# Plant-Leaf Recognition Based on Sample Standardization and Transfer Learning
## 4 Conclusions  
The present paper introduces a novel sample-standardization method, namely MEQ, for the purpose of plant-leaf recognition. Firstly, the minimum enclosing rectangle of the leaf sample is obtained by finding the maximum connected component in the image. Then, the target area of the original sample image is cropped according to the coordinates of the rectangle. Finally, the rectangle is further expanded into a square and compressed to meet the input requirements of the models. The proposed method can effectively avoid the issue of leaf deformation caused by asymmetric sample compression due to the input limitation of the CNN model, while simultaneously maximizing the removal of redundant information in the image. In addition, an improved ResNet50 model is proposed and used in combination with MEQ methods. The main advantages of this model lie in the utilization of the _ELU_ activation function, which exhibits superior learning capabilities compared to the original _ReLU_, and the incorporation of a larger convolution kernel size within the specific residual block for enhanced extraction of global leaf features. The results of the transfer-learning experiment on six typical CNN models using various original leaf datasets and MEQ standardized datasets demonstrate that the utilization of the MEQ method significantly enhances the classification accuracy of CNN models when applied to leaf datasets. This validates the universal applicability of the proposed sample standardization method and introduces a new idea for plant-leaf recognition research.  
Simultaneously, the validation accuracy of the MEQ + EC-ResNet50 algorithm on Swedish, Flavia, and MEW2012 leaf datasets, respectively, reached 100%, 99.83%, and 99.25%, and it also obtained the best results among all comparison methods and can be used as a feasible scheme for plant-leaf recognition.  
Additionally, the proposed MEQ algorithm can serve as an exceptional preprocessing method for image samples in various other fields of image classification. By bringing the samples closer to their natural state, it significantly enhances the training and practical application of recognition algorithms. However, the current standardization method for samples falls short when dealing with image backgrounds, as it still requires retaining a portion of background information to prevent the loss of blade-edge details. Moving forward, we will continue refining the MEQ algorithm and optimizing the approach to processing sample background information.  
Conceptualization, G.L. and H.N.; methodology, G.L.; software, G.L. and R.Z.; formal analysis, G.L.; investigation, G.L.; resources, H.N.; data curation, G.L. and H.N.; writing--original draft preparation, G.L.; writing--review and editing, G.L., R.Z. and H.N.; visualization, G.L. and H.N.; supervision, D.Q.; project administration, D.Q. and H.N.; funding acquisition, H.N. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Fundamental Research Funds for the Central Universities (No. 2572022 BC03), the Innovation Training program for the college students at Northeast Forestry University (No. 202110225485), and the Project of National Natural Science Foundation of China (No. 31570712).  
Not applicable.  
The raw data supporting the conclusions of this article will be made available by the authors on request.  
The authors declare no conflicts of interest.