Diseases in fruit plants and leaves are a major cause of destruction and economic loss. Automated systems help greatly with the detection of diseases at early stages. While considering the field of detection of disease in plants, deep neural networks work perfectly to identify and classify diseases. These networks are mentored to conduct high-value results in detecting and classifying diseases, and to fulfill the demands of food deformation prevention.

There are different methods for collecting images under certain conditions. Images are captured by multiple appliances, such as cameras, sensors, mobile phones, and other devices. In this era, more datasets containing guava are publicly available on multiple forums, such as Kaggle, Mendeley, and many others [10]. Pre-processing of images is an important phase in image processing. The pre-processing phase entails multiple steps which help highlight the focused parts and remove irrelevant information from guava leaf images. In the real world, label noise on images is a matter of concern. Multiple techniques have attained the best results in denoising images, especially mixed noise, speckle noise, and salt and pepper noise. Low contrast and color distortion in guava leaf images make them blur. Scattering and light absorption also affect clear image visualization [11]. Images are preprocessed by using rotational filters such as horizontal and vertical flipping. Data augmentation techniques are used, such as applying rotations and zooming into images [12]. Color spacing techniques are extensively applied in image processing. RGB, CIELAB, and CMYK models are mostly used as color spacing techniques to give the best results.

Feature extraction is a process in which reatures are reduced from the raw dataset and new features for manageable processing are created. Texture analysis has a wide range of applications [13]. Pattern recognition requires feature extraction to solve problems in prediction, cluster discrimination, and representation of data in the best way [14,15]. Content-Based Image Retrieval (CBIR) converts high-level image visuals into feature vectors that contain some properties [16]. There are multiple techniques to extract the features from guava leaf images, such as handcrafted-based features, region-based features, deep CNN-based features, texture-based features, color-based features, morphological-based features, etc. Extraction of features is categorized into hand-crafted-based features and deep-based features.

The selection of features from plant leaf images is carried out after the extraction of hand engineered and deep-based features. The set of features is chosen while noisy, poor, and extra features are eliminated from the original set of features [17]. There are five main types of feature selection, which are (1) Linear Method, (2) Non-Linear Method, (3) Filter-Based Method, (4) Wrapper Method, (5) Embedded Method. Linear methods include PCA and LDA. PCA stands for Principal Component Analysis, which is used for data reduction [18]. LDA stands for Linear Discriminant Analysis and is used for the conversion of high dimension features into lower dimension features [19]. Non-linear methods include Entropy [20], Genetic Algorithm (GA) [21], Binary Gray Wolf [22], Slap Swarm [23], Atom Search [24] and many others. Filter methods includes mRMR [25], Missing Value Ratio [26], and many others. Wrapper methods include Jackstraw [27] and Boruta [28]. Finally, embedded methods include LASSO [29], Ridge [30], Elastic [31], and many others. Image fusion [32] helps greatly in improving classifier accuracy with less computational cost [33]. Different algorithms are proposed that use image fusion to get the best accuracy results.

Image classification is the last step in image processing [34]. Classification tends to dominate the feature vector to determine which object belongs to which class [35]. There are different types of techniques used for the classification of healthy and diseased images of plant leaves. Image classification is divided into three main categories, which are (1) Supervised Learning, (2) Unsupervised learning, and (3) Object-based image analysis. Supervised Learning is used to detect the new category of the object from training data [36]. Unsupervised Learning is a process in which an image is identified in an image collection without using labeled training data [37]. Object-based analysis involves the grouping of pixels on the basis of some similarities such as shape and neighborhood [38]. To get the most accurate results, the Plant Village dataset is used for testing and training purposes. A total of 80% of guava leaf images are used for testing while 20% of them are used for training purposes. The achievable accuracy is 97.22% using Alex-Net and Squeeze-Net after segmentation and classification [39]. Atila et al. [40] designed the Efficient-Net architecture, which is designed for classification purposes. Different architectures are applied using CNN for model training to get highly accurate results. The model training is performed on the dataset of 87,848 images. Images are preprocessed using different techniques such as downscaling and squaring methods, they are then classified, and an accuracy of 99.53% is achieved using AlexNet, VGG-16, and GoogLeNet [41]. Several algorithms are used for image classification. These are SVM [42], K-Nearest Neighbor (KNN) [43], Naïve Bayes [44], Shadow algorithms [45], Minimum Mean Distance (MMD) [46], Decision Trees [47], K-Means Clustering [48] and many others. The datasets are frequently classified by SVM. This involves supervised learning and comprises points that are in the sample space and different regions [49]. Segmentation is performed on the preprocessed data in three stages. In the first stage, the deep CNN is trained to learn the mapping from the space map. In the second stage, prediction-based labels are acquired. At the last stage, these acquired labeled images are sent to SVM for classification and achieve an accuracy of 86% [50]. In machine learning, KNN is a statistical classification algorithm. It gathers the objects selected by neighbors having the highest number of votes [51]. KNN is inspected for the detection of weeds from UAV images of the chili crop of Australia. In comparison with KNN, SVM and Random Forest (RF) are used. The achievable accuracies across RF, SVM, and KNN are 96%, 94%, and 63%, respectively [52]. KNN is also used for classifying facial expressions [53]. Additionally, KNN is used for the classification of grape leaves into healthy and unhealthy leaves. Texture-based and color-based features are extracted from grape leaf images and are classified by the KNN classifier, and an accuracy of 96.66% is achieved [54]. Table 1 depicts an overview of recent works related to plants diseases analysis