This paper intends to present an automated mango grading system under four stages (1) pre‐processing, (2) feature extraction, (3) optimal feature selection and (4) classification. Initially, the input image is subjected to the pre‐processing phase, where the reading, sizing, noise removal and segmentation process happens. Subsequently, the features are extracted from the pre‐processed image. To make the system more effective, from the extracted features, the optimal features are selected using a new hybrid optimization algorithm termed the lion assisted firefly algorithm (LA‐FF), which is the combination of LA and FF, respectively. Then, the optimal features are given for the classification process, where the optimized deep convolutional neural network (CNN) is deployed. As a major contribution, the configuration of CNN is fine‐tuned via selecting the optimal count of convolutional layers. This obviously enhances the classification accuracy in grading system. For fine‐tuning the convolutional layers in the deep CNN, the LA‐FF algorithm is used so that the classifier is optimized. The grading is evaluated on the basis of healthydiseased, ripeunripe and bigmediumvery big cases with respect to type I and type II measures and the performance of the proposed grading model is compared over the other state‐of‐the‐art models.

We present a highly effective algorithm for image dehazing that leverages the valuable information within the hazy image to guide the haze removal process. Our proposed algorithm begins by employing a neural network that has been trained to establish a mapping between hazy images and their corresponding clear versions. This network learns to identify the shared structural elements and patterns between hazy and clear images through the training process. To enhance the utilization of guidance information from the generated reference image, we introduce a progressive feature fusion module that combines the features extracted from the hazy image and the reference image. Our proposed algorithm is an effective solution for image dehazing, as it capitalizes on the guidance information in the hazy appearance. By combining the strengths of deep learning, progressive feature fusion, and end-to-end training, we achieve impressive results in restoring clear images from hazy counterparts. The practical applicability of our algorithm is further validated by its success on benchmark data sets and real-world SEM and TEM images.

In this work, a non-destructive and automated mango grading system model is developed for grading the mangoes into three categories depending on their internal features like Soluble Solid Content (SSC) as well as Total Acid Content (TAC). Initially, the prepared database is de-noised with the proposed adaptive Gaussian noise removal approach. Then, the Gray Level Co Occurrence Matrix (GLCM), Gray Level Run-Length Matrix (GLRM) features, and Near Infrared (NIR) spectroscopy features were extracted. As the curse of dimensionality persists, a patch-based Principal Component Analysis (PCA) model for dimensionality reduction is introduced. Subsequently, the dimensional reduced features are subjected to aproposed ensemble classifier that encompasses: ‘Support Vector Machine (SVM), Random Forest (RF), three Artificial Neural Network (ANN), and K-Nearest Neighbor (KNN)’. The weight of the third ANN is optimally-tuned by a novel improved meta-heuristic model depicting the Lion-Binary crossover mask base Whale Optimization (LBWO) Algorithm.

The structure of the proposed framework is separated into three stages: i) foundation deduction, ii) component extraction, and iii) preparing and characterization. At first, K-implies grouping methods were carried out for foundation de- duction. The second step applies color, texture, and shape-based feature extraction methods. Finally, a “merging” fusion feature is analyzed with a C4.5, support vector machine (SVM), and K-nearest neighbors (KNN). Overall, the recognition system produces an adequate performance accuracy with 97.89, 94.60, and 90.25 percent values by utilizing C4.5, SVM, and KNN, respectively. The experimentation points out that the proposed fusion scheme can significantly support accurately recognizing various fruits and vegetables.