Precision agriculture (PA) is the application of geospatial techniques and sensors (eg,
geographic information systems, remote sensing, GPS) to identify variations in the field and …

Developments in space-based hyperspectral sensors, advanced remote sensing, and
machine learning can help crop yield measurement, modelling, prediction, and crop …

Unmanned aerial vehicle (UAV)-borne hyperspectral systems can acquire hyperspectral
imagery with a high spatial resolution (which we refer to here as H 2 imagery). As a result of …

For better agricultural productivity and food management, there is an urgent need for
precision agriculture monitoring at larger scales. In recent years, drones have been …

Machine learning techniques are widely used for pixel-wise classification of hyperspectral
images. These methods can achieve high accuracy, but most of them are computationally …

Farmers explore the capabilities for applications of Robotic Process Automation (RPA) with
image processing, pattern recognition and machine learning, so its logical to ask where best …

Remote sensing plays a significant role for crop classification. Accurate crop classification is
a common requirement to precision agriculture, including crop area estimation, crop yield …

Understanding plant disease resistance is important in the integrated management of
Phytophthora infestans, causal agent of potato late blight. Advanced field-based methods of …

The accurate identification of rice varieties using rapid and nondestructive hyperspectral
technology is of practical significance for rice cultivation and agricultural production. This …

High spatial and spectral resolution (H 2) imagery collected by unmanned aerial vehicle
(UAV) systems is an important data source for precise crop classification. Although this data …