A battle royale optimization with feature fusion-based automated fruit disease grading and classification

Abstract

<abstract> <p>Fruit Disease Detection (FDD) using Computer Vision (CV) techniques is a powerful strategy to accomplish precision agriculture. Because, these techniques assist the farmers in identifying and treating the diseased fruits before it spreads to other plants, thus resulting in better crop yield and quality. Further, it also helps in reducing the usage of pesticides and other chemicals so that the farmers can streamline their efforts with high accuracy and avoid unwanted treatments. FDD and Deep Learning (DL)-based classification involve the deployment of Artificial Intelligence (AI), mainly the DL approach, to identify and classify different types of diseases that affect the fruit crops. The DL approach, especially the Convolutional Neural Network (CNN), has been trained to classify the fruit images as diseased or healthy, based on the presence or absence of the disease symptoms. In this background, the current study developed a new Battle Royale Optimization with a Feature Fusion Based Fruit Disease Grading and Classification (BROFF-FDGC) technique. In the presented BROFF-FDGC technique, the Bilateral Filtering (BF) approach is primarily employed for the noise removal process. Besides, a fusion of DL models, namely Inception v3, NASNet, and Xception models, is used for the feature extraction process with Bayesian Optimization (BO) algorithm as a hyperparameter optimizer. Moreover, the BROFF-FDGC technique employed the Stacked Sparse Autoencoder (SSAE) algorithm for fruit disease classification. Furthermore, the BRO technique is also employed for optimum hyperparameter tuning of the SSAE technique. The proposed BROFF-FDGC system was simulated extensively for validation using the test database and the outcomes established the enhanced performance of the proposed system. The obtained outcomes emphasize the superior performance of the BROFF-FDGC approach than the existing methodologies.</p> </abstract>