Abstract Drones, also called Unmanned Aerial Vehicles (UAV), have witnessed a
remarkable development in recent decades. In agriculture, they have changed farming …

Infinite numbers of real-world applications use Machine Learning (ML) techniques to
develop potentially the best data available for the users. Transfer learning (TL), one of the …

In the change detection (CD) task, the UNet architecture has achieved superior results.
However, due to the inherent limitation of convolution operations, UNet is inadequate in …

Images gathered from different satellites are vastly available these days due to the fast
development of remote sensing (RS) technology. These images significantly enhance the …

Remote sensing is a useful tool for monitoring spatio-temporal variations of crop
morphological and physiological status and supporting practices in precision farming. In …

Abstract Deep Neural Networks (DNNs) learn representation from data with an impressive
capability, and brought important breakthroughs for processing images, time-series, natural …

Owing to effective and flexible data acquisition, unmanned aerial vehicles (UAVs) have
recently become a hotspot across the fields of computer vision (CV) and remote sensing …

Precision farming is made possible by rapid advances in deep learning (DL) and the internet
of things (IoT) for agriculture, allowing farmers to upgrade their agriculture operations to …

Birds take off and land on a wide range of complex surfaces. In contrast, current robots are
limited in their ability to dynamically grasp irregular objects. Leveraging recent findings on …

In recent years, technological advances have led to the increasing use of unmanned aerial
vehicles (UAVs) for forestry applications. One emerging field for drone application is forest …