![]() The accuracy and loss measures for performance assessment are utilized to assess the performance of the proposed model. The proposed model is trained using the different hyperparameters, including several epochs, optimizer, loss function, and activation function. On the provided dataset, we trained a DL-based VGG-16 model for the automated classification of apple diseases. ![]() For this, a fruit recognition dataset is used from the Kaggle website. ![]() The gathering of data and its labeling constitute the initial phase of the investigation. Apple images are classified using deep learning (DL), which has demonstrated its efficacy in IP and classification. In this work, we present a method for detecting infections in apple fruit and preventing new infections appropriately caused by environmental factors. Therefore, accurate diagnosis of apple diseases and sound decision making are crucial in minimizing agricultural losses and encouraging economic expansion. In IoT-based agriculture, machine learning (ML) and image processing (IP) methods are the major expertise needed to suggest and build effective ways to detect and avoid infection in agricultural goods. Farmers have a hard time pinpointing the source of apple disease since symptoms induced by several diseases may be quite similar and may even coexist. Every year, the apple sector loses a significant amount of money due to diseases and pests. ![]()
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