Advanced wood species identification based on multiple anatomical
  sections and using deep feature transfer and fusion
Kallil M. Zielinski Leonardo Scabini Lucas C. Ribas Núbia R. da Silva Hans Beeckman Jan Verwaeren Odemir M. Bruno Bernard De Baets
In recent years, we have seen many advancements in wood species
identification. Methods like DNA analysis, Near Infrared (NIR) spectroscopy,
and Direct Analysis in Real Time (DART) mass spectrometry complement the
long-established wood anatomical assessment of cell and tissue morphology.
However, most of these methods have some limitations such as high costs, the
need for skilled experts for data interpretation, and the lack of good datasets
for professional reference. Therefore, most of these methods, and certainly the
wood anatomical assessment, may benefit from tools based on Artificial
Intelligence. In this paper, we apply two transfer learning techniques with
Convolutional Neural Networks (CNNs) to a multi-view Congolese wood species
dataset including sections from different orientations and viewed at different
microscopic magnifications. We explore two feature extraction methods in
detail, namely Global Average Pooling (GAP) and Random Encoding of Aggregated
Deep Activation Maps (RADAM), for efficient and accurate wood species
identification. Our results indicate superior accuracy on diverse datasets and
anatomical sections, surpassing the results of other methods. Our proposal
represents a significant advancement in wood species identification, offering a
robust tool to support the conservation of forest ecosystems and promote
sustainable forestry practices.