Transfer learning in environmental remote sensing
Abstract Machine learning (ML) has proven to be a powerful tool for utilizing the rapidly
increasing amounts of remote sensing data for environmental monitoring. Yet ML models …
increasing amounts of remote sensing data for environmental monitoring. Yet ML models …
Review of deep learning-based methods for non-destructive evaluation of agricultural products
Deep Learning (DL) has emerged as a pivotal modelling tool in various domains because of
its proficiency in learning distributed representations. Numerous DL algorithms have …
its proficiency in learning distributed representations. Numerous DL algorithms have …
Diffuse reflectance spectroscopy for estimating soil properties: A technology for the 21st century
RA Viscarra Rossel, T Behrens… - European Journal of …, 2022 - Wiley Online Library
Spectroscopic measurements of soil samples are reliable because they are highly
repeatable and reproducible. They characterise the samples' mineral–organic composition …
repeatable and reproducible. They characterise the samples' mineral–organic composition …
[HTML][HTML] Spectral fusion modeling for soil organic carbon by a parallel input-convolutional neural network
Abstract Visible-to-near-infrared (vis–NIR) and mid-infrared (MIR) spectroscopy have been
widely utilized for the quantitative estimation of soil organic carbon (SOC). The fusion of vis …
widely utilized for the quantitative estimation of soil organic carbon (SOC). The fusion of vis …
Hyperspectral-to-image transform and CNN transfer learning enhancing soybean LCC estimation
Leaf chlorophyll content (LCC) is a distinct indicator of crop health status used to estimate
nutritional stress, diseases, and pests. Thus, accurate LCC information can assist in the …
nutritional stress, diseases, and pests. Thus, accurate LCC information can assist in the …
A comparison of multiple deep learning methods for predicting soil organic carbon in Southern Xinjiang, China
Soil organic carbon (SOC) plays an important role in soil functioning and also global C
balance. Visible-near-infrared (Vis-NIR) spectroscopy can be regarded as a cost-effective …
balance. Visible-near-infrared (Vis-NIR) spectroscopy can be regarded as a cost-effective …
Towards optimal variable selection methods for soil property prediction using a regional soil vis-nir spectral library
Soil visible and near-infrared (Vis-NIR, 350–2500 nm) spectroscopy has been proven as an
alternative to conventional laboratory analysis due to its advantages being rapid, cost …
alternative to conventional laboratory analysis due to its advantages being rapid, cost …
A bidirectional domain separation adversarial network based transfer learning method for near-infrared spectra
Z Zhang, S Avramidis, Y Li, X Liu, R Peng… - … Applications of Artificial …, 2024 - Elsevier
Traditional calibration transfer (CT) methods usually fail to adapt the source domain model
to the target domain because of changes associated with the instrument, detection …
to the target domain because of changes associated with the instrument, detection …
[HTML][HTML] Enhancing field soil moisture content monitoring using laboratory-based soil spectral measurements and radiative transfer models
Accurate information on the soil moisture content in croplands is essential for monitoring
crop growth conditions. This study aimed to enhance soil moisture monitoring by employing …
crop growth conditions. This study aimed to enhance soil moisture monitoring by employing …
Time series predictions in unmonitored sites: A survey of machine learning techniques in water resources
Prediction of dynamic environmental variables in unmonitored sites remains a long-standing
challenge for water resources science. The majority of the world's freshwater resources have …
challenge for water resources science. The majority of the world's freshwater resources have …