Automated Detection and Classification of Mango Fruit Diseases Using A Novel WOA-QRNN Technique on Infected Mango Fruit Images Through Transfer Learning
Keywords:
Mango fruits, Diseases, Image processing, Deep learning, Quasi Recurrent Neural Network, Whale Optimization AlgorithmAbstract
Mango Fruits are not only loved for their delicious taste but also valued for their rich nutritional content. This makes them an essential component of diverse diets all around the World. Despite their popularity, the mango industry faces significant challenges from diseases that impact mango trees and fruit quality, leading to reduced yields and economic losses for farmers. To ensure the sustainability of mango products, it is imperative to detect and manage these diseases effectively. By utilizing deep learning algorithms trained on images of healthy and diseased Mango fruits, researchers and farmers can accurately identify diseases at an early stage. Recent advancements in deep learning have enabled the classification and identification of mango fruit diseases from images of mangos. This study introduces an Automated Mango Fruit Disease Detection Using Whale Optimization with Quasi Recurrent Neural Network (WOA-QRNN) model, that is applied to infected mango images. The WOA-QRNN method focuses on leveraging deep learning to identify mango fruit diseases. To achieve those, the WOA-QRNN technique starts with image pre-processing using Bilateral filtering (BF) for noise reductions. Subsequently, adaptive threshold-based segmentation is applied, followed by feature vector generations using the VGG-16 model. The WOA algorithm is then employed as a hyperparameter optimizer. Finally, the Quasi-Recurrent Neural Network (QRNN) model is utilized for accurate disease identification and classifications in mango fruit. Experimental validations of the WOA-QRNN techniques are conducted using benchmark mango fruit image databases. The outcomes demonstrate the promising performances of the WOA-QRNN approaches compared to existing methods across various evaluation metrics. This research highlights the effectiveness of combining deep learning, with optimization algorithms for automated mango fruit disease detections.
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