Plant-Nitrogen is a vital element that significantly influences plant growth, fruit quality, and
yield. However, excessive Nitrogen (N) fertilizer application can have adverse effects on …

Hyperspectral remote sensing has emerged as an efficient tool to quantify the spatial and
temporal variations in crop foliar nutrients, thus reducing the burden on in-situ tissue …

This work focuses on dust detection, and estimation of vegetation in coal mining sites using
the vegetation indices (VIs) differences model and PRISMA hyperspectral imagery. The …

Using new remote sensing technology to study agricultural crops will support advances in
food and water security. The recently launched, new generation spaceborne hyperspectral …

Soil organic matter (SOM) is crucial for carbon sequestration and sustainable agriculture, yet
traditional quantification methods are challenging to apply at large scales. Hyperspectral …

Although linear discriminant analysis (LDA)-based subspace learning has been widely
applied to hyperspectral image (HSI) classification, the existing LDA-based subspace …

Neuro-symbolic models combine deep learning and symbolic reasoning to produce better-
performing hybrids. Not only do neuro-symbolic models perform better, but they also deal …

This study explores the use of leaf-level visible-to-shortwave infrared (VSWIR) reflectance
observations and partial least squares regression (PLSR) to predict foliar concentrations of …

A timing and precise diagnosis of crop nutrient status is essential for optimizing
management practices that promote environmentally friendly and enhanced crop yields …

This paper presents an improved Bald Eagle Search Algorithm with Deep Learning model
for forest fire detection (IBESDL-FFD) technique using hyperspectral images (HSRS). The …