A Deep Learning-Based Recognition Technique for Plant Leaf Classification

In the practice of plant classification, the design of hand-crafted features is more dependent on the ability of computer vision experts to encode morphological characters that are predefined by botanists. However, the distinct features that each plant has as demonstrated by its leaves can be automatically learned based on the end-to-end advantage of Deep Learning algorithms. Therefore, Deep Learning based plant leaf recognition methods is an important approach nowadays. In this article, we are applying three technologies to achieve a model with high accuracy for plant classification. A Conditional Generative Adversarial Network was used to generate synthetic data, a Convolutional Neural Network was used for feature extraction and the rich extracted features were fed into a Logistic Regression classifier for efficient classification of the plant species. The effectiveness of this method can be seen in the wealth of plant datasets that it was tested on. The paper contains results on seven datasets with different modalities. We utilized both Deep Learning and Logistic regression in effectively classifying the plants using their leaf images with accuracies averaging 96.1% for about eight datasets used, but greater for the individual datasets from 99.0 to 100% on some individual datasets. Extensive experiments on each of the datasets demonstrate the superiority of our method compared with others and are highlighted in our results.