Cork is a versatile natural material. It can be used as an insulator in construction, among many other applications. For good forest management of cork oaks, forest owners need to calculate the volume of cork periodically. This will allow them to choose the right time to harvest the cork. The traditional method is laborious and time consuming. The present work aims to automate the process of calculating the trunk area of a cork oak from which cork is extracted. Through this calculation, it will be possible to estimate the volume of cork produced before the stripping process. A deep neural network, Mask R-CNN, and a machine learning algorithm are used. A dataset of images of cork oaks was created, where targets of known dimensions were fixed on the trunks. The Mask R-CNN was trained to recognize targets cork regions, and so the area of cork was estimated based on the target dimensions. Preliminary results show that the model presents a good performance in the recognition of targets and trunks, registering a mAP@0.7 of 0.96. After obtaining the mask results, three machine learning models were trained to estimate the cork volume based on the area and biometric parameters of the tree. The results showed that a support vector machine produced an average error of 8.75%, which is within the error margins obtained using traditional methods.