The abaca also known as cañamo of manila, produces a natural fiber which is known as the most resistant worldwide natural fiber, that has properties like resistance to salinity, mechanical strength, flexibility, and durability. The unique characteristics of the fiber have caused an increase in its use in many industries. However, the cultivation of abaca is threatened by the presence of Fusarium oxysporum f. sp. cubense, one of the most important diseases which has been considered the most lethal, due to the scarcity of existing control methods. The article aims to highlight recent data about the characteristics of the plant and its fiber, crop management, productivity, and the market as an approach for updating the current knowledge regarding the abaca. It was found that some characteristics of abaca fiber such as strength and physical properties depend on the position of its leaves, on the maturity of the plant and also on the fiber extraction system. Regarding fiber extraction, it is mentioned that there are two types of methods (stripping and decortication). Concerning the productivity, there exists a considerable difference between the ways of bundle extraction, for instance, the hand stripping technique produces about 20 kg of fiber bundles per day. It is concluded that abaca has great potential as a crop that could boost the markets of various countries. Though the information available on this crop is scarce, so it is necessary to increase research about its production and management to promote greater use.

The fast and accurate identification of forest species is fundamental to support their conservation, sustainable management, and, more specifically, the fight against illegal logging. Traditionally, identifications are done by using dichotomous or polytomous keys based on physical characteristics of trees. However, these techniques are of little use when the trees have been cut, removed from their natural environment, and consequently there is only a partial subset of information on all those traits. In these cases, it may be possible to resort to the anatomical characteristics of the wood, which are less affected by environmental factors and therefore have a high diagnostic value in the identification. For some years now, computers have been used to support the identification processes through interactive keys and access to global repositories of digital images, among others. However, techniques based on machine learning have recently been developed and applied successfully to the identification of both plant and animal species. Consequently, automatic or semiautomatic techniques have been proposed to support botanists, taxonomists and non-experts in the species identification process. This article presents an overview of the use of these techniques as well as the current challenges and opportunities for the identification of forest species based on xylotheque samples.