Identifiera löv i skogar – Att lära en dator känna igen löv med ImageAI

Sammanfattning: A current field of research today is machine learning because it can simplify everyday life for human beings. A functioning system that has learned specific tasks can make it easier for companies in both cost and time. A company who want to use machine learning is SCA, who owns and manages forests to produce products. They have a need to automate forest classification. In order to evaluate forests, and to plan forestry measures, the proportion of leafy tree that is not used in production must be determined. Today, manual work is required of people who have to investigate aerial photos to classify the tree types. This study investigates whether it is possible, through machine learning, to teach a computer to determine whether it is leaf or not in photographs. A program is constructed with the library ImageAI which receives methods for training and predicting information in images. It examines how the choice of neural network and the number of images affects the safety of the models and how reliable the models can be. Exercise time and hardware are also two factors that are investigated. The result shows that the neural network ResNet delivers the safest results and the more images the computer exercises, the safer the result. The final model is a ResNet model that has trained on 20,000 images and has 79,0 percent security. Based on 50 samples, the mean value for safety is 90,5 percent and the median is 99,6 percent.