# Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning
## 2 Literature Review
### Plant Leaf Classification  
Research on plant leaf classifications with machine learning has shown promising results. Many authors have developed new models and algorithms to improve the level of accuracy in plant leaf classification. [PERSON] [18] presents a multiple classifier system for plant leaf recognition that combines different classifiers trained on shape and texture features, improving identification performance by up to 28% compared to a monolithic approach and showing favorable comparisons with the best results reported in the literature for the datasets used. Later, [PERSON] [19] proposed a feature extraction technique based on sinuosity coefficients, which achieved high accuracy in plant species classification. [PERSON] et al. [20] presented a simple leaf recognition method that is computationally efficient and highly efficient for plant recognition. The proposed method was demonstrated on a publicly available leaf image database to demonstrate its usefulness and efficiency. [PERSON] [21] designed a modified local discriminant projection (MLDP) algorithm that aims to extract discriminant features for the designed plant leaves by preserving the local geometrical structure of leaves, and it showed promising results in an experimental study. The performance of the MLDP method is tested on the public ICL leaf image database, and its performance validates its effectiveness and feasibility. [PERSON] et al. [22] developed a new plant classification model based on enhanced segmentation and optimal feature selection. They created a hybrid algorithm C-EFO for the optimal feature selection and finalized the classification through the Enhanced Recurrent Neural Network (E-RNN) model. Their experiment was conducted in two plant leaf datasets and showed effectiveness and feasibility compared to the conventional models. The authors of [23] proposed a model that can classify up to 79 different plant species in India using the Convolution Neural Network DenseNet-161 model architecture with a testing accuracy of 97.3%. The application works on any Android platform and can classify the input plant image with an average latency of 1.98 s. In [24], the authors applied 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 a logistic regression classifier was used for efficient classification of plant species. From the eight datasets used, they achieved an average accuracy of 96.1% and up to 99 to 100% for individual datasets.  
Different authors have simultaneously emphasized the outperformance of deep learning among other classification algorithms. [PERSON] [25] and [PERSON] [26] both achieved high accuracy in classifying ornamental plant and flower leaves, respectively, using various machine learning algorithms, with Multi-Layer Perceptron (MLP) performing the best and [PERSON] [27] highlighting that Convolutional Neural Network (CNN) categorization surpasses traditional methods in accuracy. Similarly, the authors of [28] reviewed various techniques such as support vector machine, ResNet, K-nearest neighbor, VGG-16, and others for leaf classification. They discussed the differences between machine learning and deep learning regarding the plant leaf classification problem and concluded that deep learning models achieve maximum accuracy. [PERSON] et al. [29] also used machine learning to identify leaves of mangoes, oranges, and guavas through Google Teachable with high accuracy.