# Ontology with Deep Learning for Forest Image Classification
## 1 Introduction  
The majority of classification algorithms treat classes or categories of images independently both in terms of visual and semantic aspects [1]. In contrast, human beings use semantic relationships when classifying images into their respective categories [2]. For instance, it might seem unreasonable to distinguish \"tree\" from \"vegetation\" since a \"tree\" is a kind of \"vegetation\". Generally, human beings use features to distinguish different kinds of objects. For example, the NDVI (normalized difference vegetation index) is an essential feature for distinguishing between vegetation and water, while shapes can discriminate between broad-shaped leaves and needle-shaped leaves. Most classification algorithms achieved better performance results on easy image classification datasets such as Caltech 256 [3] and Caltech 101 [4]; however, they neglected the concept of semantics [5], which led to poor results on fine-grained images [6]. An ontology is a hierarchical structure of a particular domain that consists of all classes or categories as well as relationships such as \"is-a\" and \"kind-of\". It captures semantic relationships between classes or categories in a manner that is close to human perception. The adoption of ontologies in image classification algorithms incorporates semantics tools, thus leading to increases in image classification accuracy. Traditional ontology-based algorithms hugely suffer from the problem of error propagation because these ontology classifications are based on having classifiers at every ontological node, such that node subcategories are discriminated. Such errors were caused by intra-class variations of super-categories. The previous uses of ontologies in classification focused on improving speed rather than accuracy. Ontologies revolve around the use of semantic relationships, and data are expressed more at the semantic level, thus accounting for better classification. This study proposes an image classification model based on ontology and an ensemble stack of the Xception, VGG16, and ResNet50 models, which are employed to generate a set of features that are used by merged classifiers driven by taxonomic relationships in an ontology to improve image classification accuracy. The three pre-existing models, Xception, VGG-16, and ResNet50, have been adopted in this study via transfer learning because they have an innately dissimilar architecture that abstracts unrelated information from the images used for the classification purposes [7]. Some potential applications of ontological bagging in forestry include species classification. Information relating to forest tree species plays a critical role in ecology and forest management [8]. Our proposed ontological bagging model can be employed to improve the accuracy of species classification in forestry. Vegetation is an important part of an ecosystem because it provides oxygen and a suitable place for human beings to live [9]. Therefore, information concerning vegetation is very critical; hence, our proposed model can be used to classify vegetation into different types and categories. Our model can also be used to classify fruits into their respective categories. Fruit classification plays an important role in many industrial applications, including supermarkets and factories. The importance of fruit classification can be seen in people with special dietary requirements; in this case, they can be assisted in selecting categories of fruits [10]. The contribution of the study is summarised as follows:  
* We integrate semantic ontologies and aggregate outputs from hypernym-hyponym classifiers to increase image category distinction and also eliminate error propagation problems, hence increasing image classification accuracy.