## 2 Literature Review  
In current times, various types of techniques and methods have been established for the detection of plant leaf diseases. These are generally characterized into disease finding or disease detection methods and disease sorting or disease classification methods [22]. Many techniques use segmentation, feature fusion, and image classification implemented on cotton, strawberry, mango, tomato, rice, sugarcane, and citrus. Similarly, these methods are appropriate for leaf, flower, and fruit diseases because they use proper segmentation, feature extraction, and classification. To make a computer-based system work efficiently, ML techniques are generally used to enhance the visualization of the disease symptoms and to segment the diseased part for classification purposes. Mango fruit is important in the agricultural sector due to its massive production volume in Pakistan. Therefore, several approaches for the detection and identification of mango plant leaf diseases have been proposed to prevent a loss of harvest. [PERSON] et al. [23] considered segmentation, recognition, and identification techniques. They found that almost all the techniques are in the initial stage. Moreover, they discussed almost all existing methods along with their advantages, limitations, challenges, and models of ML (image processing) for the recognition and identification of diseases. [PERSON] et al. [24], in their study on powdery mildew of strawberry leaves, achieved an accuracy of 94.34% by combining an artificial neural network (ANN) and speeded-up robust features (SURF). However, by using an SVM and GLCM, their highest classification accuracy was 88.98%. They used HOG, SURF, GLCM, and two supervised ML methods (ANN and SVM). [PERSON] et al. [25] adopted an ML technique using a wavelength between 403 and 446 nm for the detection of the blue color. They precisely distinguished unhealthy and healthy leaves of wild rocket, also called salad leaves. [PERSON] et al. [26] used the Friedman test to rank multiple classifiers and post hoc analysis was also performed using the Nemenyi test. In their study, they found the MGSVM to be the superior classifier with an accuracy of 94.74%. [PERSON] et al. [17] proved their results to be 3.15% more accurate than the traditional method used on pumpkin leaves. They used PCA in order to obtain 97.3% accurate results. [PERSON] [27] extracted the color features to detect diseases on cotton leaves. [PERSON] et al. [28] developed an expert system for the detection of diseases including powdery mildew and sooty mold on all members of the citrus family. [PERSON] et al. [29] recommended a computerized system to segment and classify the diseases of citrus plants. In the first part of their suggested system, they used an optimized weight technique to recognize unhealthy parts of the leaf. Secondly, color, geometric, and texture descriptors were combined. Lastly, the best features were nominated by a hybrid feature selection technique consisting of the PCA approach called entropy and they obtained 90% accuracy. [PERSON] et al. [30] proposed a method to classify anthracose (diseased) leaves of mango plants. They used a multiple layer convolutional neural network (CNN) for this task. The method was applied to 1070 images collected by their own cameras and gadgets. Consequently, the classification accuracy was raised to 97.13%. [PERSON] et al. [31] presented a segmentation method in 2019 based on deep learning called Mango net. The results using this method are 73.6% accurate. [PERSON] et al. [11] used a CNN and showed 96.6% accurate outcomes. No preprocessing or feature extraction was performed in this proposed technique. [PERSON] [32] detected unhealthy regions for mango diseases, including red rust, anthracose, powdery mildew, and sooty mold. Sooty mold and powdery mildew both create a layer on the leaf by disturbing its vein pattern, as the vein is a vital part of the plant. No techniques have been proposed to make this specific diagnosis, which is a major weakness in the available literature.