## 1 Introduction  
Countries dependent upon agriculture are facing a terrible threat and great loss due to plant diseases, which cause a decline in the quality and quantity of fruits and yields [1]. Pakistan is among those countries where a large amount of its income is earned by importing and producing a variety of crops, vegetables, and fruits cultivated in different areas of the country [2]. Therefore, it is necessary to identify diseased plants by implementing computer vision and image processing techniques [3, 4]. Recently, deep learning (DL) techniques, specifically, convolutional neural networks (CNNs), have achieved extraordinary results in many applications, including the classification of plant diseases [5, 6]. The mango is a highly popular fruit and is available in summer [7]. It is important in the agricultural industry of Pakistan due to its huge production volume. Several approaches to the detection and identification of mango plant leaf diseases have been proposed in the literature. Although a large number of diseases affect mango orchards, only some of them are causing great loss to the economy of the country. A few of the more common diseases [8] include powdery mildew, sooty mold, anthracose and apical necrosis, as shown in Figure 1. In the current era, computer scientists are aiming to devise computer-based solutions to identify the diseases in their initial phase. This will aid farmers to safeguard the crop until it is harvested, resulting in the reduction in economic loss [9]. According to agricultural experts, naked eye observation is the traditional method used to recognize plant diseases. This is very expensive and time-consuming as it requires continuous monitoring [3, 10]. Hence, it is almost impossible to accurately recognize the diseases of plant at an initial stage. Unfortunately, very few techniques that address the diseases of the mango, also called the king of fruits, have been reported before now [11; 12] due to the complicated and complex structure and pattern of the plant. Hence, there is a need for efficient and robust techniques to identify mango diseases automatically, accurately, and efficiently [4; 13; 14; 15]. For this purpose, the images used as a baseline can be captured by digital and mobile cameras [16; 17].  
Machine learning (ML) plays an important role in the identification of diseases [16; 18]. ML is a sub-branch of artificial intelligence (AI) [19]. It enables computer-based systems to provide accurate and precise results. Real-world objects are the main objects to inspire ML techniques [20]. A few computerized techniques, for instance, segmentation by K-means and classification using SVM to identify the diseased area, are reported in [21].  
Hence, as per the available and above discussion, there is a strong need for the automatic detection, identification, and classification the diseases of the mango plant. Keeping this in mind, this article covers the following issues: data augmentation to increase the dataset; tracking the color, size, and texture of the diseased part of leaf; handling background variation and the diseased part; the proper segmentation of the unhealthy part of the leaf; and, at a later stage, robust feature extraction and fusion to classify the disease. The following key contributions were performed to achieve this task:  
1. Image resizing and augmentation in order to set query images.
2. A method for the segmentation of the diseased part.
3. Fusion of color and LBP features by performing canonical correlation analysis (CCA).
4. Using classifiers of ten different types to perform identification and recognition.