Current rates of species loss triggered numerous attempts to protect and conserve
biodiversity. Species conservation, however, requires species identification skills, a …

Automated signal recognition software is increasingly used to extract species detection data
from acoustic recordings collected using autonomous recording units (ARUs), but there is …

Variation in avian diversity in space and time is commonly used as a metric to assess
environmental changes. Conventionally, such data were collected by expert observers, but …

Convolutional neural network has a huge partake and is still a dominating tool in the field of
computer vision. In this study, we introduce a model with depthwise separable convolution …

Passive acoustic monitoring is a cornerstone of the assessment of ecosystem health and the
improvement of automated assessment systems has the potential to have a transformative …

We introduce a novel fine-grained dataset and benchmark, the Danish Fungi 2020 (DF20).
The dataset, constructed from observations submitted to the Atlas of Danish Fungi, is unique …

The accurate identification of species in images submitted by citizen scientists is currently a
bottleneck for many data uses. Machine learning tools offer the potential to provide rapid …

The categorization and identification of agricultural imagery constitute the fundamental
requisites of contemporary farming practices. Among the various methods employed for …

The article reviews and benchmarks machine learning methods for automatic image-based
plant species recognition and proposes a novel retrieval-based method for recognition by …

Plant plays an important role in agricultural, industrial, medicine, environmental and
ecological protection. Recently, with global warming, biodiversity loss, rapid urban …