Wood Species Recognition System
From an aerial view, trees may look simply ordinary, with little to distinguish individual species beyond a guess of their native origin. In 2016, Malaysian-based researchers developed a neural network capable of identifying tree species by classifying pixel data based on colour and texture with an accuracy rate of 97%. The current neural network depends on what is known as the Improved-Basic Gray Level Aura Matrix (I-BGLAM) technique, teaching the algorithm to identity minute differentials in greyscale between pixels and matching the colour concentration and overall texture to its training database. This database contains information on the aspects of 52 tree species based on 100 images collected not by satellite, but through aerial photography. The number of species used to build the neural network is small (considering there are thousands of tree species), but if tree species information and imagery were available for areas globally it could be applied in more forests outside of the those in Malaysia which acted as test samples. The application of this technology is meant to ensure that land parcels where logging is allowed do actually contain the tree species that a logging company is authorized to cut. Conversely, if illegal logging takes place, regulators could hypothetically use this information to check what trees existed in the area that was logged, and where it took place. The application now is limited to the test site, but in the future with more widespread usage, this technology could be a boon for preventing illegal resource extraction and to ensure more accurate logging.
Zamri, Mohd Iz’aan Paiz, Anis Salwa Mohd Khairuddin, Norrima Mokhtar, and Rubiyah Yusof. “Wood species recognition system based on improved basic grey level aura matrix as feature extractor.” Journal of Robotics, Networking and Artificial Life 3, no. 3 (2016): 140-143.
Categories
Artificial Intelligence, Illegal Resource Extraction, Industry/Natural Commodities, Visual Technologies
More Ideas
Air Pollution Robot
The dangers of air pollution to human health are well documented, though the traditional methodology of collecting and reporting on sample lags behind the need to keep abreast and regulate air pollution in a meaningful amount of time. The use of drones and robots have been identified by researchers as resources that can be tweaked […]
Artificial Life, Biodiversity, Climate Change, Ecological Monitoring, Industry/Natural Commodities, Lifestyle, Monitoring
Telematic Rivers
Erica Kermani’s artwork seeks to answer a central question: if rivers were seeing an equal, living entity, would humans take issues like climate change threatening them more seriously? In his year-long art exhibition in 2017, Kermani, in collaboration with Diana Salcedo & Jeana Chesnik, created a new forum of interaction between humans and rivers to […]
Climate Change, Ecological Monitoring, Lifestyle, Monitoring, Pollution, Visual Technologies
Co-occupied Boundaries
Art is easily found in nature but rarely is what considered art today inherently natural. The concept of co-occupied mediums that serve to be both functional for nature and aesthetically pleasing to people is being actively explored by Asya Ilgun and Phil Ayres, from the CITAstudio at The Royal Danish Academy of Fine Arts. In […]
Climate Change, Ecological Monitoring, Lifestyle, Monitoring, Pollution, Visual Technologies