Enhancing Fruit Disease Recognition Using Deep Learning Model

Abstract

Fruit and vegetable identification and classification system is always necessary and advantageous for the agriculture business, the food processing sector, as well as the convenience shops and hypermarkets where these products are sold. Therefore, it is necessary to build an effective automated tool to meet the needs of the market by boosting the outcome, in order to improve economic efficiency. In this paper, a two-stage model is proposed to recognize fruits using camera images. Fruit disease recognition plays a crucial role in ensuring the quality and yield of fruits in agriculture. The framework for fruit disease recognition using a combination of VGG16 feature extraction, APGWO and CNN classification.VGG16 is a deep convolutional neural network known for its excellent feature extraction capabilities. APGWO adaptively adjusts the parameters to enhance the search efficiency and accuracy of feature selection. In this study, Adaptive particle – Grey Wolf Optimization (APGWO) has been applied for choosing the most pertinent features.