emerges valuable rapid non-hazardous content physical prediction, traditionally limited texture analysis, extended through application novel approaches, shedding light broader technology quality The ongoing statistical modeling technological also showcased, mainly focused machine learning methods. Lastly, spectral libraries emphasized, such Global Spectral Calibration Library Estimation Service, Brazilian Library. In conclusion, become important multitude across agricultural contexts. review underscores growing advancing standardization reproducibility sustainable procedures, ensuring brighter science.

Язык: Английский

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