In recent years unmanned aerial vehicles (UAVs) have emerged as a popular and cost-
effective technology to capture high spatial and temporal resolution remote sensing (RS) …

Cereal plant heads must be identified precisely and effectively in a range of agricultural
applications, including yield estimation, disease detection, and breeding. Traditional …

Deep-learning-based object detection algorithms have significantly improved the
performance of wheat spike detection. However, UAV images crowned with small-sized …

Landslides are catastrophic natural hazards that often lead to loss of life, property damage,
and economic disruption. Image-based landslide investigations are crucial for determining …

Accurate counting of maize tassels is essential for monitoring crop growth and estimating
crop yield. Recently, deep-learning-based object detection methods have been used for this …

Unmanned aerial vehicles (UAVs) are considered as one of the promising technologies for
the next-generation wireless communication networks. Their mobility and their ability to …

This work introduces a method that combines remote sensing and deep learning into a
framework that is tailored for accurate, reliable and efficient counting and sizing of plants in …

During the seedling stage, real-time monitoring and detection of seed germination are
important for testing the quality of seeds, crop field management, and yield estimation …

Today there is a rapid development taking place in phenotyping of plants using non-
destructive image based machine vision techniques. Machine vision based plant …

With shrinking natural resources and the climate challenges, it is foreseen that there will be
an imminent stress in agricultural outputs. Deep learning provides immense possibilities in …