Deep Learning Methods for Tree Detection and Classification

Abstract

This paper presents the results of our deep learning methods for tree detection and classification on aerial images in the Plant Recognition University Challenge sponsored by Ameren in 2021–2022. The task was to locate the trees in an aerial image and predict their family, genus, and species. For tree detection, we applied various supervised learning methods with labeled training data as well as semi-supervised learning methods with the addition of unlabeled data. Our experimental results show that the semi-supervised learning method outperformed the supervised learning methods, improving the f1-score by an average of three percent on the set of images used in the final Plant Challenge competition. For tree classification, We applied various machine learning methods and deep learning models for image classification to predict family, genus and species on the portions of images detected of trees by the detection models. By considering the relationships between family, genus and species, we developed a multi-head ResNet18-based neural network and increased mean accuracy by two percent over the baseline ResNet18. Finally, our team ranked first among all teams in the Plant Challenge competition.