The correct identification of timber species is a complicated task for the wood industry and government institutions regulating the different laws that ensure legal and transparent commerce. Currently, experts perform this process using the organoleptic characteristics of the wood. However, the methodology used is time-consuming and limited to environmental conditions. Moreover, it has a scalability issue since acquiring this specific knowledge and experience has a slow learning curve. On the other hand, deep learning models have evolved as possible solutions for process automation. Therefore, this paper explores convolutional neural network models suited to run on edge devices. The present study created a database with 25k images of 25 timber species from the Peruvian Amazon. We trained-validated multiple lightweight models (less than 5M). The experiments were made using a repeated stra...

Almost half of the tributaries of the Amazon River originate in the tropical Andes and support large populations in mountain regions and downstream areas. However, it is difficult to assess hydroclimatic conditions or to evaluate future scenarios due to the scarcity of long, high-quality instrumental records. Data from the Global Precipitation Climatology Project (GPCP) provide a complete record since 1979 and offer a good representation of rainfall over the tropical Andes. Longer records are needed to improve our understanding of rainfall variability and summer monsoon behavior at various scales. We developed the first annually resolved precipitation reconstruction for the tropical Andes in Peru, based on tree-ring chronologies of Cedrela and Juglans species. The annual (November–October) reconstruction extends the short instrumental records back to 1817, explaining 68% of the total v...