# Enhanced Plant Leaf Classification over a Large Number of Classes Using Machine Learning
## 2 Literature Review
### Leaf Disease Identification  
A range of machine learning techniques have been explored to identify leaf diseases. [PERSON] [30] highlights the use of image processing and ML algorithms, achieving high accuracy with a combination of k-Means, Firefly optimization Algorithm (FA), and Support Vector Machine (SVM). These algorithms were exploited to identify plant diseases, emphasizing the importance of plant health in agriculture and global warming. The new proposed technique has an achieved accuracy of 91.3%. The authors of [31] conducted a study to explore using various machine learning algorithms for leaf disease detection and classification, such as SVM, KNN, SGD, XGB, and random forest. Evaluation parameters like F1 score, recall, precision, and accuracy are considered to evaluate the performance of the algorithms. The experimental results show that the achieved accuracy varies between 66.66% for KNN and up to 83.33% for the SGD algorithm. In [32], the authors aimed to detect leaf diseases in rice through a new CNN model combining MobileNetV2, DenseNet121, and NASNetMobile. The proposed model can improve the accuracy of classification performance to 97.5% when compared to the performance of three standard CNN models. Similarly, a study [33] was conducted to classify tomato leaf disease. The authors considered implementing CNN algorithms to achieve an accuracy of 99.89%. A system that observes the crops' growth and leaf diseases continuously to advise farmers in need is proposed by the authors of [34]. The proposed framework produces efficient crop condition notifications to terminal IoT components that assist in irrigation, nutrition planning, and environmental compliance related to farming lands. SVM and CNN are used to train the IoT infrastructures to detect the various types of leaf diseases. In [35], the authors propose a modern generic approach for wheat disease classification using Decision Trees and deep learning models. The refined models improved the decision tree algorithm accuracy by 28.5% and CNN accuracy by 4.3%. This knowledge-based system can help farmers apply appropriate management methods. The authors of [36] tried to solve the problem of Rumex obtusifolius Linnaeus by introducing a hybrid CNN model involving Visual Graphics Group-16 for well-separated leaves, ResNet-50 for complex issues, and Inception-v3 for illumination problems. The model was tested on two benchmark datasets. This model achieved high accuracy rates of 97.51%, 97.4%, 94.45%, and 95.9% on accuracy, recall, precision, and F1-score, respectively. A Machine Learning Framework for the classification of Date Palm Disease was developed by [37]. The framework uses 80 GLCM texture features and 9 HSV color moment features from leaflets. Two types of ML algorithms, classic SVM and KNN, and ensemble learning methods, RF and LightGBM, were tested. The SVM classifier outperformed the combined GLCM and HSV features, achieving an accuracy of 98.29%. The authors of [38] proposed a vision-based automatic medicinal plant identification system using neural network techniques and deep learning. A novel DeepHerb dataset, consisting of 2515 leaf images from 40 Indian herbs, is used to identify plants. The model deploys ANN and SVM to extract features and classify the herbs. The proposed DeepHerb model learned from Xception, and ANN outperformed pre-trained models by 97.5% accuracy. Another study [39] shows that the ANN algorithm and its variants, CNN, are highly effective in classifying tasks for monitoring paddy rice diseases. Potato plant disease detection is studied in [40; 41] using CNN networks, and it gives satisfactory results. Ref. [42] includes a review of 17 studies related to banana plant disease identification, ranging with an accuracy between 80% and 99.61%. These studies collectively underscore the potential of ML in this domain, with CNN, SVM, and Random Forest emerging as particularly promising approaches.