With the rise of deep learning, the combination of spectroscopy analysis techniques and
deep learning methods has been extensively utilized in the field of agriculture, such as the …

Abstract Machine learning (ML) models have recently been used in visible and near-infrared
reflectance (VNIR) spectroscopy applications. However, the predictive performance of ML …

Amidst the growing food demands of an increasing population, agricultural intensification
frequently depends on excessive chemical and fertilizer applications. While this approach …

Classifying soil texture is critical to investigate soil processes and functions influencing
agronomic decisions and environmental stewardship. Despite the multiple benefits together …

Visible and near infrared spectroscopy (vis-NIRS) has shown potential to predict soil
phosphorous (P) with reasonable accuracy. However, spectra pre-treatment is essential in …

Previous works indicate that data fusion, compared to single data modelling can improve the
assessment of soil attributes using spectroscopy. In this work, two different kinds of proximal …

New soil sensing technologies, such as visible and near-infrared diffuse reflectance
spectroscopy (VNIR) and X-ray fluorescence spectroscopy (XRF), emerged as alternatives …

Develo** a rapid, accurate, and cost-effective method for predicting constituents related to
soil nitrogen (N), phosphorus (P), and potassium (K) content is considered substantial. One …

Soil quality assessment, as a means to assess the impact of human activities on soil, is of
great significance to achieve sustainable development. Proximal sensing offers a rapid and …

An accurate assessment of soil fertility is crucial for monitoring environmental dynamics,
improving agricultural productivity, and achieving sustainable land management and …