# Ontology with Deep Learning for Forest Image Classification
## 2 Related Works
The main merit of the model is its ability to learn relationships between objects and annotation proposals. The study contributed extensively to solving computer vision tasks, and it performed well both in image classification and image auto-annotation. However, the shortcomings of the model were that it required fine tuning on the orange dataset, which is time-consuming. A unified CNN-RNN model for multi-class image classification was proposed in [21]. The classification process consisted of learning semantic redundancy and the co-occurrence dependency in an end-to-end way. The model has the ability to obtain semantic level dependency and image label relevance by learning the joint image label embedding. The model could also be trained from scratch to integrate both pieces of information in a unified framework. The results obtained show better performance in terms of classification than the state-of-the-art multi-label classification model.The shortcomings of the model were that it fails to make a prediction on small objects that have little covariance dependencies with other, larger objects. Considering that microscopic imaging technology is rapidly advancing, bio-image-based approaches to protein subcellular localization have sparked a lot of interest. However, there are fewer techniques for predicting protein location, with the majority of them relying on automatic single-label classification. Therefore, a study [22] developed an artificial intelligence (AI)-based stacked ensemble approach for the prediction of protein subcellular localization in confocal microscopy images. The ensemble approach was built by stacking ResNet152, DenseNet169, and VGG16 as base learners, and their predictions were integrated and fed as input to the meta-learner. The model was implemented on an image dataset obtained from Human Protein Atlas Image Classification on Kaggle and attained precision, F1-score, and recall of 0.71, 0.72, and 0.70, respectively. The main difficulty encountered in the study was a huge imbalance of images in the image categories, as some classes had very few images, which were insufficient to train the model. In our study, we used a data augmentation technique to determine image balance across categories. The evolution of AI applications has significantly increased the utilization of smart imaging devices. Convolutional neural networks (CNNs) are widely used in image classification because they do not require any handcrafted features to influence performance. However, fruit classification in the horticulture field suffers from the significant disadvantage of requiring an expert with extensive knowledge and experience. To address this issue, a study [23] developed MobileNetV2 with a deep learning technique for fruit image classification. The study did not require the intervention of experts. The model used 26,149 images of 40 different fruit types from a Kaggle public dataset and achieved 99% accuracy. The model could be improved using a larger variety of fruits for broader fruit classification. The idea of annotating images has received a significant amount of attention due to the sharp increase in volumes of images. By considering the area of agriculture, a study [24] proposed a deep learning repetitive annotation approach for recognizing a variety of fruits and classifying the ripeness of oil palm fruit. The model was implemented on 3500 fruit images and achieved an accuracy of 98.7% for classifying oil palm fruit and 99.5% for recognizing a variety of fruits. CNNs are also used in agriculture for seed classification, despite the inherent limitations of traditional machine-learning approaches in extracting features and information from image data. Ref. [25] created a deep CNN based on MobileNetV2 with a simple architecture for seed classification. The model was applied to a seed dataset with 14 different seed classes and achieved an accuracy of 95% and 98% on testing and training, respectively.