Advances in computing technology have fostered the development of new and powerful
deep learning (DL) techniques, which have demonstrated promising results in a wide range …

Automatic identification of diseases through hyperspectral images is a very critical and
primary challenge for sustainable farming and gained the attention of researchers during the …

This paper reviews advanced Neural Network (NN) techniques available to process
hyperspectral data, with a special emphasis on plant disease detection. Firstly, we provide a …

This article gives a survey of state-of-the-art methods for processing remotely sensed big
data and thoroughly investigates existing parallel implementations on diverse popular high …

The livestock activity accounts for a large part of the transformations in land cover in the
world, with pasture areas being the main land use in Brazil and the main livelihood of the …

The large data volume and high algorithm complexity of hyperspectral image (HSI) problems
have posed big challenges for efficient classification of massive HSI data repositories …

Recent developments in the field of Industry 4.0 (I4. 0) and its enabling technologies have
led to a renewed interest in sustainable product design (SPD). So far, however, little …

The large amount of data produced by satellites and airborne remote sensing instruments
has posed important challenges to efficient and scalable processing of remotely sensed …

Spectral indices are of fundamental importance in providing insights into the distinctive
characteristics of oil spills, making them indispensable tools for effective action planning …

Green clouds optimally use energy resources in large-scale distributed computing
environments. Large scale industries such as smart grids are adopting green cloud …