OrchidNet: A Self-Supervised Learning-Based Efficient Multiscale Feature Fusion Convolutional Neural Network With a Lightweight Architecture for Orchid Classification

Classifying orchids is a challenging task: the color, shape, and texture features of each part of an orchid image are small, the category similarity is high, and the features are blurred. Traditional deep learning methods are not suitable for orchid image classification, mainly because they rely on a large amount of human interpretation for labeling models to achieve a certain degree of accuracy. Therefore, to address the human interpretation problem of orchid classification, we propose a self-supervised learning-based efficient multiscale feature fusion convolutional neural lightweight network architecture called OrchidNet. OrchidNet is divided into three parts: 1) a convolutional neural network (CNN) attention network; 2) a self-supervised fine-grained mechanism; and 3) a feature pyramid network for multiscale feature fusion. In this article, we focus on lightweight and high-performance materials. The motivation for our method is to use CNNs to upsample the backbone architecture (backbone) of local visual features. In orchid images, most of the features are extracted by the CNN backbone (attention mechanism). Afterward, the self-supervised fine-grained mechanism is used for feature screening. Finally, the global and local feature information is downsampled through the feature pyramid for multiscale feature fusion. This study significantly impacts the image-processing community of orchid classification. The proposed OrchidNet improves the accuracy of orchid classification and uses a self-supervised learning method that does not require manual labeling, thereby reducing the labeling time and the cost of manual interpretation. The experimental results show that the proposed OrchidNet is superior to state-of-the-art methods for orchid classification used in recent years.