Abstract
A new modification of multi-CNN ensemble training is investigated by combining multiloss functions from state-of-the-art deep CNN architectures for leaf image recognition. We first apply the U-Net model to segment leaf images from the background to improve the performance of the recognition system. Then, we introduce a multimodel approach based on a combination of loss functions from the EfficientNet and MobileNet (called as multimodel CNN (MMCNN)) to generalize a multiloss function. The joint learning multiloss model designed for leaf recognition allows each network to perform its task and cooperate with the others simultaneously, where knowledge from various trained deep networks is shared. This cooperation-proposed multimodel is forced to deal with more complicated problems rather than a simple classification. Therefore, the network can learn much rich information and improve its generalization capability. Furthermore, a multiloss trade-off strategy between two deep learning models can reduce the effect of redundancy problems in ensemble classifiers. The performance of our approach is evaluated by our custom Vietnamese herbal leaf species dataset, and public datasets such as Flavia, Leafsnap, and Folio are used to build test cases. The results confirm that our approach enhances the leaf recognition performance and outperforms the current standard single networks while having less low computation cost.
Highlights
In pattern recognition field, image classification related to the labeling of input images into a fixed set of categories is challenging. is field involves various techniques for detecting and extracting features from input images and maps them with available templates in the database
Many consumers and patients select natural products because they believe these products are more compatible with the human body and have fewer side effects, and they are safe when used for a long time [3]. erefore, we build a database on herbs and plants and recognize them with necessary information, illustrations, and urgent jobs. is information should be presented in a concise, specific manner but still ensures correctness so that mass users can interact with the data sources, which will be reviewed by reputable herbalists and plant experts
We compare to other methods in terms of leaf recognition. e recognition rate obtained through our methods is greater than 95% with and without data augmentation. e experimental analysis revealed that the recognition rate by using U-Net + simple convolutional neural networks (CNNs) generally increases with applying data augmentation, with the recognition rate achieved 99.25%
Summary
Image classification related to the labeling of input images into a fixed set of categories is challenging. is field involves various techniques for detecting and extracting features from input images and maps them with available templates in the database. Classification of natural objects such as plants and herbal species in the surrounding environments has become an important task. Recognizing valuable, threatened plants and herbal species may raise awareness among people in our society to partially contribute to preserve them. Vietnam has rich herbal and land-plant resources that need to be preserved and efficiently exploited to promote the economic growth of high-tech agriculture. These valuable sources are in danger due to human activities. E information on various herbal and plant species is quite limited to nonexpert users with many barriers. Many consumers and patients select natural products (derived mainly from medicinal plants) because they believe these products are more compatible with the human body and have fewer side effects, and they are safe when used for a long time [3]. erefore, we build a database on herbs and plants and recognize them with necessary information, illustrations, and urgent jobs. is information should be presented in a concise, specific manner but still ensures correctness so that mass users can interact with the data sources, which will be reviewed by reputable herbalists and plant experts
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